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Simplifying the Merchant Onboarding Process with Automation

Written by Allison Karavos on . Posted in Blog. No Comments on Simplifying the Merchant Onboarding Process with Automation

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Simplifying the Merchant Onboarding Process
with Automation

The Challenges of Manual Merchant Onboarding

Merchant onboarding is a critical process for acquiring businesses that involves acquiring, analyzing, and integrating large volumes of data. However, the manual and time-consuming nature of this process often results in delays and errors. For instance, if data or knowledge of the merchant is lacking, then identity can’t be validated. Compliance with Know Your Customer (KYC) and other governmental regulations has to be determined, as does creditworthiness. This takes days and can still involve a high degree of manual handling. To streamline this process and make it more efficient, automation is the way forward.

Compliance with KYC and Other Regulations

KYC stands for “Know Your Customer” and is a process that financial institutions and other regulated companies use to verify the identity of their clients. This process involves collecting and verifying various types of information about the client, such as their name, address, date of birth, and other identifying information. The objective of KYC is to prevent financial crimes such as money laundering, terrorist financing, and other fraudulent activities.

During the merchant onboarding process, compliance with KYC and other governmental regulations is required. Failure to comply with these regulations can result in fines and other legal consequences. By automating the merchant onboarding process, companies can streamline the KYC process, making it quicker and more efficient, while also ensuring compliance with regulatory requirements.

The Benefits of Automation

Simplified Data Integration

To simplify data integration, acquirers need to access and efficiently handle and analyze all data sources such as bank account information, commercial data, address verification, KYC checks, credit score, and more. To achieve this, a merchant onboarding solution with integration capabilities that can rapidly aggregate data from various sources is required. Non-standard data, such as that from social media, can supplement sources – if the acquirer has the means to get at it and pull out what’s relevant. To achieve this, the best solutions offer pre-built adaptors built on industry standards.

Operationalized Risk Models

Operationalized risk models play a critical role in the merchant onboarding process. They integrate with the other elements that make up the end-to-end merchant onboarding process, ensuring that risk decision-making is not a bottleneck in the process. Technology and model-agnostic solutions can integrate with SAS, Excel, and anything else besides. Business-defined rules lay down the terms and conditions for each merchant and identify exceptions that require further investigation. A visual interface lets business users quickly establish the relationship between the risk model and the automated onboarding process.

Effective management of data is essential to a rapid, efficient merchant onboarding process. Technology for automated risk analytics and decision-making integrated into the onboarding process taps into multiple data sources and systems for a streamlined end-to-end process. To learn more about simplified data integration and operationalized risk models for merchant onboarding, check out our guide on our website.

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Machine learning – all a bit ‘Skynet’?

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Machine learning –
all a bit ‘Skynet’?

Machine Learning – Revolutionizing Financial Risk Analysis and Decision-Making

While the concept of Machine Learning (ML) may conjure up images of a dystopian future where machines have taken over the world, much like Skynet in the Terminator movies, the reality is quite different. While Skynet may have been a malicious and all-powerful AI system, Machine Learning is simply a tool that can help us better understand and leverage data. So, while Skynet may have been the ultimate villain, Machine Learning is more like the trusty sidekick that helps us save the day.

Here’s how ML is rapidly becoming a game-changer in the field of financial risk analysis and decision-making:

The Power of Data

Machine learning enables businesses to gather and analyze data faster, thereby arriving at insights quicker. This is because the software program uses pattern recognition to build automatic analytical models, eliminating the need for human intervention.

Dynamic Fraud Detection

Machine learning algorithms can learn from a customer’s previous transactions and use them to identify patterns of behavior, allowing for dynamic fraud detection. This eliminates the inconvenience of manual validation processes while also increasing fraud detection rates, saving considerable costs.

Huge Cost Savings

According to analysis firm Oakhall, global financial services firms could save $12 billion annually through machine learning fraud management. This underscores the tremendous potential for risk analysis and decision-making with machine learning.

Harnessing Machine Learning for Predictive Analytics

To fully benefit from the predictive analytics power of machine learning, financial institutions need a fast, simple way to connect their machine learning application to their credit and lending decisioning processes.

Machine learning is revolutionizing the financial risk analysis and decision-making process. Its power lies in its ability to gather and analyze data faster, dynamically detect fraud, and save costs. By harnessing its predictive analytics capabilities, businesses can unlock its full potential for risk analysis.

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Loan Origination Software Plays Its Part in Banking’s Digital Transformation

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Loan Origination Software
Plays Its Part in Banking’s Digital Transformation

Loan origination — and, subsequently, loan origination software — is at an interesting intersection right now.

At a less institutional level, peer-to-peer lending is expected to grow at a CAGR of 53% between 2016 and 2020. But as lending technology matures, its impact could reduce profits in American banks by $11 billion per year, or roughly 7%.

This evaporation of margins is not a new problem for banks, though it is one that is increasingly disconcerting. In 2012, for example, the share of risk and compliance within general banking costs was 10%, a large portion of overall costs as they were. In 2017, risk and compliance are expected to consume 15% or greater. While costs are rising, it’s hard to actually mitigate risk with incremental risk management improvements. In large part, return on equity in banking often resides below the cost of capital, impacted by capital building projects and fines.

The result: to see increased growth into the next decade, banks are digitizing more processes. This has begun to happen, but a variety of studies — including many on millennial bankers — shows the digital transformation of financial services has not yet fully arrived. Since lending is a huge revenue source for banks across virtually all segments from small business to enterprise, making sure digital loan origination is properly executed is preeminent for many banks now.

As McKinsey has noted, the shift to increased digital transformation focus in financial services came about because of five distinct pressures (paraphrasing here):

  • Changing customer expectations: Consider the rise of mobile and on-demand experiences.
  • More regulations and risk-function effectiveness: Seen in increased regulations in most first-world economies, as well as more fines being dealt since the 2008 crisis.
  • Data management and advanced analytics are hallmarks of competitive banks now: Buzzword or not, we are living in a Big Data era.
  • Disruption: Risk management programs are essential for banks to compete with upstarts — if the upstart makes a big bet and misses, the established bank can favorably reposition.
  • Increasing pressure on costs/returns: And as noted above, risk management doesn’t necessarily deliver in this way on the balance sheet.

As such, rapid-fire, on-demand loan origination programs and software have begun cropping up in the financial services world. Why? When risk decisions are made in seconds, loan origination cycles shorten for the customer. Shorter cycles create positive customer experiences, brand loyalty, connection to the bank and its relationship managers, and continued business. Speed can be good.

But, banks attempting a new approach to loan origination need more than speed. While quick risk decisioning and credit scoring is crucial, a loan origination software program also needs:

  • The ability to process both structured and unstructured data: This would allow for the incorporation of both standard and alternative decisioning, i.e. credit documents vs. items from a loan applicant’s blog.
  • Compliance: Perhaps the most important at the bank level, compliance calculation and True in Lending Act (TILA) disclosures need to be compliant, and documents must be in compliance with the Electronic Fund Transfer Act.
  • “Look for initiatives within easy technological reach:” That’s advice from McKinsey above, and it makes good sense. Some loan origination software barely requires advanced coding anymore, so your IT side can work on more value-add internal projects.

There are other considerations such as ease to operationalize risk models that should be deployed.

Financial services, and especially loan origination have long suffered from a lack of transparency and simplicity. It oftentimes seemed that financial services firms were underserved by technology, or “square-peg/round-holing” the problem. That’s not the case anymore, and loan origination software and approaches are of huge value for established banks as a way to drive a growth culture forward. The crucial step is the right partner for your specific needs.

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Loan Origination in the Golden Age of Instant Everything

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Loan Origination in the Golden Age
of Instant Everything

It may look like the golden age of “instant.” The casual iPhone user can get an Egg McMuffin delivered to their door while it’s still steam-hot in the bag. The average Twitter user can use their ampersand key to swat through a brand’s customer service obstacles like Arnold Schwarzenegger cutting through the jungle in Predator. It may look like we’re in the golden age of immediate-results technology, but we’ve only just reached the earliest, primordial stages in its existence, and consumers have already and instantly adjusted to the instant  age.

As the world of apps grows, consumers have, and will, expect every corner of our daily lives, especially the institutions that manage our financial loans or our medical information, to be as cloud-efficient and scalable as the app that schedules an undergrad to walk your dog while you’re in a meeting. Technology tends to evolve inwardly and sensitively, finding it’s way into our banks and homes–not broadly and impersonally. Notwithstanding, loan origination systems are a deeply personal technology.

As a microcosm-example, a 2016 piece in Forbes summarized that brands who engage in direct customer service via Twitter see a 19% increase in customer satisfaction. Loan origination is one of life’s sensitive areas–vulnerable like our medical information or mortgage payments–that needs to adapt to this instant-evolution.

Picture the expectations of instant response when we have an artificially intelligent platform accessible from a contact lens. How long do you think a customer will tolerate waiting for approval or a green “success” check or a loan when all of the technology around us has already reached the speed and accessibility only dreamed up in Ray Kurzweil novels?

Waiting, whether for your breakfast sandwich or your loan decision, is as a dead and dusty a concept as your CD-RWs. Instant technology that learns for the individual and can execute in real time is the present and future. We’re already expecting banking to happen in the blink of an eye. In the future–especially in light of the cascading failures in recent financial technology–it will need to happen even faster, more efficiently, and securely.

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The Algorithm Challenge – Using AI for Risk Decisioning

Written by Allison Karavos on . Posted in Blog.

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The Algorithm Challenge –
Using AI for Risk Decisioning

  • Giampaolo Levorato, Senior Data Scientist, Provenir

How to implement advanced AI algorithms for improvements across the modeling lifecycle

We’ve all heard the term Big Data, and the world of financial services is no exception. Big data refers to large, structured and unstructured sets of information growing at ever increasing rates. Data drives key decisions made by fintechs and financial services organizations – everything from helping determine identity and approving a car loan or a mortgage to optimizing pricing and deciding when to upsell a current customer. The surge in volume, variety and velocity of data has led financial institutions to use advanced machine learning algorithms to make smarter, faster decisions. But using AI is not without challenges. There can be several obstacles to successful deployment, including choosing the right algorithms, interpreting and explaining complex models, deploying the models, ensuring the infrastructure is sufficient, and managing bias.

AI Challenges

  1. Choosing the right algorithm: not all algorithms perform equally well on the same dataset. Depending on the nature of the data, organizations must be able to choose and configure the best algorithm to fit their data.
  2. Model complexity, interpretability and explainability: the intricacy of AI algorithms can make them “black boxes” in the sense that often even the developers don’t know why and how the algorithms make the decisions they do.
  3. Model deployment: deploying a model into production requires coordination between data scientists, software developers and business users, posing a challenge with regards to the different programming languages and approaches that need to be unified into one solution.
  4. Infrastructure requirements: many organizations lack the infrastructure required for data modeling and reusability. Being able to quickly develop and test different tools, across different, large datasets, is essential to producing more accurate, manageable results.
  5. Exclusion bias: many consumers globally remain ‘credit invisible’ or thin-filed, meaning that little-to-no credit scores are available for them.

Overcoming the AI hurdles

What’s the best way to tackle these challenges? Financial services organizations should transition from traditional Generalized Linear Models (GLM) to explainable AI algorithms to improve the speed and accuracy of their decisions. According to a recent survey conducted by Pulse and Provenir, 69% of companies plan to invest in AI-enabled credit decisioning in 2022.  AI algorithms can also help to more easily identify fraud and creates opportunities for improvement of the customer experience across the entire lifecycle.

Benefits of AI

  • Algorithm Optimization: choose the most appropriate algorithms from a wide variety of options, including Gradient Boosting Decision Trees, Random Forests and Deep Neural Networks, depending on the nature of the dataset.
  • Interpretability and Explainability: through a careful adoption of SHAP and LIME explanation methods it is possible to explain how and why your model has made a prediction.
  • Ease of Deployment: use of a unified platform enables seamless deployment, allowing businesses to take fast, effective action.
  • Scalability: reduce the development time from months to days by automatically training, testing, monitoring and managing your model.
  • Diverse Data: by leveraging traditional and alternative data, improve your model accuracy, while managing bias and promoting financial inclusion.

Moving to AI algorithms has numerous benefits – including higher accuracy, improved compliance and superior scalability – all of which have tremendous impact on your overall business stability and growth. Using AI algorithms means more predictive, more accurate models, resulting in increased profits, reduced losses and more up-to-date risk assessments. After conducting internal research, Provenir has observed that AI algorithms can improve a model’s accuracy by up to 7%, while automated model development and deployment can reduce time and effort by up to 90%. This automation ensures faster speed-to-market with more accurate models and the ability to quickly respond to consumer needs and market trends, for true scalability. And the effects of this go beyond an individual business when you consider the further-reaching implications on the economy as a whole – The Wall Street Journal forecasted a 14% increase in the global GDP by 2030 thanks to the advancements of AI.

More legislation is now in play that requires full explainability of models. Fully interpretable and explainable models meet these requirements by clearly demonstrating how and why models make the decisions they do. In addition to compliance, model governance can be incredibly difficult with traditionally siloed environments. Separate environments for data collection, model development, deployment and monitoring require an immense amount of time and resources to integrate.  With a cohesive, all-in-one environment you eliminate that integration time and effort, enabling live, real-time results and helping reduce human error from manual processes.

The Value of a Unified Platform

Further to the siloed environments of data collection, model development, deployment and monitoring, models are also often built separately from decision engines and unnecessarily moving data between them increases time, effort and the probability of errors. With a unified platform that incorporates data, AI and decisioning, models are built and implemented in the same platform, ensuring seamless data and model integration, eliminating recoding delays and ensuring maximum performance of your models. In Provenir’s experience, models implemented in a unified platform can save up to 30% of a modeling project’s overall time and effort.

But what makes AI so powerful and capable? It’s all about the data. The more data your AI models have, the better your advanced algorithms will perform. A data-agnostic platform that can integrate and enrich your existing data sets with any other type of data set (i.e. various forms of alternative data) is critical. This seamless integration to a wide variety of data sources helps to encourage financial inclusion, manage bias and improves the predictive power of your models. And it’s not a one-and-done deal – true value comes from the continuous improvement that happens when you bring data, AI and decisioning together. Model monitoring and a constant feedback loop helps you fine-tune your decisions for continual optimization.

Being able to increase your predictive power and make more accurate decisions has impacts across the entire customer lifecycle. Real-time dashboards and reports help you stay up-to-date on changes with your customers, your portfolio and all of your models – allowing you to automatically generate updated predictive models, with everything available for live monitoring. This helps to enable better relationships with your customers, increases your agility in responding to market needs, and better predicts (and prevents!) fraud and loss.

According to The Economist, 86% of financial services executives are planning to increase their investment in AI – but most AI projects never make it out of the concept/planning stage. Despite how daunting moving from linear models to advanced AI models can seem, it is possible to implement AI and see results in under 60 days.

Check out our cheat guide to leveling up your risk decisioning with AI.

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Replacing Your Legacy Credit/Loan Application Processing Software

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Replacing Your Legacy Credit/Loan Application Processing Software

Your business has moved on, did your processing solution keep up?

Before The Gap was an international clothing brand, it was a small record store in San Francisco’s Lakeside district.

Similarly, tech giants LG got their start selling cosmetics, toothpaste and other personal hygiene products.

That’s right.

LG was originally the Lak-Hui Chemical Industrial Company.

There are dozens of stories like these. Businesses grow and evolve over time. And while you might never pivot as dramatically as The Gap or LG, your products and services have and will continue to evolve to meet new market demands.

It goes without saying, but if you shift your offering, expand into new markets, or even grow, your current software may no longer meet your needs. And while it can be tempting to try and adapt existing technology to meet current business requirements, it’s often like trying to fit a square peg into a round hole. When assessing the long-term feasibility of your existing loan application processing solution ask the following questions:

  1. What’s the cost of maintaining the current system?
  2. How much will it cost to make significant changes to meet new business needs?
  3. How long does it take to make changes?
  4. Is it making you less competitive?
  5. Do you rely on the vendor to make key updates?

Over time, keeping your software operating smoothly will cost much more than investing in new technology.

Don’t believe it?

Consider this. Outdated technology cost businesses $1.8 trillion in wasted productivity in 2016.

Is your software making you more, or less, competitive?

Can your current solution:

  • Be adapted to new business processes?
  • Support a growing number of users?
  • Automate repetitive tasks?
  • Handle operations on a bigger scale?
  • Power a first-class consumer experience?
  • Enable business users to make changes quickly?
  • Make integration to data sources and other tech solutions easy?

    Your credit application processing solution should power not impede business growth and help make you more competitive. If you’re constantly fighting the system to make changes, waiting on integrations due to complex coding, or sacrificing the consumer experience because the system can’t support instant approvals, then it’s time to make a change. Why? Because, if you can’t make changes quickly your business is exposed to increased risk and missed opportunities.

    Consumers demand instant decisions and the best user experience. For today’s tech savvy customer making them wait more than a few seconds for a loan decision is like expecting them to go back to the days of dialup internet. While it used to be fine to wait a minute for the internet connection to kick then another minute for a website to respond, it’s now considered slow if a website isn’t visible in just a couple of seconds. If you continue to use the ‘dialup’ of loan application solutions, expect your customers to have found an alternative option before the modem even starts to warble!

    Telltale signs your credit/loan application processing system is past its sell-by date include:

    • You rely on your dev team to make simple changes
    • Making sure it works properly is becoming increasingly expensive
    • Waiting on changes is slowing down business growth
    • It can’t scale to meet your business needs
    • It’s preventing you from making real-time decisions
    • Tie-dyed t-shirts, leisure suits, and mullets were acceptable fashion choices when you first started using the software

      What should you look for in a replacement?

      The benefits of replacing a legacy system far outweigh the temporary inconvenience of implementing a new loan application processing system, but how do you know which replacement solution to select?

      Here are five key things to look for in a replacement:

      1. A low-code solution – Low-code solutions allow you to configure, maintain and even create new processes without having to rely on your dev team. Instead, you can drag and drop different components to make changes quickly and easily. The right low-code solution can reduce or eliminate the delays caused when business teams have to rely on over-burdened dev teams or the solution vendor to make updates.
      2. Simplified integration capabilities – Integration, whether it’s to internal or external sources, is a challenge for many businesses but it shouldn’t be. Your credit application processing solution should make integration easy, so when new integrations are needed, which they will be, the reliance on dev involvement will be minimal and business users can take the lead.
      3. Advanced automation options – Process automation is a vital component to powering business growth and ensuring a first-class customer experience. Your new solution should make it easy to automate processes and also enable you to reuse automation components across multiple business processes.
      4. Scalability – You would never invest in a one bed property if you knew you’d need something bigger in a few weeks. So, why treat a processing solution any differently? If you’re investing the time and money in changing solutions you should choose one that you can keep for many years, which means picking one that is able to scale as your business grows!
      5. Flexibility – It’s impossible to predict what changes your business will need to adapt to in the future, so your credit application processing solution needs to be flexible enough to allow your business to remain agile. For example, Provenir’s simple drag and drop interface, allows you to build new tools easily when you need them, allowing the business to respond to changing markets and take advantage of new opportunities as they arise.

      Wrapping Up

      Saying goodbye is never easy. But when you find a credit application processing system that configures to your needs with minimal coding, integrates at lightning speed, and that actually makes your life simpler, you won’t regret making the change.

      After all, the (first) end of Michael Jordan’s basketball career brought a triumphant return, a record-breaking winning season, three championships (and how dare we forget about Space Jam). And, the end of Genesis, as the world knew it, brought Phil Collins’ solo career. What would the world be without that rendition of True Colors?

      Endings are just the opportunity for something new. It’s time to take the leap!

      The Ultimate guide to Decision Engines

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

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      Cloud Computing: A Brief History and Its Evolution

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      Cloud Computing:
      A Brief History and Its Evolution

      Cloud computing has come a long way since its humble beginnings in mainframe computing several decades ago. It has evolved to become an integral part of modern-day technology, offering businesses and individuals unprecedented flexibility and accessibility to vast computing resources. In this blog, we’ll explore the history and evolution of cloud computing, and how it has paved the way for a new generation of cloud computing that promises even greater value and automation.

      Cloud Computing’s Humble Beginnings

      The concept of cloud computing is rooted in the mainframe computing era of the 1950s, where computer systems were massive and expensive to operate. It was during this time that virtualization, the precursor to cloud computing, was first introduced. Virtualization allowed multiple users to share a single mainframe computer, reducing costs and increasing efficiency.

      Virtualization remained a niche technology until the advent of personal computers in the 1980s. With the rise of the internet in the 1990s, the concept of remotely accessing computing resources over a network began to take shape. However, it wasn’t until the 2000s that cloud computing as we know it today began to emerge.

      Amazon Leads the Way

      In 2006, Amazon launched Amazon Web Services (AWS), a cloud computing platform that allowed businesses and individuals to access computing resources over the internet. AWS was the first cloud platform to offer on-demand computing resources, allowing businesses to scale up or down as needed, without having to invest in expensive hardware or software. AWS quickly gained popularity, and other cloud providers, such as Microsoft and Google, followed suit. Today, cloud computing has become a ubiquitous technology, with businesses of all sizes using it to power their operations.

      We’re already starting to see the emergence of the “super cloud” or “cloud +1,” which sits above the cloud, offering businesses a ready-to-go system. This new cloud architecture will need an easy-to-use visual front-end so that users can assemble cloud “building blocks” for a total solution.

      The Financial Industry and Cloud Computing

      The financial industry has traditionally been slow to adopt cloud technology. However, this is changing rapidly. In a Cloud Security Alliance survey last year, 61% of respondents admitted that a cloud strategy is only in the formative stages within their organization.

      With the capabilities cloud computing offers, banks and other financial institutions can’t afford to ignore the cloud. By using cloud computing, they can do more with less and reduce high in-house IT costs.

      Cloud computing has come a long way since its inception in the mainframe computing era. From its humble beginnings, it has evolved to become an integral part of modern-day technology, offering businesses and individuals unparalleled access to vast computing resources. As we look to the future, the next generation of cloud computing promises even greater.

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      Celebrating World Health Day 2022 – #HealthierTomorrow

      Written by Allison Karavos on . Posted in Blog.

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      Celebrating World Health Day 2022
      #HealthierTomorrow

      10 Fintechs / Finservs Impacting Global Health

      April 7 is World Health Day – a day celebrated annually to mark the anniversary of the founding of the World Health Organization (WHO) in 1948. This year, the theme is centered around Our Planet, Our Health, with the goal to focus global attention on the actions needed to keep not just people, but the entire planet healthy. The more work we do as a society to promote the well-being of the earth and its inhabitants, the better off we’ll all be.

      Encouraging both individual health and promoting broader societal health initiatives (like accessible healthcare, clean drinking water and proper sanitation) is critical. Proven interventions such as government programs or charitable initiatives that expand individual access to health care and promote healthy behaviors (i.e. moderate exercise, adhering to prescriptions, eating more vegetables) can reduce the global disease burden by 40 percent over 20 years. And the economic impact is staggering. Better health could “add $12 trillion to global GDP in 2040 – an 8 percent boost, or 0.4 percent a year faster growth.”

      This is why the United Nations’ Sustainable Development Goals (and the companies that aim to meet them) are so important. Including everything from Good Health and Well-Being, Clean Water and Sanitation, to Affordable and Clean Energy, the goals aim to ensure equal access globally to the basic health fundamentals some of us take for granted. That inequality has been particularly prevalent in the past couple of years, revealing underlying weaknesses in all areas of society and highlighting the urgency of creating sustainable well-being for all individuals. The current global economic design is not sustainable – with an inequitable distribution of income, wealth and power resulting in a majority of the population living in poverty. As the World Health Organization states, “A well-being economy has human well-being, equity and ecological sustainability as its goals. These goals are translated into long-term investments, well-being budgets, social protection and legal and fiscal strategies. Breaking these cycles of destruction for the planet and human health requires legislative action, corporate reform and individuals to be supported and incentivized to make healthy choices.”

      We’ve chosen to take this World Health Day to look at some unique fintechs and financial services organizations who have these goals in mind – fostering better health for people and the planet at large.

      • Sempre Health – Working with regional health-care plans, Sempre Health encourages users to be responsible about taking medications as prescribed and re-filling as required. With discount codes reducing the cost of prescription co-pays and easy refill reminders, the incentive is clear – taking your prescribed meds as intended benefits your health and your wallet.
      • PayZen – A mobile app that uses data and AI to create individualized patient payment options, PayZen aims to make healthcare more affordable for American families. The company even offers an innovative spin on the ever-popular Buy Now, Pay Later (BNPL) product, with their own version referred to as Care Now, Pay Later. Health-care providers have access to PayZen’s predictive modeling power, enabling them to offer the right payment options to the right patients at the right time via a fully automated process.
      • Novus – The UK’s first B Corp certified neobank, Novus bills itself as a ‘force for good,’ offering a payment card where you earn impact coins for every payment. Coins can then be used to support over 10 different causes – including ocean conservation, education, hunger alleviation and reforestation. Their app also allows you to track your carbon footprint and discover ethical, sustainable brands in a variety of categories, with additional incentives for purchasing from them.
      • Arogya Finance – With a belief that access to healthcare is a basic human right, India-based Arogya Finance has partnered with local hospitals and health-care providers to enable direct access to medical loans for individuals and their families. The health insurance system in India is complicated, with penetration notoriously low – Arogya Finance’s method of providing funds directly to patients means it can lend even to those outside of formal lending structures.
      • MedPut – A unique employee benefit app, MedPut pays for and negotiates unpaid healthcare bills, with repayments made through small, consistent payroll deductions. The deductions are interest-free, with no credit check required, and having MedPut negotiate on the patient’s behalf saves time, stress and ideally even money, with the organization frequently able to negotiate discounts on larger bills.
      • Lynx – With a goal of introducing modern fintech to healthcare, Lynx is an API-connected healthcare payments, banking and e-commerce platform.  With the majority of Americans experiencing climbing deductibles and increasing healthcare costs, Lynx has combined fintech with deep healthcare expertise, enabling healthcare companies to improve affordability, drive health engagement and enhance financial security.
      • M-KOPA Solar – This African company’s innovative pay-as-you-go financing model allows customers to get instant access to life-changing products, while slowly building ownership with a series of flexible micro-payments. One such plan involves solar power – allowing families to reliably power basic necessities like electric lights, refrigerators and charging ports.
      • Babylon Health – Combining AI and innovative technology with human expertise, Babylon Health offers affordable and accessible healthcare to a global audience. With 24/7 access to doctors, nurses, personalized care plans, referrals to specialists and personalized digital health tools through mobile devices, the Babylon app facilitates partnerships with healthcare providers to ensure greater accessibility and ease of payments.
      • ShareTheMeal – An award-winning crowdfunding smartphone app designed to fight global hunger through the UN World Food Programme, ShareTheMeal enables users to make small donations to specific WFP projects and even tracks the progress of the programs. As of this January, the app has 6 million downloads and has contributed to over 130 million meals shared to those in need.
      • Klima – A Germany-based smartphone app that encourages carbon neutrality, Klima helps users offset their carbon footprint in four simple steps: by calculating your carbon footprint; offsetting your footprint by investing in climate projects; seeing your climate achievements in action; and offering personalized tips and checklists to further shrink your climate impact. Users can choose to invest in projects related to reforestation, solar power and providing clean cookstoves to those in need.

      It’s clear there is more work to be done, and quickly, in order to achieve the Sustainable Development Goals and realize the WHOs dream of a healthier planet. We’re excited to see all of the innovative fintechs and other organizations emerging with new and unique ways to get us to a #HealthierTomorrow.

      Interested in innovation to further the goal of a healthier planet?

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      Machine Learning in Banks: The Solution to the Data Scientist Talent Gap

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      Machine Learning in Banks:
      The Solution to the Data Scientist Talent Gap

      In 2023, the shortage of skilled data scientists is still a challenge for financial institutions. According to Indeed.com, the popular recruiting site, searching for “Data Scientist Financial Services” returns 1745 results. McKinsey & Company studied over a dozen banks in Europe that have replaced older statistical-modeling approaches with machine-learning techniques and saw significant improvements in their business metrics.

      The Talent Gap Challenge:

      With the increasing importance of data analytics in banking, the shortage of skilled data scientists is becoming increasingly serious. Tools for collecting, sifting, and sorting data become faster, cheaper, and better, but people with the skills to make use of the results are harder and harder to find.

      Cloud-Based Machine Learning Services:

      Cloud-based machine learning services can help fill the talent gap by opening up opportunities for junior or internal hires to augment risk analytics teams, provide immediate value, and grow into more advanced roles. Machine Learning can train and deploy a credit risk model in about 20 minutes, even by someone with little to no experience.

      Benefits of Machine Learning:

      Machine learning is not just a temporary solution to a talent problem. McKinsey & Company’s study of European banks revealed increases in sales of new products, savings in capital expenditures, increases in cash collections, and declines in churn after replacing older statistical-modeling approaches with machine-learning techniques.

      Automated Risk Decisioning:

      Combining machine learning with automated risk decisioning can prove invaluable to a financial institution’s bottom line. Automated risk decisioning helps make better credit decisions and improves overall portfolio performance.

      Machine learning is the solution to the data scientist talent gap in the banking industry. Cloud-based machine learning services can provide immediate value and help junior or internal hires grow into more advanced roles. The benefits of machine learning are significant and can positively impact a financial institution’s bottom line.

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      Guest blog: How mobile data secure solutions can help prevent fraud throughout the customer journey

      Written by Allison Karavos on . Posted in Blog.

      GUEST BLOG

      How mobile data secure solutions
      can help prevent fraud throughout the customer journey

      • Sarah Small, Global Partner Marketing Manager, Sekura Mobile Intelligence

      It’s clear that the events of the last two years have forced the business world to undertake a digital reality check and left organisations racing to address the challenges created by the rapid and unexpected adoption of online services. This double-edged sword of rapid consumer behaviour change has created the ideal environment for organisations to rethink their business model towards a digital-first strategy, whilst unfortunately also exposing them to digital fraud and online scams in unprecedented volumes.

      Changes to consumer behaviour – a playground for fraud

      Much has been written since the pandemic hit about the impact of Covid 19 and the changes that society has had to make in both personal and working lives. High streets are struggling, major retailers have closed or moved exclusively online, and for a period of time our working and in part social lives became an online activity only.

      But as ever, where the genuine consumers are, the fraudster will follow. With the dramatic shift to online digital transaction has come the inevitable increase in online fraud – social engineering-enabled scams, account takeovers and theft.

      In the first half of last year, criminals stole a total of £753.9 million through fraud, an increase of 30% compared to the first half of 2020. UK Finance

      And so, controls are needed. To create the appropriate level of protection in online channels, these controls must be applied across the customer lifecycle, from the initial account set up and throughout a user’s transactional journey, including verification of a customer’s identity at account application and onboarding, confirmation of ID at account login and by verifying device ownership and authenticating the user during transactions, payments, money transfer and through all sensitive online activities.

      The ultimate goal is to achieve a secure and frictionless online experience for the genuine customer while making it as difficult as possible for bad actors to succeed. Mobile operator data can offer the solution.

      The power of mobile data

      While the world has been going through rapid digital change, global mobile operators have been working to get mobile identity services prepared and optimised in readiness to support the explosion of new services and the use cases that this has created.

      Worldwide, mobile operators are recognising and commercially deploying their digital identity services and resources to provide a unique set of tools and to enable secure customer authentication, user identity verification and account protection solutions. Their entry into this market has been carefully considered (for example to align with data privacy regulations) and now more operators are offering authentication services and other mobile identity tools and are gaining traction in the market.

      Monthly active users of operators MSISDN-based authentication services alone are approaching 1 billion and are estimated to be growing at over 17% a year. Furthermore, several dozen innovative mobile operators are developing their mobile identity services, building on new investment and capabilities in big data, AI, machine learning and APIs to bring more advanced mobile identity products to market.

      How Sekura is utilizing this data

      The Sekura prediction: 2022 will be seen as the year that mobile identity intelligence data became a truly fundamental enabler for the ongoing growth of the global economy.

      Now is the time that the global identity challenges and the increased availability of mobile solutions align to create a market in which mobile data can address real world problems using frictionless, seamless, real time mobile services to simplify customer journeys and prevent bad actors.

      The mobile phone provides unique data in that it is the only available source of ‘dynamic’ data about what is happening right now – in ‘real time’. This capability allows mobile data to be used as a source of valuable signals in the fight against fraud, even in the fast-moving digital world that we live in.

      Sekura have harnessed this ‘dynamic data’ to flag whether a device has been lost or stolen, signal that a SIM card has been recently swapped, that Call Forwarding has been set to divert calls to another device, or that a device has been recycled and is no longer used by the previous owner. The signals that it can reveal are available immediately so that if any of these actions occur, the data can be checked within an online banking or retail flow and used to help prevent fraudulent transactions or transfers before they happen.

      Even better, by using mobile data to keep fraudsters out, it is also possible to easily identify the good guys – you and me – and ensure that, with simple mobile data checks behind the scenes, we are able to access our online services securely, quickly and easily and get on with our lives.

      Ten Companies Using Alternative Data for the Greater Good

      Read the Blog

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