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The Superman Effect: The Human Side of Banking UX Design

Written by Allison Karavos on . Posted in Blog. No Comments on The Superman Effect: The Human Side of Banking UX Design

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The Superman Effect:
The Human Side of Banking UX Design

Banking UX plays a vital role in meeting customer expectations.

Customers have certain expectations regarding interactions, experiences, and treatment from their banks. Previously, banking interactions were limited to visiting a local branch, speaking to a teller or manager, and completing necessary paperwork. However, with the advent of technology, banking has rapidly evolved.

Face-to-face interactions have been replaced by automated processes, paper-based interactions have gone digital, and AI assistants have taken over from human tellers. As a result, the banking UX has become more complex. In this digital-first world, it’s crucial for banks to ensure their banking UX meets customer expectations to enhance the overall customer experience.

Exploring the Uncanny Valley: When things just aren’t quite right

In 2011, Ayse Saygin, a University of California at San Diego professor in the department of Cognitive Science explored the “uncanny valley.” Essentially, the uncanny valley hypothesizes that when man-made objects become too human, in animation or robotics as an example, humans become uncomfortable. The point where discomfort develops is known as the “uncanny valley” and it makes us want to run for the hills.

Stick with me, I’ll get to what links the “uncanny valley” to expectations, technology, and banking UX design soon… but back to the experiment.

Professor Saygin attached viewers to an MRI, testing their brain activity when shown different versions of an android. When they were shown an android with human qualities people’s brains lit up like a Christmas tree. Their brains were working overtime trying to make sense of what they were seeing.

“What we found was that if you’re going to get so close to what the brain considers a person, you better get it right,” Professor Saygin says in Huffington Post. “The brain is not very tolerant of deviations from that.”

The android didn’t meet their expectations of a robot and it definitely didn’t meet their expectations of a human. The experience wasn’t right.

The Uncanny Valley of Banking UX

More and more, as people tune into the inner workings of technology and digital experience, our tolerance for misshapen design and snake-oil gaming in user flow has plummeted. Virtual assistants that take you in circles makes people insane. Social media algorithms can be mind-numbing. Who among us hasn’t considered hurling our phone into an active volcano after a phone pop-up ad follows your thumb around?

We know when brands are trying to game us. Like Professor Saygin’s uncanny valley testing, we know when something feels off in user experience design. When it comes to real, on-the-ground needs like the digital mortgage experience, understanding the human experience–the stress and harrowing spending that the average person experiences while finding a place to live–is essential. The digital mortgage UX is the last frontier that people want littered with inadequate attempts at tapping into the human soul.

Avoiding Uncanny Valley: Developing a genuine digital experience

When UX is genuine–when it recognizes the pitfalls and joys of being a real person–it can soar. We, the people, no longer tolerate passive aggressive UX that appears out of touch with the noisy waters of the digital world. So, what makes for a genuine UX?

  • Be bold and cohesive: Craft a look and feel that doesn’t just digitize the brand’s mission. It is the mission.
  • Don’t forget the human touch: While digital assistants and chatbots can be incredibly useful, banking services can be extremely complex. Make it easy for your digital users to get in touch with a human if they need to.
  • Create emotional experiences: In the age of experience, users search for emotion to make a connection to a product.
  • Anticipate: Integrated analytics that help you anticipate your customer’s needs and make the right offers.
  • Serve don’t sell: In a world of fake news and too good to be true offers it’s time to be the guide not the salesman.
  • Keep it simple: Navigating your user experience flow shouldn’t be a challenge. Test and test again to make the route to success as simple as possible.

Honest Experiences that Meet Expectations

I’ll use mortgages as example again as let’s face it, buying a house is one of life’s great mountain climbs. It’s our homes we’re talking about, the place where we’ll live and name our dog after a Game of Thrones character. There are already hills of paperwork and expenses that make it a little harder to breath, which makes it vitally important that lenders provide an experience that anticipates and counteracts moments of stress.

Actually, smart UX should guide us through its service like Marlon Brando’s character Jor-El in the 1978 film Superman: A benevolent, all-wise parent. Let’s say we call this the Superman effect in UX: When parental free-floating apps and digital experiences lead us, pragmatically, to the thing we find most valuable.

If that sounds like climbing Everest, it’s not; we’re already there, and the technology is ready. Fintechs are already working to make a digital mortgage experience that doesn’t send customers running for the hills. A TechCrunch op-ed stated:

“Closing a home loan today takes more time and has become more difficult and costly than ever imagined…The good news is that both of these problems are being aggressively tackled by tech companies working to transform the mortgage experience and bring lending into the digital world.”

UX that’s inspired by a true understanding of what people are going through is the first rung of a step ladder that leads to customer loyalty. When brands employ technology that is harmonious with customers’ human experience, when it leads us and we, in turn, lead it, there will be no running for the hills. Instead we’ll wander hand in hand through the meadows!

But, the moment we feel that design is over-reaching or brands are using the space disingenuously, whether it’s the oddly humanistic qualities of robotics or an app that gets us into owning a house quicker, the whole experience becomes unharmonious. When technology doesn’t guide us, seamlessly and invisibly, it becomes UX’s uncanny valley.

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Driving a Better Consumer Experience in Auto Financing

Written by Allison Karavos on . Posted in Blog.

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Driving a Better Consumer Experience
in Auto Financing

Satisfied vehicle shoppers make for repeat customers

Did you know that 65% of car shoppers feel that finance applications take too long? Whether you’re looking for a car, an RV, a motorcycle or even a boat – some of the biggest headaches in our buying lives come from the mountains of paperwork that financing or leasing a vehicle requires. The traditional loan origination process is arduous, doesn’t benefit either the customer or the lender, and increases the risk of losing a customer before they can sign on the dotted line.

Let’s face it, customers are not keen to sit in dealerships for hours and fill out reams of paperwork to hopefully get approved for a loan. In the age of instant everything, customer experience matters. Entertainment is available on demand, your favorite milkshake can be delivered without talking to anyone, you can order a ride in minutes – consumers expect more and aren’t shy about telling the world when their expectations aren’t met. Brands that make missteps should expect to have their failures broadcast far and wide in viral twitter threads, WhatsApp groups and Facebook posts.

Consumers have power

If traditional vehicle dealers want to maintain and grow their customer base, they need to ensure consumer satisfaction. There are countless examples of small, innovative companies that grew to behemoths – they all have a few things in common:

  1. they take something (a process, a product, a service) that frustrates consumers and change it entirely to better suit the consumer’s needs;
  2. they continuously adapt to changing, emerging technology and;
  3. they treat their customers incredibly well.

Look at Uber and how they changed the face of private transportation. Or Netflix and how they’ve completely disrupted cable television. Or Airbnb and VRBO and the changes they’ve inspired in the hospitality industry. Of course, there’s also Amazon and the way it changed… everything, or Facebook and the advent of instant, social, worldwide communication. And no list of disruptive tech would be complete without Apple, the mother of all companies that entirely transformed the way people use personal technology. One of the ways that Apple has disrupted an entire industry is through functionality – or more specifically, the ease of functionality. “Using an Apple product feels so natural, so intuitive, so transparent… The design is so intuitive that the instruction manual is almost non-existent.” What if auto lenders positioned themselves the same way? And what if what they promised was actually true? These days, you can get a car delivered to your doorstep with innovative companies like Carvana or Carvago without having to set foot in a dealership. It’s never been more important for auto lenders to ensure they are easy to work with. 

More than ever before, our connected world and social media makes it possible for companies that do things really well to stand out. On the flipside, it ensures that the word is spread about companies that don’t do things well. Consumers have inside access to brands in a way they’ve never had before – they can sit on the phone waiting for a faceless customer service rep to maybe answer the phone, or they can instantly tweet their complaints and get a company rep to address their concerns in real time (while the rest of the twitter-verse watches). Even with the supposed ease of online loan applications, 90% of bank customers will abandon an onboarding application if the process takes more than an hour to complete, according to The Paypers. Bottom line? Consumers won’t sit and wait around for a subpar experience if they don’t have to.

Old versus new

So how does this translate to something like auto loan origination? The old-guard method of auto financing requires customers to fill out mountains of paperwork, provide copious amounts of data and multiple forms of identity. Behind the scenes underwriters then spend hours manually processing applications to determine a customer’s credit risk. The end result? Customers often feel like their time isn’t valued and that they are little more than a number on an assembly line. Even if you have technology in place to support increased automation and faster underwriting, as soon as your sales rep needs to make a phone call for a loan approval, you’re already too slow for today’s savvy, instant-everything consumers. But the good news is, when there are problems or lags in an industry or process, innovation flourishes. 

Captive/manufacturer finance currently owns over half of the market, so there is a lot to lose. Conversely, new competitors like smaller lenders have a long runway of opportunity. They are threatening the traditional dealership finance and sales process, and these threats are growing rapidly:

Enter in a new way of originating auto loans that can help transform the dealership experience:

  • Smart, digital applications that automatically pull information in through a decisioning platform
  • Automated KYC data, including identity verification and due diligence
  • Powerful decisioning tools that automate data gathering, risk modeling and personalized pricing
  • Loan decisions in UNDER A SECOND

A truly memorable, satisfactory consumer experience in auto financing is fast, easily available, and most importantly, personalized. Your customers aren’t just numbers and your finance products need to reassure them of that fact.

The future of auto financing is here – the question is how many auto lenders will put their customers first and take advantage of it? The kicker is, not only will those who do take advantage of it have happier, more loyal customers, but they will also be poised to innovate better, and faster. By creating new industry benchmarks – with better deals, instant approvals and personalized processes – you can stand out in the auto financing industry. And maybe even be the subject of the next positive viral twitter thread?

Download the eBook to discover how auto financing is changing. Learn how you can improve the customer experience and innovate faster with real-time data and AI-powered, automated decisioning tools.

Discover how Flexiplan, a digital motorcycle financing platform, uses Provenir to manage risk more effectively.

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Credit Risk Software: Build vs. Buy Options (Complete Guide)

Written by Allison Karavos on . Posted in Blog. No Comments on Credit Risk Software: Build vs. Buy Options (Complete Guide)

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Credit Risk Software:
Build vs. Buy Options
(Complete Guide)

12 factors to consider when evaluating build vs buy options for credit risk software.

I loved Lego when I was a kid, ok, ok, I’m going to be totally honest, I still love Lego (PSA: other brands of building blocks are available). The pirate theme was a favorite, but Santa must have lost my pirate ship box set somewhere over the Atlantic. So, my pirate Lego supply was limited to a mini boat, Lego characters wearing pirate costumes, and treasure chests filled with pieces of eight. So, here I have my menacing pirates setting off on elaborate plundering adventures in… a tiny ‘wooden dinghy’. Let’s face it, no self-respecting pirate would be taking that dinghy anywhere, even to pop down to the grocery store to stock up on grog.

But what does Lego have to do with deciding whether to build or buy credit risk software?

Building a credit decisioning solution for your business is like creating a Lego model. Your solution – whether it’s a loan origination system, merchant onboarding tool, or payment platform – is not a self-contained Lego brick that can act as a user interface, store data, process applications, manage integrations, maintain KYC compliance, host risk models, use machine learning algorithms, and provide a credit decision. Similar to Lego, it is a set of building blocks joined together to create the right decisioning solution for your business.

Build Vs. Buy—More Options Than Ever

The build vs. buy debate has been going on for years, and much of the discussion falls around simple options: you buy, or you build. But with technology getting more advanced every day there’s now other options such as: buying the building blocks or selecting a strategic partner. So, for the purpose of this guide we’re going to compare four options:

– Build

This is the from scratch, internal approach. If this were a Lego project it would include creating the plans for your blocks, developing the blocks internally, and building them into your finished solution. This is often the first option explored by tech savvy companies, especially if they have a wealth of tech talent available to take on the project.

– Build, but not from Scratch

This is the Lego kit solution for credit risk software. You buy the kit—so you don’t need to handle building the blocks/ components—and combine them into the solution that best fits your needs. The flexibility in finished design will vary by vendor solution. For example, some solutions may give you the option to build anything from a paddle board to a cruise liner. Others may only let you build a sailboat.

– Buy

Another common choice is the buy approach, in this situation you’re buying your pirate boat fully built, you might be able to change a few of the decorations, but the design stays pretty standard. Ongoing maintenance and upgrade options will vary by vendor. If you spring a leak you may need to depend on the vendor to fix the hole.

– Partner

Someone else owns the Lego and has already built the ship, you use it. This may sound like the perfect solution, but you could be very limited on the design. In other words, you’ll need to adjust your needs to fit their ship design.

12 Factors to Consider When Evaluating Your Build Vs. Buy Options

Are you facing challenges in managing credit risks within your business? Maybe you’re struggling to keep up with your competitors, experiencing limitations in business growth, or dealing with a poor user experience. One way to address these challenges is by using credit risk software. However, before selecting a solution, it’s important to consider several factors:

  1. Your Pain Points What’s your pain point? – Is there an issue causing you to lag behind your competitors, impacting your user experience, or limiting business growth? What do you need to do to fix it? Is it increasing your decisioning speed? Reducing the time it takes your team to deploy new risk models? Make integration to internal or external data sources easier? Improve the accuracy of your decisioning? Automate the decisioning process? Defining the project scope and listing solution requirements is an essential step in fully evaluating your options. Without knowing your need list and your wish list you could end up with a risk decisioning river boat when what you really needed was a jet ski
  2. Fit – Perhaps the most important question: would the implemented solution meet all of your decisioning needs?  Or would you need to bring in other solutions to make up for any shortcomings? It’s also important to look at how the solution will fit in with your existing technology stack and how easy integrating the systems would be. For example, will the tech stack together like Lego blocks, or will it will it be more like trying to attach a Lego block to a house brick.
  3. Flexibility – The thing that makes Lego so incredible is the huge amount of designs you can make with just a small set of blocks. My Lego house could absolutely transform into a pirate ship when needed! So, which of the solutions will give you the flexibility you need to create the right system for your business needs?
  4. Time – Instant launch or long development process? How will each option impact your time to market? Long delays can be expensive, extend product launch times, limit business agility, and expose the business to increased risk, especially where credit origination and KYC processes are involved.
  5. Costs – The cost of each option is an obvious consideration, but it’s important to look at both initial costs and ongoing costs. Things to consider include the cost of ongoing maintenance, changes, and upgrades, whether they’re completed internally or externally. If your solution will be inadequate in a few years, what will be the cost to replace it or make it fit new business needs?
  6. Resources – What resources will you need to complete the project, and do you currently have that talent in your team? If not, what training or recruitment will need to be completed and what will be the cost to bring the required resources in house?
  7. Focus – New development projects can be all consuming—using resources, effort, and focus that could be utilized elsewhere to drive the business towards its goals. If you decide to focus your resources on an internal build, what opportunities will you miss elsewhere and is the delay to these other projects a problem?
  8. Usability – Usability can make a huge difference to your business in both the short and long-term, so it’s important to ask how usable the finished solution will be? Will you need specially trained team members? If it’s an externally built solution how much will it cost to train your team to use the system? In Lego terms, are you getting a simple kit with a few pages of instructions, or a 2000-block pack with a 500-page manual?
  9. Control – While the ability to change settings and adjust processes may seem like a nice to have option, the delays caused by waiting for vendors or your tech team to implement change requests from your risk team can have a long-term impact. Each time you have to wait for a new data source to be integrated, a score card to be changed, or a risk model to be deployed you’re falling behind your competitors. When evaluating solutions make sure to ask how much control will you have over the software. Will you be able to easily make changes and adjust settings, or will you be reliant on a third party such as the vendor?
  10. Competitive Advantage – In some situations, one solution will give you an advantage over the competition. For example, if you can build a Lego ship that has a unique design that makes it faster, smarter, and more efficient than other ships, then creating your own Intellectual Property makes sense. However, if an industry leading solution is available to buy, what competitive advantages would you gain by building internally?
  11. Business Agility – Will the selected option impact your business agility? For example, could you quickly pivot direction and make quick decisions? Or would you need long lead times to adjust your decisioning processes, make updates, or completely switch direction?
  12. Scalability – While it may be easier to shop for or build a solution that fits your needs now, looking ahead can help you avoid needing to replace your solution in a few years. So, when evaluating options ask: will your solution be able to easily grow and develop with your business, or will the decisioning solution be obsolete in a few years?

The decision to build or buy credit risk software is a critical one for financial institutions. While building an in-house solution may provide greater control and customization, it comes with a higher cost and longer development time. Buying a pre-built solution can offer faster implementation, cost savings, and access to advanced features and technology. Ultimately, the decision should be based on a thorough evaluation of the organization’s specific needs and capabilities. Working with a trusted partner can help organizations navigate the complex process of selecting and implementing the right credit risk software solution for their business.

The Ultimate guide to Decision Engines

What is a decision engine and how does it help your business processes?

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The Ultimate Guide to Decision Engines

Written by Allison Karavos on . Posted in Blog. No Comments on The Ultimate Guide to Decision Engines

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The Ultimate Guide
to Decision Engines

What is a decision engine and how does it help your business processes?

Decision engines, sometimes referred to as decision trees, are software platforms that automate business rules or business decisions – helping you streamline business processes that require decision-making without having to think about it. A decision engine automates these business decisions based on your business needs and the particular criteria the platform’s owner sets out, saving you from manual work and centralizing the decision-making process. 

What does a decision engine need to run? Besides the set of rules (logic), otherwise known as the decisioning workflow, decision engines need data. Lots and lots of data. By accessing and integrating data from multiple sources and applying these ‘rules’ according to your criteria, voila – you can automate decision-making. In the finance world in particular, decision engines are often used to help you make decisions on who to lend to and helps determine which sort of products you can offer your customers.

Automated decision engines can also enable personalized pricing and offers (i.e. finance terms and interest rates), all of which are customizable to your unique needs. Some popular examples in the world of fintech/financial services include: consumer lending, loan origination, credit card approvals, auto financing, point of sale lending like buy now, pay later (BNPL), lending to SMEs, insurance policy approvals, upsell/cross-sell offers, champion/challenger strategies, audits, collections and more.  

How does a decision engine help inform business decisions?

Decision engines can help inform various types of business decisions – on everything from basic day-to-day operations to more high-level, strategic business decisions. 

  • Strategic Decisions: Strategic decisions are top-level, and tend to be more complex, affecting a much larger portion of the organization and often applicable for a longer term (i.e. changing cost structures or planning for longer-term organizational growth). Decision engines and automated decisioning processes can expedite and streamline various processes, improve efficiency, and allow you to make smarter decisions overall. In the case of financial services, this could mean a shift in deciding who you can lend to in order to expand your overall customer base and plan for growth. Keep in mind that more complex decision execution typically requires a large amount of data, provided from a variety of data sources. Utilizing decision engines and automated decisioning processes can help an organization access, analyze, and action a large variety of data, enabling smarter decision-making.
  • Tactical Decisions: Tactical decisions are much more focused on business processes and tend to be shorter-term and less complex. Examples include launching new products, changing product pricing, managing inventory control, and supply chain and logistics. With decision engines, you can more easily analyze performance data and help determine new pricing strategies for your financial services products or look strategically at which demographic or region to target next. 
  • Operational Decisions: Focused on day-to-day operations of a business, operational decisions are much smaller in scale. They tend to be related to overall daily production and are usually executed in alignment with the overall strategic vision of an organization. In financial services, decision engines can improve efficiency and help automate or streamline varying day-to-day decisions, including loan approvals, interest rate offers, guidance on collections, merchant onboarding, pricing optimization, compliance processes, identity verification, fraud prevention and more.

Decision Engine Framework

So how does a decision engine actually work? And how do decision engines function in a business? While it’s up to each individual organization (and all of the individual business rules within) how they want their business decisions to be executed, there are some basic steps that remain true across the board.

  1. Set Desired Outcomes: Look at what your goals are. What are the specific business rules that you need your decision engine or workflows to execute on?
  2. Determine Decision Criteria: What are the standards or requirements to which you are making your evaluations or decisions? For example, in the case of many credit applications, particular criteria often include income, job status, age, marital status, debt ratio, etc.
  3. Organize Data Sources: To process these business decisions based on your desired outcomes and your determined criteria, what sort of data sources do you need? Do you need traditional credit bureau data, third-party sources, alternative data like rental info, social media presence and web data, etc.?
  4. Create Decisioning Workflows: What are the necessary steps in your decisioning process? Use the configuration tools within your decision engine to lay out your workflows and business rules and enable automated decisions.
  5. Test and Iterate: Create, test and deploy your modelling scorecards and decisioning process, and look at what happens when a typical customer is put into your system. For example, if a customer applies for a credit card, their information is put into the decision engine, which then pulls in necessary data (identity verification, KYC, income verification, fraud), and rejects or approves based on the initial criteria determined. Is something missing? Can your business process be smoother? Iterate!
  6. Determine Next Steps: Where is your threshold for complex applications? Which applications need manual intervention? Straight-through processing enables instant decisions for more simple credit and lending requests, while a rules-driven decisioning process helps to identify and re-route exceptions that require more manual intervention. 
  7. Monitor and Optimize: Is your decision engine offering real business value? Keep tabs on your decisioning performance by using the information your decision engine gives you. Identify opportunities for further enhancement of your decisioning process and tools and enable more efficient decisioning – and business growth.

How does a decision engine function in a business?

As we’ve shown, there are a large variety of ways that decision engines can help inform business processes. But how exactly does it do that? In the case of financial services, think of all the manual decisions that require human intervention. If an individual needs a car loan, for example, how does a lender determine if that individual is creditworthy or not? And if they are, what interest rate or repayment terms should they be offered? Having an automated decision engine can streamline the application, approval, and funding process to ensure an efficient, superior customer experience. 

In the auto financing example, applications can move from manual, paper-heavy forms, and hours of sitting in a dealership to simplified, online applications. An individual can easily fill out an application and provide ID, which then allows a decision engine to move that person quickly and easily through the decisioning workflow along a series of pre-determined steps, according to the initial criteria.

In this case, that criteria could start with analyzing data for identity verification (is this person really who they say they are? How old are they? Do they have a valid driver’s license?), then move through to various factors that determine creditworthiness. Does this person have an income that is above our threshold? What is their credit score? How much debt does this person already have, and what is their debt-to-income ratio? Do they have previous loan defaults on their record?

As the decision engine automatically accesses and analyzes all the data required according to the business rules, it moves that application through the workflow based on the answers. Driver’s license? Check, on to the next step! Old enough to own a car? You betcha. Have a job? Yep, move along! But then comes a doozy of a credit score and a record of numerous loans having gone to collections. The buck stops here and the decision engine (as per the initial ‘instructions’ when setting out the original workflow) stops the application and determines that this individual is NOT a risk this lender wants to take.

Of course, not all situations are as black and white as that example, but the beauty of automating business processes with a decision engine is that you can streamline and improve efficiency for many situations and types of applicants, while focusing that most precious resource, humans, on the more complex cases that require manual intervention.

Data, Data, and More Data

Despite all the wonderful ways that business processes can be improved using decision strategies, there can be no automating decision execution without extensive data and data aggregation. Data, preferably varied and from a wide range of data sources (including historical data), is critical to the decision-making process.

All financial services organizations use data to make informed decisions across the customer lifecycle – but having to manually access and integrate data sources is nothing short of a nightmare. Data consumption has evolved, right alongside the decision engines that data feeds into. It’s impossible to make accurate decisions based on business needs without the right data that aligns with the particular criteria set out. Think back to the examples previously discussed – where do you get information on loan payments, credit policies, credit scores, income to debt ratio, age verification, etc.? It’s all about your customer data sources.

These days, more and more lenders are increasingly looking to a wider range of data sources, including alternative data like rental payments, social media interactions, website info, travel data and more, to ensure: 

  • A more accurate view of identity verification
  • A more holistic view of risk and creditworthiness
  • Better fraud prevention

All this data must be accessed, analyzed, and actioned appropriately to help ensure more accurate, automated decisions that provide value to a business. As The Financial Brand said, “Data, by itself, is not a valuable asset. It’s what you do with it that counts.” Having a variety of data available on-demand is essential for enhancing your automated decisioning. Third-party data providers, connected through a centralized platform or marketplace with a single API, can make this data consumption effortless, giving you the ability to access and integrate numerous data sources in minutes. Use that data to test your decisioning workflows, and then iterate and adapt with ease.

AI-Powered Decisioning

The use of artificial intelligence and machine learning is growing. AI in financial services is seen as a $450 billion opportunity. But how can you use AI most effectively in your decision engines? Using AI/ML to power your decisioning process enables:

  • Improved decisioning accuracy
  • Superior fraud detection
  • Enriched customer relationships
  • Improved customer satisfaction
  • Expanded customer base
  • Optimized pricing
  • Revenue growth

McKinsey pointed out that “The continuing advances in big data, digital, and analytics are creating fresh opportunities for banks to improve the credit-decisioning models that underpin their lending processes… the banks (and fintech companies) that have put new models in place have already increased revenue, reduced credit-loss rates, and made significant efficiency gains thanks to more precise and automated decisioning.”

It may seem daunting to try to implement AI into your decisioning processes, but you don’t necessarily need data scientists on your team to make AI impactful. With a technology platform that incorporates both data sources and advanced machine learning into your decision engine, you can make use of advanced decisioning – and get all those benefits listed above.

AI allows you to do things that may be challenging for traditional decision engines, including enabling more approvals for unbanked consumers, adapting to rapidly changing market trends and consumer demands without sacrificing the customer experience, and finding relationships in your data (see? Data is king!) that may be otherwise unseeable. If you do happen to be lucky enough to have data scientists in-house and need to figure out a way to utilize all their expertise in your decision engine or business applications, look for a technology partner that can easily migrate existing models into a user-friendly platform.

What’s the benefit?

While we’re talking about data integrations, automated workflows, data scientists, machine learning… why go to all this trouble? There is immense value in using decision engines in financial services instead of manually trying to make complex decisions around your business processes. Some of the benefits include:

  • Boosted Performance: make decisions faster and more effectively, enabling optimized business performance
  • Increased Profits: lend to more customers, without increasing your risk, allowing for better profit margins
  • Improved Efficiency: save time and resources, with fewer human interventions needed and the ability to make decisions faster
  • Flexibility: change your decision criteria without having to re-do your entire workflow
  • Scalability: easily add more data integrations and new criteria or decision parameters to your workflows as your business grows or the needs of your consumers/the market changes
  • Focused Resources: save your underwriters’ attention and manual intervention for more complex cases
  • Consistency: ensure consistency and stability in your decision-making processes, enabling enhanced customer relationships and reliability in business performance
  • Transparency: get full visibility into what your decision engine is doing and measure performance so you can easily optimize
  • Capture information: manual underwriting requires manual information capture – with an automated decision engine you can easily maintain information on your customers, your decisions, and your overall performance, which you can then feed back into your decision engine for further optimization

Also read: The Essential Guide to Credit Underwriting

Customer experience is more critical than ever. In an age of having everything available on demand (tv shows, rides, food delivery, workouts), your consumers expect speed. On top of that, they value customization. We want Netflix to know exactly what kind of show we’re up for next or appreciate when our Facebook feed is filled with ads that resonate. According to PwC, 80% of consumers rank speed as a key buying factor, and Salesforce says that 76% of consumers expect customized offers. Who has time for that if you’re busy making all your business decisions manually?

The Future of Decision Engines

What does the future hold for decision engines? From our perspective, the prospects are bright. Did you know that Forrester recently added Digital Decisioning Platforms to their Wave report? According to Forrester, Digital Decisioning Platforms (DDP) are “an evolution of expert systems, knowledge-based systems, business rules management systems, and decision management systems.” It’s a mouthful, but it’s clear the trajectory is positive when you automate your business decisions. And with the increased acceptance of artificial intelligence and machine learning, the ways in which we can automate decisions will only get more exciting (and profitable).

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Ten Fintechs Using Alternative Data for Financial Inclusion

Written by Allison Karavos on . Posted in Blog.

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Ten Fintechs Using Alternative Data
for Financial Inclusion

Ensuring the Underbanked and Underserved Have Access to Credit

At one point, it was impossible for people to buy things without having cash in hand, right then and there. And then dawned the age of credit. While credit has taken many forms (layaway plans and credit cards, instalment plans and payday loans, mortgages and Buy Now, Pay Later products), one thing has remained constant: to get credit, you need to qualify for it.

As fintechs and credit providers evolve, so has the way lenders handle their credit risk decisioning. A traditional credit score (based on things like credit history, payment history and debt ratio) is no longer the only way to evaluate creditworthiness – and, it naturally precludes a large number of people who may not have much of a credit history to evaluate (i.e. minorities, recent immigrants, younger consumers, the financially underserved and others who are new to credit).

This is where alternative data comes in. A broad term that essentially refers to all credit data not currently reported via traditional credit scores, this type of data strengthens a person’s ‘profile’ and provides a more robust, comprehensive view of the risk associated with lending to them. The types of alternative data keep growing, but the term includes things like rental payments, utility records, social media presence, telco data and open banking info.

Also, read: What is Banking as a Service (BaaS)?

Financial Inclusion and Supporting SMEs

Using alternative data and deeming more people creditworthy is clearly good for business—it means organizations can more accurately predict risk and say yes to more people and enables lenders to grow and scale their business in a way that traditional data might not allow. But there’s more to it than that. Not only is alternative data good for business, it’s good for their consumers also. Companies all over the world are finding unique and inspiring ways to use alternative data to promote greater financial inclusion for thin-file/no-file clients (also known as the underbanked/unbanked), and to support greater access to credit for SMEs/MSMEs.

While this list is in no way comprehensive (there’s just too many amazing organizations doing awesome things) – here are ten unique companies using alternative data for the greater good.

  1. Bankly – In Nigeria, Bankly helps their users digitize and grow their cash in a safe, sustainable manner. Using technology and human touchpoints to digitize cash, they are able to generate data to create a digital/financial identity, which ensures their thin-file customers gain access to broader financial services including credit and insurance. Seventy-five percent of their users identify as underbanked, including such underserved populations as farmers, market traders, artisans and transport workers who are often paid in cash and can’t easily access traditional banking services.
  2. Davinta – Indian-based Davinta is an AI-based digital platform focused on offering credit and other financial products to people living in rural areas. The company leverages data from both traditional and alternative channels to recommend tailored financial products to their customers. To date, Davinta has acquired nearly 15,000 registered users, the vast majority (12,000) of which are women. As they say, they are not just another financial inclusion enterprise, but endeavor to “create wholesome social inclusion of the larger Indian society towards equal life opportunities.”
  3. Esusu – This American company uses rental payment data to help underserved populations build credit history. Serving low to moderate income households in the U.S., their proprietary platform reports rent payments to the three major credit bureaus in the region, allowing customers to build credit and unlocking future opportunities that may have otherwise been out of reach.
  4. Fairbanc – Headquartered in the United States but operating in Indonesia, Fairbanc offers a highly-scalable closed-loop credit platform for micro-merchants, enabling them to access the supply chain and more easily purchase fast-moving consumer goods. With a focus on financial inclusion for women, Fairbanc has access to a customer base of 650,000 unbanked micro-merchants in Indonesia, with nearly 260,000 of them being women. Their AI/ML platform analyzes transaction data and history to grant instant digital credit lines; and with their ‘Pay Later’ API integrated directly into Unilever’s order-taking tables, merchants need only a basic phone to participate.
  5. Fundfina – Operating in India, Fundfina is a financial marketplace powered by open banking architecture and machine learning analytics. Focused on MSMEs, the organization partners with local financial institutions to serve more than 150,000 customers across India, who would otherwise find it difficult to access traditional credit thanks to a lack of credit history. Combating the slow, complex lending process that is typical in India, Fundfina enables thin-file credit assessments through its proprietary digital engine (they’ve developed their own credit scoring method, TrueScore, looking at transactional data and payment history), curating the most appropriate financial products and even offering cashflow management tools to promote financial literacy.
  6. First Circle – One of the first fintech companies to be licensed by the Securities and Exchange Commission (SEC) in the Philippines, First Circle was founded to empower SMEs by helping to bridge the credit gap found for small businesses in the region. With various growth programs available, revolving credit lines, and mobile-first applications processes, First Circle aims to help customers who often have no credit data or fixed collateral available, many who have been forced to work with predatory lenders in the past.
  7. Oriente – Based in Hong Kong, Oriente has built a digital-first infrastructure designed to ignite economic opportunity for unbanked consumers and underserved merchants. Using real-time alternative data and insights, Oriente enables thousands of merchants to increase conversion rates while lowering risks. Their proprietary identity infrastructure uses AI and machine learning to make it hassle-free for unbanked consumers to get digital credit, and even enables them to build their credit profile if they pay on time.
  8. Paycode – Designed for those in remote, rural areas, South Africa’s Paycode provides financial services technology solutions to unbanked citizens, using biometric data collection for identity verification and to securely authenticate banking transactions. By partnering with local financial institutions, their complete alternative banking and payment platform has been able to create low-cost bank accounts for first-time users, with over 4 million end-users across 8 countries so far.
  9. TiendaPago – An innovative fintech operating in Mexico and Peru, TiendaPago targets ‘Mom and Pop’ businesses for financial inclusion, providing closed-loop working capital financing. Their mobile-based platform uses data related to inventory purchases to assess creditworthiness of merchants, ensuring that merchants can pay distributors for the correct amount of inventory they need to adequately provide for their customers and grow their business. Merchants typically have limited cash funds available to pay distributors, resulting in higher price points for inventory and limiting sales.
  10. ZigWay – Based in Myanmar, Zigway aims to help low-income families gain more access to household essentials in an affordable way. Offering a monthly subscription service that enables households to purchase quality staples like rice and cooking oil in bulk, they provide savings of up to 20 percent for participants. Using a proprietary, machine learning-based credit scoring model, ZigWay is able to offer participants flexible payment plants. They even promote accessibility and inclusion by empowering ‘Super Users’ to help register their neighbors, request services and make payments on their behalf. To date, they’ve piloted their services with over 500 customers, delivering enough food for over a million meals.

The story of alternative data – what it means, how it’s utilized, who uses it – will keep changing and evolving as more and more fintechs and data providers find unique ways to incorporate it in their risk decisioning processes. That is, if they can efficiently access it. When we surveyed 400 fintech decision-makers globally, the stats on using alternative data were pretty staggering:

  • 60% said access to alternative data sources is limited and 74% said data of any kind is not easily accessible, while 60% found it a challenge that they don’t have a centralized view of data across the customer lifecycle
  • 70% said data not being easily integrated into their decisioning solution was an impediment to using alternative data, and 51% said it simply wasn’t accessible in their organization

But the value of using alternative data for credit decisioning is clear – not only does it enable a more complete view of your customers, it also allows for greater financial inclusion, better access to credit for SMEs/MSMEs, and it can help you grow your business in ways you may never have imagined. If you find it challenging and costly to select, access, and use the right data at the right time to make accurate, inclusive decisions, check out how Provenir Data can help. Take control of your data, all from one centralized, easy-to-access global data platform, and never worry about how to integrate alternative data sources again.

Discover how Provenir Data can help you incorporate alternative data into your credit risk decisioning and encourage greater financial inclusion.

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Finally, The Secret to Credit Risk Modeling with Python

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Finally, The Secret to Credit Risk Modeling with Python

I’m going to defer to a quote from the Banker Scene in Monty Python’s Flying Circus to start this blog:

“I’m glad to say we’ve got the go-ahead to lend you the money you required…

We will, of course, want as security the deeds of your house, of your aunt’s house, of your second cousin’s house, of your wife’s parents’ house, and of your granny’s bungalow. And we will, in addition, need a controlling interest in your new company, unrestricted access to your private bank account, the deposit in our vaults of your three children as hostages…”

It may seem a little odd to quote Monty Python at the start of a blog about credit risk modeling using Python but, I have two very good reasons:

  1. While the level of security the borrower in this scene needed to provide is absurd, it highlights the need for lenders to fully understand the risk of a loan in order to not require their customers to put their children up as collateral…
  2. You’ll find out in a second

When Monty Python was filmed back in 1969 credit risk modeling was in its infancy, in fact it was barely crawling let alone walking into adolescence. Understanding default risk was a complicated, long, and labor-intensive process. We can thank risk modeling pioneers and languages like Python for helping banks and other lenders make more informed decisions about loan applications.

A Brief Recap on the History of Python

It was the week of Christmas in 1989 in Amsterdam, 48* F and cloudy, when Guido van Rossum started tinkering on a small project to busy himself while his employer’s office closed for the holiday. van Rossum set out to create a language, based on a previous project called ABC, that was reliant on the Unix infrastructure and conventions, without being Unix-bound. He placed a high emphasis on readability and uniformity in an era that praised languages like C and Perl (PHP’s high-maintenance personality would crash the party later). The result was a gorgeously elegant open source language named after (here’s reason 2) Monty Python’s Flying Circus.

Since its official release in 1993, Python has displayed its prowess across multiple industries and in widely varying use cases. It is now the most widely taught introductory language in the top computer science programs worldwide, was named the most in-demand programming language in the U.S. by Forbes’ fintech columnist, and hit #2 on the list of most GitHub pulls by language in 2017.

Python Risk Modeling in Finance

One increasingly popular application of Python is in credit risk modeling. Many brilliant data scientists and analysts wrangle the usability of Python to implement machine learning and deep learning algorithms. From simple algorithms like logistic regression, decision trees, random forests, support vector machines via classification and regression, to more advanced methods like clustering and neural networks, the many advantages of Python are opening the doors to apply artificial intelligence to credit risk challenges.

Yet, despite the promise that Python and other popular languages bring to financial services, we talk to companies nearly every day who vent frustration with the process of testing and deploying models to production, then maintaining or changing models as business objectives evolve. It has been stated that “The next breakthrough in data analysis may not be in individual algorithms, but in the ability to rapidly combine, deploy, and maintain existing algorithms.” I would venture to guess that most of us in financial services (or any data-driven industry, for that matter) would say, “Of course.” Delays in model deployment are nothing new, and we’re ready for a change.

Challenges Deploying Credit Risk Models using Python

Ben Lorica, the Chief Data Scientist at O’Reilly once called out the delays that exist from modeling to production, identifying two root causes for the long analytical lifecycle:

  1. Silos -Data Scientists and Production Engineering teams are historically divided, resulting in a wall between the two organizations that results in inherent delays.
  2. Recoding – Models often have to be recorded before they can be deployed into production. So, while your data scientist may prefer Python risk modeling, your production system probably requires Java.

You’re probably squirming in your seat right now because you get the same familiar anxiety that I do when thinking about this dynamic. But, stay with me.

It Doesn’t Have to Be This Way

If everybody is so frustrated with this problem, why doesn’t anyone fix it?

I’m so glad you asked.

Provenir tackled this challenge head on in two very distinct ways:

  1. Empowerment – Silos or no silos, your production system should be so easy to use that your data scientist can deploy a model autonomously. Deploying a model into a Provenir decisioning environment is as simple as attaching a document to an email. Just upload the document, visually map your values, and go.
  2. Native Operationalization – Recoding?! That sounds like re-work to me. Why not operationalize models in their native languages? So, Provenir supports the native operationalization of risk models in R, SaS, and Excel along with PMML and MathML. We also include support for Python, not just because we value any opportunity to share a Monty Python joke, but because we know how beneficial it is to let your team create models in the language they’re most comfortable with!

See for yourself how easy it is to deploy a Python model:

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7 Reasons to Use Salesforce for Credit/Loan Origination and Risk Decisioning

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7 Reasons to Use Salesforce
for Credit/Loan Origination and Risk Decisioning

I had breakfast in an original Paramount Diner over the weekend. This authentic throwback to the 60s included fully restored jukeboxes on each table, letting diners choose their own private dining soundtrack.

So, what do my eating habits have to do with using Salesforce for loan origination and risk decisioning?

Well, this jukebox had a long list of song options that the diner needed to scroll through until they found something they liked. But the list was long, why? Because the diner wanted to include a range of tracks so that there was something for everyone.

It struck me as I watched the 10-year old at the table scroll through a range of classic tracks that he declared were ‘rubbish’ until he reached the pages of recent pop tracks that I declared were rubbish, that this experience was very much like shopping for financial services.

You’re often forced to scroll through the irrelevant offers until you find the one you’re ready to press play on. But, it doesn’t end there. You then you have to fill in the application and then if you’re lucky only wait a few seconds for your application to be approved.

But, financial services providers have the power to improve that experience when they combine the data within their CRM systems with their loan decisioning technology.

Using Salesforce to improve efficiency, make smarter decisions, and personalize user experience

Financial institutions want to serve customers well, so they strive for efficiency improvements. Process automation plays a big part in this. Manual, disconnected credit and lending processes are being weeded out and replaced with digital, automated solutions.

This is progress. But for complete efficiency, risk analytics and decision-making should be tied into other business systems. Salesforce is an excellent case in point. Its customer relationship management (CRM) solution is widely used by financial institutions to manage customer interactions.

Many banks, card issuers, and fintech companies manually extract and duplicate data from Salesforce to complete credit checks, risk scoring and due diligence processes using legacy systems.

This is slow and inefficient. And it can change. When credit and lending decisioning processes and Salesforce are connected, there can be seamless data exchange. Through connected ecosystems, a single data set can drive real-time risk analytics and decisioning.

The right technology, pre-integrated with Salesforce can help automate loan and credit origination. It can help your business:

  1. Increase use of Salesforce CRM data throughout the organization – listening for, reading and writing data into and out of Salesforce eliminates the manual moving of data from Salesforce to legacy systems. Technology can also enrich native Salesforce data with information maintained in other systems, which can be created and stored as custom fields within Salesforce.
  2. Automate originations and underwriting processes – by leveraging decisioning technology that can easily integrate to external and internal data sources and bureaus, organizations can make real time decisions based on the aggregated data, operationalize any risk models in minutes and use Salesforce to automate originations and underwriting. Also read: What is credit underwriting?
  3. Create a more transparent lending process – with a 360-degree view of your customers at a glance. You can unify your entire lending business through a single platform, giving borrowers, lenders, brokers, underwriters, and every member of your team a transparent view of the lending process.
  4. Provide end-to-end compliance and better reporting – automatically aggregated data from internal systems, KYCnet, and other external systems can be made available to a compliance interface built within Salesforce. Capabilities such as business rules that ensure only the right data is aggregated for each client simplify compliance end-to-end.
  5. Tailor product offers to the right customers – customers expect companies to know what they need when they need it. Combining a CRM such as Salesforce with a credit decisioning system allows businesses to collate the data they need by connected siloed data. So, you can take a consumer—not product—centric approach.
  6. Preapprove offers for existing customers – in addition to providing a customer focused offer, integrating Salesforce with a loan decisioning solution allows a business to preapprove customers for specific offers. This ensures that you only promote offers that are suitable for the customer and improves the application process.
  7. Target customers based on life events, financial triggers, or specific behaviors – data analytics can help your business predict the need for financial services based on event or behavior triggers such as marriage, saving habits, or even reduced the use of their existing products. With an integrated CRM and decisioning solution you’ll be able to not just predict the need for services but also choose the right product and preapprove the customer before reaching out through a tailored marketing campaign.

The benefits of using Salesforce for Credit/Loan origination and risk decisioning

The number of benefits that combining a CRM such as Salesforce are many and they don’t just offer small opportunities to advance your lending business. In fact, this perfect combination of technologies will empower your business to create a smarter, faster, and more customer centric user experience.

It will also bring many business gains such as the automation of manual lending processes, better KYC monitoring, and smarter decisioning to reduce risk. One huge opportunity these technologies, when used together, offer is the chance to grow and evolve your business. With analytics and customer knowledge deeply ingrained into your origination technology you’ll have a much clearer understanding of consumer needs, empowering your business to better target customers and develop products that best meet the needs of an evolving market.

Using Provenir within your Salesforce environment

With the Provenir pre-built integration adapter for Salesforce, financial institutions can automate complex analytics and decisioning processes for credit and loan applications from within their Salesforce environment.

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Python Vs. R

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Python Vs. R

The 90’s were responsible for a number of incredible developments including the internet, which forever changed the world. 90’s culture isn’t often seen in a positive light, but don’t forget it was the decade that bought both Python and R into the world. These two programing languages gave data scientists an immense amount of power to operationalize risk models, and in turn created the Python vs. R debate that’s still argued 30 years later.

When it’s time to choose the right programing option for your next risk model wouldn’t it be nice if selecting a model language was as simple as Neo’s choice in the Matrix?

“You take the blue pill—the story ends, you wake up in your bed and believe whatever you want to believe. You take the red pill—you stay in Wonderland, and I show you how deep the rabbit hole goes.”

After all, when it comes to risk analysis and data analytics the answer is easy, you need the red pill to get answers…

The red option lets you jump into the data rabbit hole, analyze the information, and get the answers you need to solve your risk questions.

So, what does that mean for Python vs. R? It means the question is, “would you like this red pill or this other red pill?”

Choosing Your Medicine—Which pill will answer your risk questions?

R and Python are two of the most popular programming languages in the analytical domain and are considered close contenders by many data analysts and scientists. Take a look at what they have in common:

  • they’re free
  • they’re supported by active communities
  • they offer open source tools and libraries

As awesome as these similarities are, the fact that both R and Python tick all three boxes can often make it difficult to choose one over the other.

In the Matrix, which we’d like to point out was another stellar 90s creation, Morpheus gave Neo the pill for a specific use—to identify his body’s signal from millions of others, then use that information to collect him. It’s not unlike a risk or analytics model, where you need the right code to collect and analyze the required data. So, with both Python and R offering powerful modeling capabilities to grant you entry to the data rabbit hole, the real question is: Which red pill offers the easiest route to the data and provides the results in a useable way?

Consider how the model will be used

So, it’s not just the capabilities of a program that influence the preference of R or Python, it’s also the context it’s being used in. R’s strength is in statistical and graphical models, and it sees more adoption from academicians, data scientists, and statisticians. Whereas, Python, which focuses more on productivity and code readability, is popular with developers, engineers, and programmers.

As a general-purpose language Python is widely used in many fields including web development. It’s also gaining popularity across investment banking and hedge funds, and is deployed by banks for pricing, risk management, and trade management platforms. Yet, surprisingly, unlike R, knowing Python is not yet a common requirement for tech talent working in most areas of financial services. So, in the Python vs. R debate, data scientists with a heavy software engineering background may prefer Python, while statisticians may rely more on R.

Usability

Python has acquired a positive response from data scientists involved in machine learning. Since the learning curve is low for its users, Python’s real strength lies in its simplicity, unmatched readability, and flexibility—all powered by a precise and efficient syntax. Since it is a full-fledged programming language, Python is great for implementing algorithms for production use as well as for integrating web apps in data analytical tasks.

On the other hand, R is great for exploratory work and is suitable for complex statistical analysis, owed to its growing number of packages. But the drawback for R beginners is that R has a steep learning curve and often makes the search for packages difficult. This can prolong the data analysis process and cause delays in implementation. While R is a great tool, it is limited in terms of what it can accomplish beyond data analysis. Many of the user libraries in R are poorly written and often considered slow, which can be an issue for users.

Library and Packages

Python has extensive libraries that significantly reduce the time span between project commencement and meaningful results. The repository of software for the Python programming language is so rich that the Python Package Index (PyPI) currently comprises of 130,641 packages. The library has a variety of environments to test and compare machine learning algorithms.

The packages offer solutions that are not only intuitive but also flexible. A good example is PyBrain, which is a modular machine learning library offering powerful algorithms for machine learning tasks. Considered to be a popular machine learning library, Scikit-learn offers data-mining tools to bolster Python’s existing superior machine learning usability.

In comparison, CRAN (Comprehensive R Archive Network) remains a huge repository with 10,000 packages that can be easily installed in R. Active users contribute in the growing repository on a daily basis and many of the capabilities of R (like statistical computing, data visualization) are unmatched. While the learning curve for beginners is steep, once a user knows the basics, it becomes much quicker to learn advanced techniques. For many statisticians, implementation and documentation in R are more approachable than in Python.

But newly installed packages in both Python and R are alleviating the weaknesses that each suffers. For example, Altair for Python and dplyr for R support the traditional flow of data visualization and data wrangling.

Data Visualization

Data Visualizations is an integral part of data analysis and can simplify complex information by identifying patterns and correlations.

R’s visualization packages include ggplot2, ggvis, googleVis, and rCharts. Visualizations through R can efficiently and effectively, make the most complex raw data set look informative and pleasing to the eye.

When compared to R, Python has a huge amount of interactive options like Geoplotlib and Bokeh and picking the best and most relevant can sometimes get exhausting and complex. Data visualization is delivered better through R and appears less complicated.

Choosing Between R and Python

So far, Python is considered a challenger to R and remains more popular due to its wide-usability and because it can implement production code. But to be fair, both R and Python come with their own set of pros and cons, and the decision to deploy the right one primarily depends on what kind of data set you are looking at and what problem you need to solve.

Both are constantly developing at a rapid pace and there is currently no universal standard for picking one over the other.

How to Integrate Risk Models Without Wasting Time and Money

Whether they choose Python, R, or another option, companies spend huge amounts of time developing risk models to figure out which customers provide the least risk for their business. One of the biggest challenges businesses face is how to operationalize these models quickly and efficiently. This can be especially difficult with complex models that are made possible with R and Python as many risk ‘solutions’ require the models to be translated into code that it can understand. If your business is using one of these solutions you’ve probably already experienced the high cost and excessive time needed to connect your latest model to your risk decisioning process.

As simple solution to these pain points is to use a model-agnostic risk decisioning solution. With a model-agnostic risk platform you’re free to choose the risk model that helps you navigate through the rabbit hole and secure the risk answers that you need to keep your business moving forward. The Provenir Risk Decisioning Platform is a great example of this model-agnostic approach. By using simple wizards risk models developed in a variety of tools can easily be imported, mapped and validated. Provenir automatically generates a list of the data fields; all you need to do is pick the data Provenir needs to send to the model, as well as the data the model should send back to Provenir to drive the decisioning. This entire process takes just a few minutes, which means you not only gain an effective way to maximize the value of your models, but can also instantly adapt risk decisioning processes whenever a model changes.

If your business is wasting time and money implementing risk models take some advice from Morpheus in the Matrix, “What are you waiting for? With Provenir you’re faster than this.”

What? That’s exactly how it went in the movie…

See how simple it is to operationalize risk models in Provenir with this insider how-to.

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Merge Ahead – What Happens When Buy Now, Pay Later and the Credit Card Industry Intersect?

Written by Allison Karavos on . Posted in Blog.

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Merge Ahead –
What Happens When Buy Now, Pay Later and the Credit Card Industry Intersect?

Accelerating growth by working together

The BNPL industry, while similar in theory to credit cards, has provided a unique twist on credit – it’s immediate, it’s typically for a single purchase (traditional BNPL products don’t offer an open-ended credit limit) and there is usually a set installment plan for repayments (most often three or four).

But even that is changing – as more BNPL providers enter the growing market space, they are changing the rules (and the products offered) at a rapid pace. As providers and products continue to evolve, the wants and needs of customers are coming sharply into focus – for example, some consumers have expressed frustration at the one-and-done aspect of installment plans at checkout, and would rather have a revolving, renewable credit limit, which certain providers now offer.

BNPL in its current state began as a 21st century, usually internet-based alternative to credit cards, allowing consumers to purchase items at point of sale (either online or in a physical store location) via installment plans. Australia is often credited with being the pioneer of BNPL, with giants like AfterPay and OpenPay, but Sweden-based Klarna brought the movement to Europe and other companies began offering BNPL services across the globe in quick succession. As more regulatory oversight comes into play and more widely varied BNPL products emerge on the scene, industry analysts and other providers may increasingly look to how Australian products react to shifts in trends as an indicator for how the evolution of the market will play out globally.

It’s clear the fintech buzz-term of the decade has to be Buy Now, Pay Later – it’s impossible to get away from. But as BNPL continues to grow and evolve so rapidly, where does that leave the credit card industry?

BNPL Crashes Credit Cards’ Party

The impact of the meteoric rise of BNPL has not gone unnoticed by the credit card industry. Consumers, especially the younger generation, have been actively looking for alternatives to high-interest credit cards, forcing traditional lenders to be more competitive if they want to stay relevant. Sixty-two percent of current BNPL customers think that the payment concept could completely replace their credit cards1, with just over a third of users in the U.S. (36%) being repeat customers – utilizing BNPL services once a month or more2. As of April 2021, about 5.8 million Australians have a BNPL account, while 38 percent of people in Singapore utilize BNPL for frequent purchases.3

And why is BNPL so popular? 47% of users take advantage of BNPL plans because they want to avoid paying credit card interest. Other reasons included their friends using BNPL, credit cards being maxed out, and feeling more responsible when they break large purchases into smaller payments2. In Europe, “BNPL options are projected to see the largest gains in usage [in e-commerce] in the next three years and almost double their share until 2024, accounting for almost 14% of the spending on e-commerce in Europe.”

With BNPL on its rapid ascent, where does that leave credit cards? Since the start of the Covid-19 pandemic, the value of credit card transactions in the United States has dropped by approximately 11%4. You could argue that consumers were spending less thanks to job loss and closed stores amid economic uncertainty, and there may not be definitive data yet to suggest they are shifting spending from credit to BNPL, but data does suggest “a universal decline in credit card ownership,” particularly among Gen Z consumers, half of whom don’t even own a credit card4. As the revenue streams of big banks in certain regions dry up thanks to loss of interchange and other card-related fees, the credit card industry is looking for ways to offset those declining transactions. Rather than looking at BNPL as direct competition, banks and other traditional lenders can look at BNPL as an opportunity.

“As the digital word spins faster, people expect financial services to be aligned with their busy and demanding lifestyles. The wide adoption of [BNPL products] being universally integrated into purchases reduces friction, grows sales, and gives consumers more options to improve the customer experience. Banks that ignore this market dynamic risk missing out on the opportunity to engage with future generations of borrowers.”4

Lending United

How can BNPL and credit cards work together then? Is there an ideal future state where each space merges together to offer consumers the best of both worlds? There are pros and cons to traditional credit card lending as well as Buy Now, Pay Later plans – depending on a particular consumer’s perspective, risk comfort level, cash flow needs, credit history, etc. If banks were able to include checkout purchase options in their digitization strategy4, they could help ensure longer-term growth potential – perhaps giving users options to pay in installments via credit card or offering lower interest rates. Some consumers may not be aware that the traditional BNPL model shifts the interest fees away from the consumer and onto the merchant, offering a mutually attractive option for both (less perceived interest on the part of consumers, and larger carts with less abandonment for merchants). But, in part as a result of the apparent lack of interest, some 30% of BNPL users trust BNPL providers more than credit cards when it comes to fair business practices.5  

BNPL providers could protect consumers with a blended approach – keeping the simplified lending consumers love (particularly at checkout) but offering additional rewards or perks and ensuring that consumers are able to actually build credit with their use of installment payments. And the rewards of convergence would be worth it as the runway of opportunities is not getting any smaller – a study by Mastercard showed that “43% of consumers in Asia Pacific are willing to increase their spending by at least 15% if allowed to pay in installments.” Meanwhile a research study conducted by Coherent Market Insights showed that the global BNPL market will grow to upwards of $33.6 billion by 2027 (a massive increase from $7.6 billion in 2019)6. To capitalize on this growing market (and those consumers who want to increase their spending) MasterCard invested in Pine Labs, an Asia-based BNPL provider. The partnership aims to bring new installment plans to Asia, with a solution rolled out earlier this year in Thailand and the Philippines, followed by Vietnam, Singapore and Indonesia. Credit, debit and bank cardholders in the region will subsequently have access to installment plans for both online and bricks and mortar merchants upon checkout.

With more than half of the world’s consumer borrowing happening in the Asia Pacific region, there are massive opportunities for fintechs to offer shoppers what they want – namely flexibility and convenience. In a press release from earlier this year, Sandeep Malhotra, Mastercard’s Executive Vice President, Products & Innovation, Asia Pacific outlined the benefits to both consumers and merchants. Flexibility and cash flow management for the former; an increase in sales and reduction in cart abandonment for the latter. And of course, the development of an omni-channel solution benefits MasterCard too: “Installment options complement Mastercard’s wide range of payment programs and align completely with Mastercard’s mission of fostering an integrated, inclusive digital economy and delivering great checkout experiences with payments that are secure, simple and smart.”7

MasterCard isn’t the first credit card company to think outside the box (and they won’t be the last). American Express launched a BNPL-style service in 2017, with its cardholders utilizing installment payment plans for nearly $4 billion in purchases. The “Pay It, Plan It” program directs AMEX to pay a card transaction right after it’s made through a linkage with the consumer’s bank account, and then permits consumers to turn that card transaction into a short-term installment plan for a small fee8. As mentioned, traditionally the merchants pay these types of fees, meaning AMEX is shifting this cost to the consumers – another example of the many ways the term BNPL is no longer a one-size-fits-all product.

By utilizing some of the most-loved aspects of BNPL, credit cards can help ensure their ongoing relevance. And at the same time, evolving their lending methodology and risk decisioning processes could help them widen their net. For example, “alternative data can be used to better underwrite loans for consumers who fall outside of traditional credit metrics,” like the unbanked or underbanked (who usually love BNPL), particularly in regions where it’s very difficult for the average consumer to build credit because spending profiles have changed9. Using technology to better access and aggregate real-time data to make better credit decisions can also potentially provide an easier intersection of BNPL and credit cards – better data and instant decisioning means less risk, allowing providers to offer more personalized payment plans. As Jim Marous put it, “financial institutions must deliver innovative credit options, on-demand, in an almost instantaneous manner” to capture the hearts (and wallets) of their target audience10.   

A Match Made in Fintech Heaven

The future of BNPL is here – in fact, it changes almost daily. And the credit card industry can clearly capitalize on that on a wider scale, if the success of the regional, niche partnerships that MasterCard and AMEX offer are any indication. Not only does a thoughtful marriage help both industries continue to benefit from the incredibly large runway of available market share, it helps merchants and consumers too. As we strive for more inclusion in this increasingly global, diverse world, BNPL and credit cards have the opportunity to give more viable lending/payment/purchasing options and more protection to all types of consumers, especially the unbanked and underbanked. And what better way to help stimulate the global economy than being sure that everyone has a chance to participate in it?

For more information on what industry influencers say about the future landscape of BNPL, read our latest eBook.

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Why Customer Experience is so important in financial services, and how a unified decisioning platform can help

Written by Allison Karavos on . Posted in Blog.

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Why Customer Experience is so important in financial services,
and how a unified decisioning platform can help

Enhancing Customer Experience Across the Entire Lifecycle

Personalized experiences are everything these days, and the world of financial services is no exception. We expect our Netflix recommendations to be spot on, we download apps to preview what a new hairstyle would look like, and we trust our Instagram feeds to offer up relevant ad content… but some traditional financial services institutions are missing the boat on personalized, meaningful customer experiences. And their growth is suffering as a result. Why? In part, because legacy systems (including data infrastructure and decisioning platforms) just can’t handle making more personalized offerings. To truly enhance the customer experience from end-to-end, financial services institutions, both big and small, need to look to an all-in-one, unified decisioning platform.

In times of economic uncertainty, banks and fintechs may be looking for ways to cut costs. But improved customer engagement is key to both creating long-term loyalty and acquiring new customers – both of which can help you weather any economic storms. Sometimes, you need to invest strategically to react appropriately to market disruption. As the Financial Brand shares, “No matter what the future brings, financial institutions need to develop a strategy that can recession-proof and future-proof their current business models. The banking industry has a once-in-a-generation opportunity to transform legacy business models to become more competitive and more resilient during economic upheaval. By integrating data, analytics, advanced technologies, automation and an up-skilled workforce, banks and credit unions can become more future-ready and agile in a crisis.” 

CX: The Rise of Instant Everything

So, what exactly does the oft-overused term Customer Experience really mean? And why is it so critical to an organization’s success? Broadly, customer experience is the impression your customers have of your brand and your solutions as they interact with you – at all stages of the buyer’s journey, from first view of an ad or consuming your content all the way through to purchasing, onboarding and renewals. Customer experience matters in everything we as individuals consume – think about how you feel about your favorite grocery store, smartphone, hair products or exercise program (is Peloton actually a cult?). Now think about how you feel about your banking apps, your credit cards, your mortgage company, your last auto financing application. Are those everyday financial transactions memorable (in a good way)? Do you feel seen? Do you feel like your needs are being met in a personalized way?

Consumers want instant answers and tailored offers with their lending/banking experiences, with far less waiting and paperwork. With the rapid increase in digital-only banking and fintech innovations like buy now, pay later (BNPL) and embedded financing apps, providing anything less than a stellar customer experience means fewer repeat customers and a decrease in brand value.

Accenture reports that 5% of traditional banks’ revenue is at risk as millions of consumers are enticed by the transparent, tailored offerings of fintechs and neobanks. A 2020 survey of credit union/bank marketing leaders found that personalized approaches are the most effective for engaging people and expanding share-of-wallet. But 44% of those same organizations only send a couple of targeted marketing emails per year. Why? “Limited data insights make it difficult to truly understand a consumer and what they need in the moment.”

Data + AI-Powered Decisioning Technology = More Satisfied Customers

What does your risk decisioning platform have to do with the customer experience? In one word… everything. While some financial institutions are still using siloed environments and separate vendors or partners for data, decisioning workflows, analytics models and business insights, the more agile, adaptable organizations are looking at unified, all-in-one decisioning platforms. One solution that integrates real-time data, advanced analytics, artificial intelligence and machine learning (AI/ML), and decisioning automation can help accelerate digital transformation for a more customer-centric experience. With a unified solution, you can:

  • Make smarter, more accurate decisions
  • Shorten the product development lifecycle and get new products/offerings to market faster
  • See real-time views of decisioning and performance data to uncover actionable business insights
  • Create streamlined user experiences across the customer lifecycle
  • Scale and grow your business to respond to market trends and consumer demands (with fewer growing pains for your loyal customers along for the ride)
  • Democratize data access for more holistic views of your customers
  • Optimize pricing and product offerings
  • Expand your customer relationships with personalized upsell/cross-sell offers

McKinsey doesn’t hold back: “Predictive customer insight is the future.” Their article on ‘Future of CX’ predictions states that “those with an eye toward the future are boosting their data and analytics capabilities and harnessing predictive insights to connect more closely with their customers, anticipate behaviors, and identify CX issues and opportunities in real time.” While customer success teams and feedback surveys will always have a place in understanding the consumer experience, it’s clear that real, actionable data that can be analyzed in real-time is a game-changer.

But accessing, integrating, and analyzing data is not the only challenge facing financial institutions who desire a better experience for their customers. In today’s ultra-competitive landscape, doing so at speed is critical. As the Financial Brand shares, “Speed is a competitive weapon. The ability to see market trends, adjust strategies, innovate, create new solutions, make tactical decisions, and deploy resources quickly provides a business advantage… Banks and credit unions can no longer respond to opportunities and challenges with a legacy banking timetable.” But being able to adapt your offerings and pivot to new products or strategies quickly is next-to-impossible without an integrated, unified solution.

What to Look for in a Unified Solution

If you’re overwhelmed by the idea of choosing yet another technology partner, don’t fret. We’ve looked at some key factors to consider when evaluating decisioning platforms.

  • No-code Management: Can you easily integrate systems, change processes, and launch new products, without relying heavily on your IT team and/or your technology vendors? Is your team empowered with a low/no-code UI (see, the customer experience matters in absolutely everything!) that can offer you things like pre-built data integrations and drag-and-drop functionality?
  • Connected Data: Do you have easy access to both real-time and historical data, including alternative data sources? Can you centralize your data sources (goodbye silos) so users can more efficiently manage various data sets across the credit lifecycle and make smarter decisions?
  • Centralized Control Across the Lifecycle: Can you bring together data and decisioning to better manage identity, fraud, and credit decisions across the entire lifecycle of the customer? Are you able to efficiently connect all systems to fully understand customer needs and personalize user experiences? When consumers are expecting seamless experiences, with tailored financial services and protection from fraud, can your technology deliver?
  • Auto-Optimization: Does your decisioning platform get more accurate each time decisions are made? Are you able to see how your current risk models are performing, and can you respond to these performance shifts once you’ve spotted them? Instead of relying on humans or individual systems to uncover opportunities for improvements, can you connect all systems and power a continuous feedback loop that keeps optimizing your decisions?
  • Ability to Scale: Your systems may be working well enough for now, but as the industry continues to grow more and more competitive… can they adapt and scale with you in the future? Does your decisioning solution simplify your growth or inhibit it?

A unified decisioning platform not only powers more accurate decisions across the entire customer journey, but it enables rapid growth and innovation opportunities. Instead of waiting for vendors to make workflow changes or sifting through siloed sets of data, you can spend more time focusing on what matters – your customers. Adapt as the market shifts, diversify to meet your customers’ needs, personalize offers to encourage engagement and brand loyalty. The opportunities for enhancing the customer experience are endless – and with a holistic, unified view of your data and decisioning, you don’t have to compromise your risk strategy to do so.

Discover more benefits of unified access to AI-powered decisioning and the data that fuels it.

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