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Top Three Mortgage Lending Trends: How to Make Smarter Credit Decisions Today to Thrive Tomorrow

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Top Three Mortgage Lending Trends:
How to Make Smarter Credit Decisions Today to Thrive Tomorrow

From HELOC to HELOAN, the global mortgage lending market is vast – it reached almost $11.5 billion in 2021 and – despite economic slowdowns – is estimated to grow at a CAGR of 9.5% through 2031, reaching a mammoth size of $27.5 billion. 

However, the last few years have brought the mortgage industry face-to-face with an unprecedented challenge – to digitize core functions almost overnight to tackle record levels of origination and forbearance activities. Many lenders had to expedite tech projects to provide the necessary infrastructure needed to support these new practices and accelerated digital solutions to create better customer experiences and reduce operational costs.

While the industry has found success in adopting new digital solutions, the UK still faces a housing affordability crisis, leaving consumers even more reliant on credit for mortgage originations, refinancing, and regular payments. Though there are attempts to combat the lack of affordable mortgages, like this initiative from Skipton Building Society, rates continue to rise.

Amidst these economic challenges, however, innovation and technological advancements in the industry provide opportunities for companies to adapt and succeed in this challenging environment. From better customer experiences to more accurate credit risk decisions and more financial inclusion, the industry is evolving. 

Discover the top three mortgage lending trends that can help you make smarter credit decisions today to thrive tomorrow.

Trend 1: Increased Use of Automation

Mortgage lending can be tedious for both lenders and applicants at the best of times, due to lengthy, complex processes with multiple stages. While mortgage transactions can take between six to eight weeks to close on average, consumers believe they should take no more than three. That’s why automation is a trend with wind in its sails: decisioning automation can help lenders meet borrower expectations. 

Why it’s popular

Instead of having to wait months for a mortgage, decisioning automation allows lenders to approve customers in a fraction of the time. Even the most complex processes are streamlined, saving time (and brain power) across the board. Customers benefit from approval periods that align with their expectations, while lenders expedite their workload to produce more accurate decisions, faster – freeing up resources to attract and retain customers while boosting sales volume. 

How to use it

While automation may seem intimidating to actually use, finding the right decisioning automation tech is often the biggest hurdle. Take control with flexible technology that offers drag-and-drop UI, letting you configure and reconfigure automations to reflect your changing needs, eliminating reliance on vendors and dev teams. With optimized data and integrated workflows that can layer on top of existing tech and talk to a variety of systems, automated decisioning can be as simple as clicking a few buttons.

Trend 2: Data-Driven Risk Decisioning

Credit risk decisioning is an essential element of mortgage lending, ensuring that lenders are mitigating fraud and default risk and borrowers are getting the right loan terms. For long term loans like mortgages, accuracy is essential to mitigate risk and provide competitive offers to consumers. And an increasing number of mortgage lenders are using data-driven risk decisioning to do both.

Why it’s popular

Mortgage lenders no longer have to accept uncertainty – whether it be in economic conditions or customer behavior. Accessing real-time data ensures more accurate creditworthiness assessment and lower risk for the lender. It can also help businesses grow by providing the insights needed to hyperpersonalize offers for both new and existing customers, improving competitive advantage. On-demand data can also help flag if risk profiles change, allowing lenders to step in long before missed payments or home repossession.

How to use it

The ideal way to harness data-driven risk decisioning for your mortgage lending business is to invest in a data and decisioning ecosystem in which the decisioning engine pulls real-time data on demand from a variety of data sources through a single API. The streamlined, integrated tech stack helps you better understand consumer needs across the entire customer lifecycle. Add in machine learning for evolving customer insights that will eliminate the guessing game and let you make smarter credit risk decisions.

Trend 3: Alternative Credit Scoring Models

Financial inclusion has been gaining traction in the fintech world for years, but recent global economic and political overhauls permanently changed the way we think about access to financial services. Alternative data is a central feature enabling financial inclusion initiatives for lenders across the world. No wonder 65% of credit risk/lending decision makers use alternative credit data on at least half of their credit applications. And that number is only growing, helping lenders accelerate financial inclusion by enabling the creation of alternative credit scoring models, eliminating reliance on traditional credit bureau data alone.

Why it’s popular

Traditional credit scores don’t tell the whole story, especially when it comes to thin or no-file consumers – and 71% of credit providers agree. Alternative data lets lenders access a variety of data that doesn’t come from credit bureaus, including utility payment history, employment data, geographical data, and rent payment history – data that would be especially relevant to establish creditworthiness for a new homebuyer. Mortgage lenders who use alternative data to build alternative credit scoring models can expand their customer bases without increasing risk and support financial inclusion at the same time.

How to use it

In order to build alternative credit scoring models, you need decisioning tech integrated with alternative data. The most powerful data and decisioning platforms simplify the data supply chain, pulling in the relevant data exactly when you need it to ensure more accurate decisions for every application. And don’t compromise on risk – create processes that pull in more alternative data for thin file applicants and less or none for traditionally creditworthy applicants. 

These Trends are Here to Stay

Mortgage lending is often a long, complex process that puts a strain on both lenders and borrowers. The trends we explored today help alleviate that strain, and that’s why they’re here to stay. 

From automation that improves processing speed and customer experience to data-driven risk decisioning that improves risk assessment accuracy and competitive edge through personalized offers to alternative scoring models that help lenders grow their business and accelerate financial inclusion of the under or unbanked, these trends represent the future of the industry.

Want to take these trends and run with them? Make sure your mortgage lending business is ready with our eBook, The Secret to Consumer Lending Success. Download it today!

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The Lending Revolution: Building World-Class Digital Lending Experiences in Southeast Asia

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The Lending Revolution:
Building World-Class Digital Lending Experiences in Southeast Asia

Digital lending has the potential to revolutionize financial inclusion in Asia’s emerging economies. For individuals and small businesses in the region, accessing credit has traditionally been a daunting, time-consuming process, often resulting in high rejection rates and limited options. 

However, with the arrival of digital lending, the process has become faster, more efficient, and more accessible, offering a world of opportunities for those previously excluded from formal financial systems. Digital lending offers faster decision-making, better risk assessment, and more customized product features. By accessing real-time data, lenders can make faster credit decisions, which leads to faster disbursements and better customer experiences. 

Aditya Chintawar is the Chief Product Officer at KoinWorks, an SME-focused neobank that helps customers build credit scores to solve the problem of accessing credit in Indonesia. With a population of over 270 million people that are largely unbanked or underbanked, a report by McKinsey & Company estimates that the economic impact of digitization would be a $150 billion or 10% of GDP growth. By leveraging digital platforms and technologies, lenders can reach a wider customer base, which can help drive economic growth and development. Aditya recently spoke to Provenir’s General Manager for APAC, Bharath Vellore about their experience building world-class digital lending experiences tailored to this market. Check out some of the key takeaways from the discussion. 

Digitization vs. Automation: Understand the Difference and the Path to Follow

Digitization and automation are not the same! Step one to digital lending is effective digitization: the process of transforming analog or manual processes in lending into digital ones. This involves the use of digital technologies such as Optical Character Recognition (OCR), data analytics, machine learning algorithms, and digital platforms to improve operational efficiency, enhance customer experiences, and expand reach to underserved segments of the population. By taking a written statement on a new lender, digitizing it and inserting it into your data lake, you enrich the quality of your models and open the doors for new customers with no previous credit histories. Digitization allows lenders to access data in real time to make faster credit decisions, and provide more customized and personalized products to their customers. Digitization is a key factor in transforming the lending industry and enabling lenders to compete in today’s rapidly evolving market and provide more customized products to their customers.

Once a process has been digitized effectively, it can then be automated. Manual to digital to automation is the path to follow, and it is essential to understand what is being digitized to ensure it is effective. Digitalization unlocks additional data points, making it easier to build better products, perform better risk assessments, and provide better customer experiences. Understanding lending behavior through key data points is critical, and the development of any digital lending product should take this into account. In terms of client experience, the customer response to digitalization has been great, and certain forms of face-to-face interaction can still be maintained, such as voice KYC or video calls.

Balancing Inward and Outward Focus

To digitize effectively and launch new digital products, lenders must balance inward and outward focus. Inward focus requires proper digitalization – adapting operational processes such as underwriting so they can be done by computer systems – in order to reduce friction, make credit underwriting faster, and provide insights into risk assessments. However, properly executed digitization must also happen on the operational level for the availability of services to be possible. A step-by-step approach ensures that each aspect of the process is able to handle the previous load, ultimately ensuring that the availability of service is on-demand, 24×7. Many digital lending products are launched with an outward focus on great front-ends designed for great user experiences. Koinworks operates in a setting where the average smartphone has 4GB of RAM and 64GB of storage. To be relevant to users, the app needs to have a small footprint and be easy to use. The app also offers a dedicated support team to help users with their loan applications and other needs. But if the back-end cannot function up to speed, it will lead to client frustration. Providing ongoing analysis of user behavior can help identify cross-selling opportunities and increase loan limits for existing customers. So, when it comes to inward or outward development focus, it’s an issue of building an agile, end-to-end infrastructure, to strike a balance between the two and launch as quickly as possible. 

The Challenges of Retention in Digital Lending

Retention in digital lending is challenging. Strategies for reducing rejection and anxiety include defining trust and critical parameters with the business to avoid fraud and risk, and maintaining effective communication with the client. Lending is a complicated business, and testing underwriting systems takes time, so running multiple programs on smaller budgets to identify which product works is essential for each type of customer is essential. Additionally, the focus should be on creating a seamless customer experience, reducing friction, and taking into account customers’ digital footprint.

The lack of trust in emerging economies where financial inclusion plays a huge role, has a significant impact on decision-making and strategy. Building trust and infrastructure is essential for the success of digital lending in these markets. Scalability and agility are also important, as they allow lenders to adjust their offerings to meet changing customer needs. Fintechs should focus on agility when building product features to respond to changing market needs quickly. Finally, being open to new ideas and defining trust and infrastructure will help fintechs succeed in a rapidly evolving environment. That’s why digital lending becomes, not just a nice-to-have, but a must-have in order to compete on quality and time-to-market.

“The goal is to create a virtuous cycle. Better data leads to better risk assessment, which leads to better products and experiences. All of which, in turn, lead to better data.”

– Aditya Chintawar, Chief Product Officer at KoinWorks

Digital lending is transforming the lending industry in Southeast Asia and around the world. By leveraging digital technologies and data, lenders can improve their operational efficiency, enhance customer experiences, and expand their reach to underserved segments of the population. However, to succeed in these markets, lenders must balance inward and outward focus, understand the difference between digitalization and automation, address challenges related to retention, and build trust and infrastructure.

Watch the full fireside chat with Bharath and Aditya to learn more.

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Shaking Up Consumer Lending in the UK

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10 Fintechs Shaking Up
Consumer Lending in the UK

Looking at the UK landscape and 10 innovative global fintechs

With the ever-evolving landscape of financial technology, consumer lending has never been more accessible and efficient – in large part, due to fintech innovation. With a global consumer credit market size of $110 billion (in the UK, consumer lending reached reached over 28 billion British pounds in January 2023, which is a dramatic recovery from early in 2020), rapidly growing middle classes in emerging markets, and economic uncertainty affecting us all, the opportunity for lenders to tap into the consumer need for credit is immense.  

As predicted, the UK economy in particular is adapting as many experts feel a major recession has been avoided, and as a result, banks are expected to increase their lending this year. “Total loans in the UK are expected to rise 1.2% this year… with falling inflation, lower-than-anticipated energy bills and a resilient job market” contributing to an increase in the UK GDP, “driving an increase in consumer and business borrowing.”

When it comes to consumer lending specifically, fintechs are answering the call for increased borrowing demands and are looking to disrupt the traditional. No credit score? No problem. Worried about missing payments? You’re covered. From a company supporting gig workers around the world to a credit card for foodies, these ten global fintechs are shaking up auto lending, BNPL, credit cards, mortgages, and retail/POS. 

Auto Lending

Lendbuzz – USA

If you’re new to credit, it can be difficult to get approved for auto financing. Lendbuzz is here to change that. The fintech proves a simple and fast application process that assesses creditworthiness with data beyond just your credit score. Working directly with auto dealerships, Lendbuzz offers personalized loans and instant decisions, taking you through the process from start to finish.

Moove – EMEA and India

Founded in Nigeria in 2020, Moove is a global startup that aims to democratize access to vehicle ownership for “mobility entrepreneurs” across Africa, the Middle East, Europe, and India. Tackling the high barrier to vehicle financing that millions face, especially in emerging markets, Moove uses a revenue-based financing model to offer car loans that drivers then pay off through their ridesharing app. 

Buy Now, Pay Later

ShopBack (formerly Hoolah) – Southeast Asia and Australia

Singapore-born ShopBack is a fintech that provides improved shopping experiences to consumers and broader reach and shopper engagement to brands and retailers. Operating across APAC, their integrated BNPL service allows you to pay off purchases in installments of three, which can be combined with features such as cashback and prepaid retail vouchers. ShopBack hopes to make shopping “more rewarding, delightful, and accessible.”

Nelo – Mexico

If you want to buy now, pay later at Mexico’s top merchants, you want to download Nelo’s top-rated app – it’s the first of its kind in the region, enabling shoppers to pay in installments with a virtual card generated at checkout. And through the company’s partnership with Mastercard, you can use it at any online merchant. You can also use it to finance everyday expenses like utilities and other bills, a mark of BNPL innovation and a sign of how the segment is likely to evolve.

Credit Card

Cred.ai, USA

Cred.ai is an AI-powered credit card designed to help users build credit while mitigating missed payments. The fintech sets up automated spending limits, helping you spend within your means, and their proprietary underwriting model means you don’t need a FICO score to apply. The card itself is metal, unicorn-themed, and free for approved applicants. It works best with their digital banking product and comes with features like an early paycheck (called flux capacitor) and digital “self-destruct” cards called stealthcards. 

Yonder, London

A rewards credit card “great for expats and immigrants,” Yonder is a rewards credit card that boasts no foreign exchange fees, worldwide travel insurance, and you can apply without a UK credit score. Leveraging open banking technology, the credit card is able to focus on financial inclusion while rewarding users for the experiences that enrich their lives, whether it’s travel or dining at Yonder’s curated restaurant partners around London.

Mortgage

Hypofriend, Germany

Hypofriend was founded to simplify and personalize the process of getting a mortgage for Germans. They use advanced technology to analyze your optimal finance strategy while predicting bank decisions in order to connect you to a personalized mortgage offer from a lender that fits your needs. The Hypofriend team is also there to advise from start to finish, demystifying the complex process and providing transparency to support more financial literacy and understanding.

HomeCrowd, Malaysia

Focused on helping Millennials in Malaysia achieve the dream of owning a home, HomeCrowd uses holistic, data-driven credit scoring to match mortgage applicants with peer-to-peer (P2P) lenders on a blockchain-powered, Web3 platform. The company is the first in the country to be licensed and regulated for P2P lending specifically for mortgages and consumer financing by the government. 

Retail/Point-of-Sale (POS)

Blnk, Egypt

Did you know that less than 4% of Egyptians have access to credit cards? The majority of Egyptians must rely on savings or finance purchases with high-interest loans. Blnk is here to change that – they enable any consumer to receive instant credit at the point-of-sale. Their current network of merchants includes over 300 businesses and the fintech has already disbursed over $20 million in loans. 

Acima, USA

US-based Acima offers consumers lease-to-own solutions as an alternative to traditional retail financing. You don’t need credit to apply and your credit score isn’t affected. Simply lease the furniture, electronics, or any other item you want to purchase and “rent” it until the cost of the item is covered, or pay early at a discounted rate. If you no longer want the item, just return it! Acima enables online and in-store shopping and offers flexible payment terms.   

Unlocking Consumer Lending Innovation

As access to consumer credit increases around the world, both fintechs and traditional financial service providers will need to leverage the right technology to provide it. The ten fintechs you just read about have found their innovative idea to disrupt consumer lending – what will yours be?

No matter the idea or use case, you need a technology partner that thinks like you. Future-proof your consumer lending strategy and launch new products with a data and decisioning ecosystem that manages risk, so you can focus on what matters most: serving your customers in new, disruptive ways. 

Read the eBook, The Secret to Consumer Lending Success, to discover how you can overcome any lending challenge with a robust credit risk decisioning platform that grants access to both alternative and traditional data sources through a single API.

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5 Ways Credit Risk Analytics Can Help Your Business Make Better Decisions

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5 Ways Credit Risk Analytics
Can Help Your Business Make Better Decisions

In today’s rapidly changing business environment, companies need to make informed decisions to stay competitive. One way to achieve this is by leveraging credit risk analytics. By analyzing data related to credit risk, businesses can gain valuable insights into their customers’ financial behavior and make better decisions based on that data. In this blog post, we’ll explore five ways credit risk analytics can help your business make better decisions.

Better Understand Your Customers

  • Credit risk analytics can help you better understand your customers’ creditworthiness, payment history, and overall financial behavior.
  • This information can help you make more informed decisions when it comes to extending credit or setting credit limits.
  • Use credit risk analytics to segment your customers based on their credit risk profile, allowing you to tailor your offerings and pricing to meet their specific needs.

Mitigate Risk

  • Credit risk analytics can help you identify potential risks before they become major issues.
  • By analyzing data related to credit risk, you can identify customers who are more likely to default on payments or who have a history of late payments, helping you mitigate risk and avoid potential losses.
  • Use credit risk analytics to monitor your customer portfolios and identify trends or patterns that could indicate future risks across the entire customer lifecycle.

Optimize Pricing

  • By analyzing credit risk data, you can optimize your pricing strategies.
  • Identify customers who are more likely to default on payments and adjust your pricing accordingly to mitigate the risk.
  • Use credit risk analytics to determine the optimal pricing and loan terms for each customer segment, based on their unique credit risk profile.

Improve Collections

  • Credit risk analytics can help you improve your collections process – reducing collection costs and improving your cash flow.
  • By analyzing data related to credit risk, you can identify customers who are at risk of defaulting on payments and take proactive measures to collect payments before they become overdue.

Enhance Customer Experience

  • Credit risk analytics can help you enhance the overall customer experience.
  • With a better understanding of your customers’ financial behavior, you can tailor your products and services to meet their specific needs and preferences.
  • Use credit risk analytics to identify customers who are most likely to be interested in a particular product or service, and target your marketing efforts accordingly.
  • You can also personalize your customer interactions and offer customized solutions based on each customer’s unique credit risk profile.

By leveraging credit risk analytics, you can gain valuable insights into your customers’ financial behavior and make more informed decisions. Whether it’s optimizing pricing, mitigating risk, or improving collections, credit risk analytics can help you achieve your growth goals and stay competitive in today’s dynamic business environment. With the right credit risk analytics tools and strategies in place, your business can stay ahead of the curve and make the best decisions possible.

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Unlocking the Power of Credit Cards: Three Innovative Trends Driving Change in the Industry

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Unlocking the Power of Credit Cards:
Three Innovative Trends Driving Change in the Industry

The credit card industry has come a long way since the first credit card was introduced in the 1950s. Today, bankcard balances have reached a new record of $930 bn in the US alone, and the industry as a whole is growing rapidly across the globe. With rapid growth comes stiff competition, so issuers are constantly looking for ways to attract new customers and retain existing ones.

With economic uncertainty shaping financial services worldwide, flexibility and creative thinking have become necessary for issuers and other lenders trying to reduce risk and default as much as possible. Traditional approaches include adjusting lending criteria, reducing credit limits, and increasing interest rates to protect against potential losses. However, forward-thinking lenders have chosen to embrace macro trends like digitization to provide better customer service and financial inclusion to expand the addressable market. 

More specifically, trends like alternative credit cards, offering BNPL functionality, and hyper-personalization are not only strengthening lenders’ risk positions and growing their business, but are changing the way we use and think about what a credit card can be. 

Explore the three innovative trends driving change in the credit card industry:

Trend 1: “Alternative” Credit Cards

Alternative credit cards are credit cards designed for new-to-credit (NTC) consumers with thin credit files or no credit history, including college students, immigrants, and low-income earners to help build credit and improve creditworthiness. Unlike traditional credit cards, these do not require hard credit pulls and often come hand-in-hand with features like financial literacy education, auto-payments and rewards to help users build credit while promoting responsible card usage. Alternative credit cards could be a great entry point for the 1.4 billion adults who are unbanked across the globe.

Why they’re popular:

Alternative credit cards offer consumer lenders an opportunity to attract new users who wouldn’t typically qualify for traditional credit cards due to a thin credit file, no credit file, or low credit score. By offering cards that teach users how to build credit, with low spending limits and auto-payments, lenders can help consumers establish good credit and build a relationship with them for future financial products.

How to offer them:

Perhaps not surprisingly, alternative data is the key to alternative credit cards. Alternative data refers to all the financial information that is not included in a credit report, like utility bills, rent payments, employment history, and sometimes even social media. Since these cards are built for consumers without traditional bureau data, lenders can create their own credit scoring models by integrating a wealth of alternative data into their decisioning engines. If you’ve got alternative data, you’ve got the foundation for alternative credit cards.

Trend 2: BNPL Payments

Buy Now Pay Later (BNPL) functionality is becoming increasingly popular in the credit card industry as credit card lenders look to carve out space in their direct competitors’ $19.5 billion market. Adding BNPL-style payments to credit cards allows consumers to spread out the cost of their purchases over time, typically with a short-term, interest-free period between three and six months. It also allows credit card lenders to take back some of the business they’ve lost to BNPL players.

Why it’s popular:

BNPL functionality is becoming popular because it offers increased flexibility and accessibility to consumers while adding additional revenue streams and competitive advantage for credit card lenders. Consumers get more time to pay off larger purchases and don’t have to create an entirely new BNPL account or download yet another app to benefit. An added bonus is built-in rewards they already receive with their credit card. Lenders can enjoy boosted revenue from BNPL interest, but the major draw is the potential to attract new customers and retain existing ones – if your credit card already offers BNPL, there’s no need to make purchases with another financial service provider.

How to offer it:

To use BNPL functionality effectively, issuers need to increase the flexibility of their platform. This can be achieved through the use of advanced decisioning capabilities, which can identify users who are most likely to use BNPL functionality, determine personalized offers, and even monitor behavior to optimize plans based on payment activity – all while reflecting your risk appetite. 

Trend 3: Hyper-Personalization

80% of consumers want credit card offers tailored to their needs – personalization is no longer a bonus, but a basic requirement. From onboarding through the entire customer lifecycle, hyper-personalized credit cards are meeting customers where they are and supporting them on their financial journeys. As economic conditions vary, these cards – which are powered by machine learning and advanced decisioning and analytics – can help ensure consumers can still pay off their cards and credit card lenders can maintain low risk portfolios.

Why it’s popular:

Hyper-personalized cards are attractive to new users, looking for tailored benefits and rewards built specifically for their spending patterns, behaviors, and even lifestyle. A hyper-personalized credit card may offer rewards for specific categories of spending that the user frequently engages in, such as travel, dining, or online shopping. The card may also offer exclusive benefits such as discounts, cashback, or concierge services, which all drives customer loyalty. The data-driven approach behind hyper-personalized credit cards can also help users to better understand their spending habits and make more informed financial decisions, while helping lenders gain insights into their customers’ spending habits and preferences, reduce risk and minimize losses from delinquent accounts, and ultimately identify potential risk factors and take proactive measures to prevent defaults.

How to offer it:

Hyper-personalized credit cards run on data, decisioning and machine learning technology to provide advanced analytics. This technology enables lenders to gather and analyze vast amounts of alternative and traditional data to gain a deeper understanding of a borrower’s financial profile and build a credit card experience just for them. Decisioning technology can also be used to automate the credit card application and approval process, allowing lenders to quickly assess a borrower’s creditworthiness and make personalized credit offers in real-time.

Go From Trending to Thriving

The credit card industry is evolving rapidly, and these three trends represent just a few of the innovative changes taking place. Alternative credit cards, BNPL functionality, and hyper-personalization are reshaping the way consumers access and use credit while helping contribute to the growth of the global industry. 

By embracing these trends, credit card lenders can reach new, creditworthy thin file or no file users, compete with rising BNPL players, and meet the exact needs of current customers. However, companies must leverage advanced analytics and alternative data sources to get the most out of these new features. As the industry continues to evolve, it’s clear that credit cards will remain an essential financial tool for consumers, and those who embrace innovation will be best positioned to thrive.

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When Were Credit Scores Invented

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When were credit scores invented and how does credit scoring work?

The History of Credit Scores

Credit scores and reports are essential components of financial services products. But do you know when credit scores were invented and how consumer credit reporting works? In this guide, we provide all the information you need to know about credit scores, including their history, and how they impact your financial life. Keep reading to learn more.

Credit Scores and Credit Bureaus: An Origin Story

Credit scores as we know them today have only been around for a few decades. However, credit reporting itself began early in the 19th century, as commercial lenders attempted to ‘score’ potential business customers to determine the risk in providing credit to them. The very first credit reporting agencies (what we know now as companies like TransUnion and Equifax), began as local merchant associations. They simply collected various financial and identification information about potential borrowers and then sold it to lenders – but these were focused strictly on commercial/business loans at the start, offered to organizations that needed funding to launch or grow their operations. The earliest credit reporting agencies in the United States were R.G. Dun & Co and the Bradstreet Company (sidenote: sound familiar? The two companies merged in 1933 and rebranded as Dun & Bradstreet Inc. in 1939), which developed an alphanumeric scoring method to determine the risk factors associated with commercial loan applications.

In the early 20th century, modern credit bureaus were formed, looking more closely like we know them today. Taking a page out of the commercial-loans book, retailers began offering consumer credit to individuals. These retailers all had individual credit managers, tasked with determining creditworthiness of applicants. In 1912, they decided to band together and formed a national association to “develop a standard method for collecting, sharing and codifying information on retail debtors.”

In subsequent years, the three major credit bureaus in the U.S. were born – today known as Equifax, TransUnion, and Experian. Through the 70s and 80s they worked together to develop consistencies in credit reporting methods and pushed for an unbiased, more automated way of determining credit scores.

Credit Score Vs. Credit Report
But what IS a credit score? And how is it calculated? And what’s the difference between a credit score and a credit report?

A credit report comes first. A detailed historical record of your financial transactions and financial status, a credit report includes everything from identifying personal information (name, address, date of birth), to consumer credit accounts (credit cards, lines of credit, auto loans, mortgages), and ‘inquiry’ information (i.e., information on the companies who have pulled your credit report to make you offers of new credit products, or pre-approvals for upsells, etc.). A credit score is then calculated based on that information. Typically a three digit number (we’ll get into regional differences later), this credit score quickly tells potential lenders how creditworthy you are. In North America, the higher the score, the lower risk you are and therefore, a more worthy applicant.

Traditional credit scoring systems are not without fault, however. They often don’t take into consideration additional factors that can influence your credit risk level (i.e., most modern credit reports don’t include rental payments, which can be a very accurate predictor of someone’s propensity to pay back debt.) And there can be a significant lag between an applicant’s activities and pulling a credit report/score – real-time data is much more valuable (and accurate) in assessing an individual’s risk.

So how do credit scores really work? A mathematical formula based on the information found in your detailed credit report, a credit score allows potential lenders to instantly assess how creditworthy you are. A higher credit score indicates that a) you are more likely to pay off your debt/repay any credit provided and b) pay off that debt both on time, and according to the agreed-upon terms. With a more favorable credit score, you are more likely to have lenders extend you credit products, such as new credit cards, auto loans, mortgages, and consumer loans. Beyond that, the higher your credit score, the more likely it is that lenders will offer you better terms, including flexible repayment schedules and lower interest rates. If you are stuck carrying a low credit score, you run the risk of not being able to access credit when you need it or having to accept higher interest rates.

Calculating Your Credit Score

A FICO score (Fair, Issac and Company) is one of the most well-known credit scores in the US. In fact, “FICO scores are used by 90% of the top US lending institutions for their risk assessment needs.” These three-digit scores, which first began in 1989, are calculated based on the information found in your credit report from one of the three major credit bureaus. There are five main factors that FICO uses to calculate your credit score, with different categories carrying different weights. (Sidenote: other credit scores are calculated much the same way but may have different weights associated with the main contributing factors.)

For FICO scores, the factors are:

  • Payment history (35%)
  • Balances owed/credit usage (30%)
  • Length of credit history/age of accounts (15%)
  • Credit mix (10%)
  • Recent credit activity and new accounts/new credit inquiries (10%)
Credit Scoring Around the World

Despite the overwhelming prominence of the United States’ three main credit bureaus, there are regional differences in credit scoring models and the use of credit scores. While each region uses the same basic premise of evaluating an individual’s credit history to determine their creditworthiness, there are variations in how that credit scoring is executed. The main variations in credit scoring methods relate to:

  • How long certain information stays on your credit report
  • Who can contribute information to your credit report
  • How many credit bureaus exist in a particular country/region
  • Whether those credit bureaus are for-profit or not-for-profit (and who owns them)
  • Whether lenders are required to use your credit report and/or credit score to determine your risk level
Here’s a handful of examples of the ways various regions handle credit scoring:
  • United States – Lenders report details of your financial situation, including credit and historical transactions, to one of the three major credit bureaus (Equifax, Experian and TransUnion) – who then either generate a credit score or provide the credit reports to a credit scoring company like FICO, which then calculates a FICO score.
  • Canada – Canada is similar to the U.S, but doesn’t use Experian as a credit bureau, and its credit scores upper limit is 900 vs 850.
  • United Kingdom – The U.K. has three major credit agencies – Equifax, Experian and Callcredit (Noddle), but each organization calculates credit scores differently.
  • France – There are no official credit reporting agencies in France; instead, credit scores are built on a bank-by-bank basis but aren’t transferable to other lending institutions.
  • Netherlands – The Netherlands has a single credit bureau, Krediet Registratie (BKR), which unpaid debts are reported to.
  • Germany – The main credit agency, SCHUFA, is a private company that tracks accounts, unpaid debts, loans, and any delinquencies. Your SCHUFA score goes down (which is positive) as you gain financial history and pay down debts.
  • Australia – Australia has four main credit bureaus (Equifax, Dun and Bradstreet, Experian, and the Tasmanian Collection Service).
  • India – India utilizes one official credit reporting agency, Credit Bureau Information India (CIBIL), which is a partner of TransUnion.
  • Japan – There is no official credit scoring system in Japan, and creditworthiness is simply determined by individual lenders, making it extremely difficult to get credit if you are a foreigner.
How does credit scoring affect consumer lending?

A credit score that is rated as ‘good’ or ‘excellent’ will save most people thousands of dollars over the course of their lifetime. If you have excellent credit, you get better rates and payment terms on everything from mortgages and auto loans to credit cards and lines of credit – essentially anything that requires any sort of financing. If you have a better credit rating, you are seen as a lower-risk borrower, with more banks and lenders readily competing for your business by offering better rates, fees, and perks. On the flipside, those with poor credit ratings are seen as higher-risk borrowers, and may either have less favorable lending terms (higher interest rates in particular), or be unable to access credit at all when they need. Apart from just accessing lending products, those with poor credit scores may find it difficult to find rental housing, rent a car or even obtain life insurance.

Lenders use credit scores as part of their risk decisioning process to determine the creditworthiness of a potential individual or business customer. So, the ripple effect of either a positive or negative credit score is significant – and it can last an incredibly long time, particularly if there are delinquencies or defaults noted on your credit report.

However, part of the issue with this is that credit scoring can often have inherent biases. This greatly impacts various demographics from fairly accessing credit. For example, immigrant communities may not have formal credit histories. No credit history = low credit score. Low credit score means they can’t easily access lending products and therefore can’t start building a credit report/score. Or they are forced to accept suboptimal terms with exorbitantly high interest rates and may have difficultly paying down that debt as a result. Which of course, is a mark against you on your credit report.

Alternative Data for Financial Inclusion

The example above is not uncommon in our global society – there are countless immigrant populations in countries all over the world, and millions more who have no access to formal financial services products. There are many terms for those who lack a traditional credit history – thin-filed, credit invisible, unbanked, underbanked – but it essentially refers to anyone who doesn’t have information in their official credit history/report to generate a credit score. This includes an estimated 62 million Americans, 200 million people in Latin America and 3.6 million in Asia having no access to formal credit. One-third of all adults globally (up to 1.7 billion people) lack any type of bank account.

How can lenders ensure equal access to credit, even for those without formal credit histories, without sacrificing their risk strategy? One way is to use alternative data. Alternative data includes anything outside of a traditional credit report that may indicate creditworthiness, including telco information, rent and utilities payment info, social media and web presence, travel data and open banking info.

Because this type of data is often missing from traditional credit reports (and thus the formulation of credit scores), they can be inherently biased towards certain minority demographics. The data that FICO scores consider (like payment history, length of credit history, etc.) is also often influenced by generational wealth and the passing of large assets like homeownership (i.e., mortgage data counts towards your credit score, rental payment usually does not). “The Black homeownership rate was 44% at the end of 2020 compared to the 74.5% rate for non-Hispanic white consumers. Since credit scoring models look at homeowners’ housing payments and ignore renters’ rental payment history, Black consumers are at another disadvantage, despite both types of payments falling under the same category of “housing.” Ensuring that lenders are supplementing traditional credit scores with alternative data helps to overcome that bias and ensures financial inclusion.

Using alternative data helps to provide a more holistic view of the financial health (both current and future potential) of customers, improves decisioning accuracy and even helps increase fraud protection with improved identity verification and KYC onboarding processes. Enabling more accurate credit decisioning allows lenders to expand their market safely, without increasing risk, and helps to encourage access to all unbanked/thin-filed individuals, setting people on the path to safely building their credit scores. Eighty-seven percent of lenders using alternative data are using it to more accurately evaluate thin/no-file customers and 64% improve their risk assessment among unbanked consumers.

Apart from individual lenders looking to alternative data sources, some credit bureaus are now offering ways to boost credit scores for thin-filed consumers:

  • Experian Boost – collects financial information that isn’t normally found in your credit report (i.e., utility payments and banking history) and includes that in the calculation of your Experian FICO score.
  • UltraFICO – free program that utilizes historical banking information to build your FICO score, looking at factors like paying bills on time, avoiding overdraft, and having savings.
  • Rental info reporting – new services that track rental payments and report that info to credit bureaus on your behalf.
How to improve your credit score
If you are struggling with a less than ideal credit score, don’t fret. There are steps you can take to improve your score over time:
  • Pay your bills on time, every time. This includes everything from mortgage payments and car loans to credit cards, utility bills and cell phone plans.
  • Reduce your overall credit utilization. Credit scores look at your credit utilization (the portion of your available credit that you use at any given time). After payment history, credit utilization is the second more important factor when calculating your credit score. Aim for 30% credit utilization or less to keep your credit score favorable and try to pay off credit card balances in full each month. (Bonus tip for a quick win – ask your credit card issuers to increase your limit slightly so your debt ratio goes down.)
  • Don’t apply for too much credit. New credit requests start with a ‘hard inquiry’ (hard inquiries include applications for new credit cards, mortgages, auto loans – too many of them can increase your credit score). Revolving credit (regularly closing old accounts and opening new ones) also has a negative impact on your credit score. Additionally, credit scores look at how long you’ve had your credit accounts – keep your old accounts open and old credit cards active but be sure to deal with any collections or delinquent accounts. If you have a lot of outstanding debt over various types of accounts, consider consolidating your loans, which results in one repayment, and possibly a lower interest rate to boot.
  • Sign up for credit monitoring services. These services can alert you to fraudulent behavior on your profile, help you keep up to date on your credit score, and often offer special tips on how to improve your credit score.

It’s clear that credit reports and credit scores have a significant impact on your ability to access credit. But as the financial services industry evolves, there are more and more innovative ways to determine creditworthiness, including the integration of alternative data, implementation of advanced decisioning solutions, and using more accurate, predictive models with artificial intelligence. And there are now more varied opportunities to access credit and financial services products, including the advancement of buy now, pay later (BNPL) solutions, and neobanks and fintechs who are taking a fresh approach to credit products.

If you’re a lender, how can you ensure that the history of credit scoring continues to evolve into something more holistic, more accurate, and more inclusive? Discover how a unified decisioning platform and easy access to a variety of data sources can help you say yes to more people, without increasing your risk.

Get the eBook

Further Reading:

15 Companies Changing the Landscape of BNPL

The Long, Twisted History of Your Credit Score

– Time Magazine

A History of Credit Scores

– point.app

The Fair Credit Reporting Act (FCRA)

– Investopedia

Learn more about how to improve decisioning accuracy and encourage financial inclusion with alternative data

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The Ultimate Guide to Credit Risk Analytics: Benefits and Pitfalls of Microservices

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The Ultimate Guide to Credit Risk Analytics:
Benefits and Pitfalls of Microservices

What is Credit Risk Analytics?

Credit risk analytics refers to the process of assessing the probability of default by borrowers and measuring the potential losses that lenders may incur due to credit defaults. This involves analyzing various factors such as the borrower’s credit history, financial health, and other relevant metrics to determine their creditworthiness.

Financial institutions rely heavily on credit risk analytics to make informed lending decisions and manage their credit risk exposure. By analyzing credit risk, financial institutions can identify potential losses and take proactive measures to minimize them. This can include measures such as setting appropriate interest rates, requiring collateral or guarantees, and establishing risk management policies and procedures.

However, relying solely on credit risk analytics without appropriate risk management solutions can have severe consequences. For example, if a lender fails to identify and mitigate potential credit risks, it could lead to significant losses, which could ultimately impact the lender’s financial stability and reputation. Therefore, it is essential for financial institutions to have robust risk management policies and procedures in place to manage potential credit risks effectively.

How is Credit risk management software used for risk analytics?

Credit risk management software is a specialized tool used by financial institutions to assess and manage the risk of default by borrowers. It leverages advanced analytics and modeling techniques to provide a comprehensive assessment of the creditworthiness of a borrower, helping lenders make informed decisions regarding loan approvals, interest rates, and other terms and conditions. Credit risk management software can also monitor and analyze credit portfolios, providing ongoing risk assessments and alerts to potential issues. By automating much of the credit risk management process, this software empowers financial institutions to improve the accuracy and efficiency of their risk management practices, ultimately leading to better business outcomes.

What does this mean for programmers?

As we speak, thousands of programmers across the globe are frantically fielding error messages and digging through millions of lines of code to prop up whatever development misstep threatens potential risks of a data breach that could annihilate their respective universe at any given time. Phew. Every so often, a new concept enters the fray, a prophet peddling the hope of a better future. More recently, this future comes in the form of a microservices architecture – the fulfillment of Service Oriented Architecture’s (SOA) loosely coupled promise.

To give you a deeper understanding of what this means for programmers, we created this ultimate risk analytics guide filled with valuable insights to aid in your risk assessment process. As an organization that gets input from a variety of risk management professionals and multiple sources, we see the benefits of using microservices in financial institutions, the positives and pitfalls of implementing them for the long-haul, and the differences between them and the traditional, monolithic approach to development.

Microservices: Risk analytics solutions

When assessing risk and development blunders, programmers everywhere say, “Everything is breaking, always.” However, this is not a catastrophe; this is the fragile reality of software development – every enterprise is a house of cards mortared with sticky notes and energy drinks.

On occasion, brand new concepts will crop up and give said programmers the potential to alleviate key risk indicators. The newest concept in question is the use of microservices. Together, we’ll explore their role in managing risk and increasing business performance for software developers globally.

So, if you’re thinking about making the move to microservices, keep reading.

3 Benefits of Microservices in Risk Management

A good place to start is to understand three high-level benefits that have propelled the adoption of microservices and the role they play in managing risk analytics solutions.

  • Microservices are Agile

    Let me paint this as a story. A hypothetical Chief Risk Officer would have their team expose a scorecard as a service as part of the credit underwriting process. The Chief Operations Officer is in charge of that process. In context of the exposure of the scorecard, everyone agrees that if the underwriting process passes seven variables to the scorecard service, the service will return as a score.

    As long as I don’t violate the contract – give me seven data points, and I’ll return a score – it doesn’t matter to the underwriting process how the score is calculated. If the risk management team discovers new data analytics data sources they can leverage, or if a new scoring model is created, they are free to implement; that change will not negatively impact the underwriting process. This level of agility means risk professionals can quickly adapt to a changing risk factors landscape.

  • Microservices are Resilient

    You have heard that because a microservice is autonomous and loosely coupled, the failure of one service tends to happen in isolation of the rest of the system. In the example above, as long as the service that is exposed adheres to the original contract, the processes that rely on the service will not break. Both sides of the contract – give me seven variables and I will give you a score – are able to meet terms in the contract best. The underwriting process can retrieve the variables any way deemed best, and the scorecard service can calculate the score as deemed best. As long as the contract is honored, neither is impacted.

  • Microservices are Open

    At this point, most microservices are designed to leverage REST as the mechanism for data exchange. REST has shown itself to be secure, lightweight, and flexible. This open nature represents enormous potential in the creation of end-to-end processes to meet operational needs of the enterprise.

    Now that you’ve got a feel for why microservices could mean a better future for software developers, it’s essential for risk managers to learn the advantages and disadvantages of using them for the long haul.

Risk Analytics in a Microservices Architecture: The positives and pitfalls of microservices in the long-term

The agile, resilient, and open nature of a microservices architecture are all significant benefits you get at first sight, but nothing is perfect. What about the long haul?

This Q&A goes further into the implementation of microservices, and some of the long-term positives and pitfalls.

  • How have microservices changed application development?

    The vision to create a loosely-coupled enterprise environment has been a Holy Grail for some time. While the same theories and techniques showed promise with XML and SOAP-based web services, the implementation of microservices better supports an agile approach to development. The decomposition of monolithic end-to-end processes gives product and process designers and developers the flexibility to create solutions that may be better fit for purpose. It enables these professionals to define more discrete capabilities, allowing developers to develop discrete functions – a more appropriate solution to the business problem they must solve.

  • What are the most common risk issues you see affecting the implementation of microservices?

    Microservices are yet another operational and developmental paradigm shift. These shifts always present challenges to implementation. The architectural maturity of an organization is often the most significant hindrance to adoption and implementation. If an organization is not in a place to facilitate the exposure of microservices, for example, due to legacy systems not supporting open messaging, it will hinder implementation.

  • Do you have any concerns regarding the current state of microservices?

    My biggest concern regarding the state of microservices is the possibility that an organization may not adequately secure its endpoints. Due to the lightweight nature of microservices, it is not a prescriptive technology. By contrast, SOAP is governed by a standards body that ensures prescriptive security recommendations are provided. Microservices are not governed, so the potential roll-out is very “wild west.”

  • What kind of security techniques and tools do you find most effective for securing microservices?

    The efficacy of security techniques and tools depend on the environment into which the microservice is deployed, but let’s take a general perspective. Microservices do not lend themselves to the “traditional” mode of security because components are not conjoined, therefore they do not share access to a common data repository (think identity control). To avoid making calls to an authentication service in every instance, using OAuth (Open Authorization) as a delegation protocol can simultaneously ensure the security and agility of the system.  

  • What do developers need to keep in mind when working on microservices?

    When working on microservices, developers must be simple and discrete. A service should not be complicated. It should solve one singular problem. It should be as simple as: Give me seven data points, and I will give you a score. Nothing more.

  • What’s the future for microservices – where do the greatest opportunities lie?

    One of the greatest opportunities in microservices lies in the potential for reuse. For example, many organizations require the ability to quickly reference employee information to match skill level to a given task. Instead of writing the code to look-up required information every time it is used in a process, the organization could write an employee look-up service to be reused by any process that needs the information.

  • Which programming languages, frameworks, and tools does Provenir use to enable the creation of microservices?

    Provenir implements a development technique called graph theory, rather than implementing a language like Java or Scala. Graphs are designed and developed using Provenir’s Studio and deployed to our Decision Engine. As part of the development, users can expose REST-based endpoints. These endpoints enable decisions, analytics, processes, etc., to be exposed as microservices. We also provide tools that enable the testing and documentation of the exposed services.

    To gain a better understanding of the concept as a whole, it’s important to nail the basics. In the final part of this risk analytics guide, we deep-dive into the defining differences of microservices and the traditional monolith and how they contribute to risk management strategies.

Microservices vs. Monolith

Unless you’ve been living under a rock without wi-fi, in which case I would question your ability to read this article, you’ve likely heard the concept of microservices compared with a monolithic architectural style. Comparison with the monolith is a great way to explain the characteristics of a microservices style because the two architectural concepts exist in stark contrast: large and interwoven, small and discrete.

For this section of the guide, we’ll contrast microservices with the monolithic approach to development to gain a baseline understanding of the concept.

  • Microservices

    Part of an architectural concept where the focus is on discrete services that do one thing and do that one thing very well. In the risk decisioning context an analytics group within an organization might be responsible for developing and exposing scorecards as microservices. 

    The data scientist, or analysts, would focus on developing really good scorecards and making sure these scorecards continuously deliver quality results. They would then expose these scorecards as discrete services that could be called upon to deliver excellent and accurate scores. An operations group could then develop applications or processes that call out to these scorecards at the right time, leveraging these scores in a decision process.

  • The Monolith

    Most of the time, business processes are designed to be an end-to-end process. That’s what we call a monolithic architecture. All parts of the decision process are developed as one, large complex process. Let’s consider scorecards again, as an example. If you want to make a change to a scorecard there may be a great deal of coordination, refactoring or redevelopment of the process, then testing before rolling out again.

  • What’s Next?

    Now that we’ve gone in dept on microservices… what’s next? Where is the industry headed? The use of microservices in financial technology can simplify how you turn your scorecards, risk models and other analytics components into services for use in a loan origination and decisioning processes. Simple right? But don’t forget that having the foundation of the right scorecards, data and risk models is critical. And then if you want to implement advanced analytics like AI/ML, you may be looking at additional challenges, despite the vast improvements it offers across the modeling lifecycle.

    For more information on how to implement advanced AI algorithms (and maybe inform even more powerful microservices?), continue reading here.

Need to balance your credit risk analytics and management with speed and business growth?

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Myths vs reality in upgrading your credit decisioning technology

Myths vs Reality in Upgrading Your Credit Decisioning Technology

Powering Up: How Banks Can Leverage Automated Credit Risk Decisioning for More Agility and Speed

Financial institutions are under pressure, and banks are feeling the heat. Consumers are even more resistant to friction in their customer experience journeys, whether they are buying appliances, vacations, vehicles, or applying for credit. So how can banks focus on growth and meeting consumer needs and expectations, while still managing risk effectively? In many cases, it means it’s time to look at your data and decisioning technology. 

Upgrading your credit risk decisioning technology sounds daunting. But we’re here to talk about some of the myths that persist around upgrading your tech – and the reality counterpoint. 

Myth #1:
Traditional Credit Data is Good Enough

Reality:
Traditional credit data is rarely enough to paint an accurate, holistic picture of a customers’ creditworthiness. Alternative data sources, including mobile/telco info, rent and utilities data, social media/web presence, and open banking info can help you gain a more comprehensive view of a potential customers’ financial health as well as their ability and willingness to pay.

The Data Challenge:
There is a ton of data out there, and it can often reside in siloed environments, making it difficult to access and costly to integrate into your decisioning. On top of that, it can be easy to assume that more data is the answer. But it’s not always what you need. The key to optimizing your data strategy is not necessarily more data but having the right data at the right time. According to IDC, in 2022“over one hundred thousand exabytes of data will [have been] generated, crossing the 100k threshold for the first time.” Yet 74% of decision-makers we surveyed said they struggle with their organization’s credit risk strategy because data is not easily accessible, and 70% say alternative data is not easily integrated into their current decisioning system. The use of alternative data to supplement traditional credit data (primarily bureau data) is critical to not only giving you a more accurate, real-time view of your customers’ creditworthiness, but it also expands your lendable market. By being more inclusive and saying yes to individuals who may have lower traditional credit scores, you’re improving financial inclusion and ensuring greater access to financial services andgrowing your business at the same time.

Myth #2:
It’s Too Costly to Upgrade Your Decisioning Tech

Reality:
It can be easy to assume that changing your decisioning tech will involve a massive amount of upfront investment (not to mention the fear of ‘wasting’ previous investments in your legacy tech). But can you afford not to upgrade? And keep in mind additional cost savings realized with self-sufficiency when changing your decisioning workflows and launching new products.

The Cost Challenge:
Cost pressures are everywhere. So it’s not surprising that sometimes banks are reluctant to consider changing technology platforms. With the hours of time and monetary investments made in implementing decisioning infrastructure, it can seem wasteful to transition away from legacy systems. But it’s important not to let the fear of past investments hold you back. Because with increased competition, demanding consumer expectations, and a shifting regulatory environment, having next generation decisioning tech is key. The cost of doing nothing will catch up to you – acquiring new customers, keeping your existing customers, preventing fraud, satisfying compliance requirements… non-action is a non-option. Upgrading your decisioning tech results in a lower total cost of ownership, thanks to eliminating product launch and iteration delays that lose you customers, the ability to automate risk decisioning workflows for more efficient processes, and improved fraud detection/prevention.

Myth #3:
It’s Too Difficult to Overhaul our Current Systems

Reality:
It’s not an all-or-nothing situation. Look for decisioning solutions that can run in parallel to your current software, or for ways to orchestrate your data more efficiently with a data ecosystem. This can create buy-in with other departments and lines of business when they see the improved efficiency and the way upgraded tech improves the overall decisioning process.

The Difficulty Challenge:
We’ve talked about the cost aspect of upgrading, which sounds daunting, but it’s about more than just money. Many people-hours are often put into choosing and implementing decisioning platforms – so why opt to do it all over again? Because the long-term benefits are worth it, and it may not be as difficult as it sounds. Rarely do you need to rip and replace all of your decisioning tech in one go. There are more flexible, agile decisioning platforms available that can integrate into or run alongside your existing workflows or you can choose to upgrade one line of business at a time. The key is choosing a technology platform that makes this easy and has experience with swapping out competitive decisioning platforms. (Provenir for example has vast amounts of experience replacing legacy, competitive decisioning systems, and can get you up and running, fast – however large the implementation may be).

How to Run the Smarter Race

One of the most common challenges banks are currently facing is competition – and the subsequent need to power risk decisions faster in order to keep up. But the key is to do this without sacrificing your risk strategy. It is possible to become more agile and self-sufficient, which allows you to make faster, more accurate risk decisions and launch new products in less than half the time – and one of the best ways to do this is upgrading to next-generation decisioning technology. Look for a partner that can offer you these key elements:

Real-time data access to hundreds of data sources through a single API

  • Advanced analytics based on your unique risk profiles
  • Integrated case management for a complete end-to-end perspective on credit applications
  • The ability to handle evolving compliance regulations and security demands
  • Low-code, business-user-friendly UI that enables self-sufficiency when changing processes and iterating workflows
  • Experience with swapping out legacy technology/competitive decisioning platforms to ensure a seamless transition
Leveraging automated, integrated data and more agile risk decisioning technology can help you increase your flexibility, accuracy, and speed. With the right tools on hand, you can keep up with new entrants in the market and also meet regulatory compliance requirements, all while making more informed credit decisions that improve the customer experience – and do it faster than the competition. Because in the race for customers… speed is everything.
Ready to improve your agility and run the smarter race?
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Digital Loan Origination in Banking: Competing with Challenger Banks

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Digital Loan Origination in Banking:
Competing with Challenger Banks

The financial industry has seen a dramatic shift in recent years with the rise of challenger banks. These digital-first establishments have emerged as serious competition to traditional banks, offering more personalized and innovative services that resonate with consumers. To compete with these new players, traditional banks must improve their digital capabilities and offer more streamlined services that provide customers with a better experience.

One area where banks can focus their efforts is digital loan origination. By automating this process and integrating it into their digital platforms, banks can provide customers with faster, more efficient loan processing. This is a crucial component in building a more competitive and innovative financial institution.

Digital loan origination allows banks to gather customer information and evaluate creditworthiness quickly and accurately. By leveraging data analytics and machine learning, banks can make better lending decisions while reducing the risk of defaults. This technology also makes it possible to offer more personalized loan products, which can increase customer satisfaction and loyalty.

Traditional banks can compete by improving their digital capabilities, and digital loan origination is a key area where they can focus their efforts. By automating loan processing and leveraging data analytics and machine learning, banks can make better lending decisions and provide customers with a better experience.

The End of the Level Playing Field

After the 2009 financial crisis, trust in traditional financial institutions took a hard hit with up to 80-90% of the public viewing them as untrustworthy, according to past studies. This led to an opportunity for challenger banks to enter the market with a clean slate and build their brand without the negative sentiment experienced by traditional banks.

Challenger banks also had a technological advantage over their established counterparts. Without the burden of legacy IT systems, challenger banks were able to adopt modern technology and offer digital services with greater efficiency and agility. As a result, challenger banks are quickly gaining ground, and the traditional banks are being forced to adapt or risk being left behind.

Challenger Banks: Reshaping the Future of Banking?

As the banking industry undergoes a transformation, many experts suggest that Challenger Banks will play a significant role in shaping the future of banking and money, despite the challenges that come with innovation. Unlike traditional banks, Challenger Banks tend to embrace a start-up mentality, leveraging a minimum viable product (MVP) approach to continually refine their product portfolio until they achieve the optimal balance.

While larger banks may struggle with operating in product silos and stretching their resources too thinly, Challenger Banks can prioritize quality and customer experience, giving them a competitive edge. But how can traditional banks compete with these innovative newcomers who are leveraging cutting-edge technology and a hyper-focus on innovative products and services?

Also, read: What is Banking as a Service?

Building Consumer Trust in Banking

Traditional financial institutions may have struggled with their reputations post financial crises, but a 2019 survey by Accenture showed extremely positive results for banks when it came to customer trust:

  • An average of 77.75% of consumers (across all persona groups) trust banks to care for their long-term financial wellbeing

Results were not so strong for non-traditional financial institutions:

  • Only 35.5% of consumers (across all persona groups) trust non-traditional institutions to care for their long-term financial wellbeing

So, while banks may be lagging behind when it comes to technology, they still outperform fintechs and challenger banks when it comes to consumer trust. Financial institutions trying to compete with their challenger competition should bank on the inherent trust that consumers still hold for brick and mortar institutions as a foundation to secure long-term loyalty with customers. Is this an obvious point to make?

Absolutely. But it’s how this trust can be used to build stronger bonds and expand product offerings that offers a huge opportunity for traditional financial institutions.

Dealing with Data: Customer Trust Expands Opportunities

In a time when data breaches are common, billions of records were stolen in 2018 alone, consumers are on high alert when it comes to sharing their information.

So perhaps one of the most fascinating results of Accenture’s study is that customer trust in traditional financial institutions extends to trusting banks to keep their data secure. 80% of consumers surveyed trusted their banks enough to share additional data to receive more relevant offers.

This gives banks an incredible opportunity to create truly personalized services using data gleaned directly from customers. But banks can go further, with many consumers sticking with the same financial institution for many years, banks have been gathering an immense amount of data on customers that can be used to personalize and pre-approve offers for individuals.

Wouldn’t it be nice if your customer’s felt like you truly understood their needs by offering the right products at the right times?

As a bank there’s a lot that can be learned from how challenger banks have approached disrupting the industry. Let’s consider a standard financial category that you may offer, and how the use of technology and data can improve that experience for your customers.

Mobile Loan Origination

Customers have an increasingly strong preference for the loan origination process to be mobile-friendly and fast.

  • Accenture found that on average 81% of consumers would share more information to get faster services and approvals

Challenger banks have greatly improved the loan origination process for consumers. They’ve removed the once long, paper-filled process and made approvals almost instant – all the while accepting nothing less than improved compliance and mitigated risk.

The smart pairing of data access and automation powers much of this process. And, while the idea of a loan being commenced and approved during an afternoon at work would be laughable 20-30 years ago, now it’s expected.

Offering this type of capability can seem daunting for both a startup with 25 employees and traditional banks, but launching a mobile or web app that can collect your customer’s application details, integrates with your systems and third-party data sources, decisions that loan, and provides an approval instantly is only a matter of starting with the right technology.

Building Data into Your Loan Origination Process: Using Data to Level the Playing Field

A common challenge banks face is being able to access, orchestrate, and use data. To get the most out of their historical data and gain access to new data, banks need to find a way to draw their data into one location as a foundation for decisioning and customer personalization.

Connecting disparate systems and data silos can provide banks with a huge advantage over their competitors as they’re able to gain much deeper insights into their customers and more easily assess associated risk. But legacy technology makes this almost impossible in many organizations.

To solve these issues, banks need to look for a solution that allows them to create a decisioning ecosystem. Technology that connects the dots between their CRM, historical data, new customer data, and their loan origination processes.

It’s only by using data to predict customer needs, pre-approve products, and personalize offerings that banks will compete with the challenger banks nipping at their heels. And, if banks can match this personalization across both physical and digital channels, banks could well disrupt the disrupters!

“Our entire approach is built on simplifying banking. One of the ways we do this is by making the customer experience fast and effortless; from the initial on-boarding process through to every subsequent interaction. The Provenir Platform gives us speed and flexibility in our lending operations, which enables a customer to apply for a loan at lunchtime, receive immediate approval, and have the money available in their account later that day.”

– CEO, Instabank

How Challenger Banks Are Capturing Customers’ Hearts (And Wallets)

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10 Fintechs Accelerating SME Lending

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10 Fintechs Accelerating SME Lending

Championing SME Survival and Growth

A new wave of fintechs and neobanks has been sweeping the world of SME Lending off its feet by embracing digital technology, data, and advanced analytics like machine learning and AI. And there’s never been a better time for it. The landscape has changed dramatically for SMEs, not necessarily for the better. The potential of a global recession has consistently lowered margins and hurt SME scaling and expansion efforts. According to a recent report by the World Economic Forum, nearly two-thirds of small to mid-sized businesses (SMBs) said survival and expansion are their primary challenge.

Unlike consumer payments, the B2B variety remain mired in legacy systems and manual practices. And unlike larger, established companies who have long and secure relations with their financial institutions, SMEs have a larger need of help in accessing working capital, which remains their critical pain point.

The result? As access to working capital from traditional lenders dries up, SMEs are increasingly looking to digital-first and alternative channels. A surprising 75% of SMEs report being more likely to use a digital-only bank as their primary provider of working capital. We revisit our list of SME lending innovators, as they go from trend setters to “the new digital normal” in SME financing.

  1. OakNorth – UK-based fintech OakNorth delivers instant credit analysis and real-time portfolio insights focused on transforming commercial lending. The co-founders of OakNorth were rejected for the credit needed to grow their business numerous times, prompting them to create their Credit Intelligence platform. Their goal was to build a robust, sustainable bank but also to create software that would enable other banks to lend to SMEs that were previously underserved.
  2. NeoGrowth – Founded in 2011, India-based NeoGrowth Credit is a tech-enabled business that offers unsecured loans to small retailers in India. Combining traditional and alternate data for more accurate credit scoring, NeoGrowth also offers dynamic repayment terms and automated collections processes to help identify the most creditworthy customers. Calling themselves pioneers in SME lending based on the underwriting of digital payments data, their mission is to help small business owners drive growth that matches their ambitions. Also read: What is credit underwriting?
  3. Kabbage – Selected for the 2019 Forbes FinTech 50 startups list, Kabbage (now owned by American Express) provides SMBs with credit by evaluating business-focused alternative data like accounting info, online sales and shipping. With this more nuanced view of data to better understand performance, Kabbage is able to offer flexible credit options in real time.
  4. Banco Pichincha – In 2016, Banco Pichincha received a credit line of $55 million from the International Finance Corporation (IFC) to finance loans to women-owned SMEs in an effort to fuel the growth of female Ecuadorian entrepreneurs. Ecuador’s largest bank, they doubled down on their mission in 2019 when they signed an alliance with the Overseas Private Investment Corporation (OPIC) and Wells Fargo for a combined loan of $108 million to support loans to MSMEs in the region that are owned, led by or support women.
  5. Allica Bank – Claiming that SMEs have often been left behind by the ‘big banks,’ Allica Bank combines modern technology with local relationships to ensure SMEs have the tools and the funding they need to operate. Based in the UK, Allica Bank offers SMEs asset financing, with up to £1 million worth of flexible financing options.
  6. Judo Bank – Australia’s only challenger bank built specifically for lending to SMEs, this innovative organization seeks to bring back the lost art of relationships in business banking. Created by experienced business banking professionals, they brand themselves as a ‘genuine alternative’ for SMEs who want quick access to not only funds, but the superior customer experience they deserve.
  7. First Circle – Based in the Philippines, First Circle’s mission is to enable SMEs to achieve their full potential through fast and flexible financial partnership. Their customers often have no credit data or fixed collateral and as a result are excluded from the traditional banking sector (and therefore often forced to work with predatory lenders). First Circle allows these SMEs to secure funding in as little as a day through an automated, digitized application process.
  8. Lulalend – Sixty percent of South African businesses find it difficult to access the capital necessary to grow their business, due to long wait times, painful paperwork requirements and the necessity of high collateral. Lulalend uses AI to score creditworthiness instantly, ensuring small business owners are able to receive funding within 24 hours of applying. To date, they’ve processed over 70,000 applications and secured funding for thousands of small businesses across South Africa.
  9. Siembro – Argentinian organization Siembro uses AI to power their in-house loan algorithm, providing them the ability to offer instant loan approvals for small businesses in the area of agricultural and machinery. With over 1.5 million small and medium farm businesses in the country who have limited access to credit (and limited cash flows), Siembro focuses on ensuring corn, wheat and soy farmers obtain the funding they need to survive.
  10. Iwoca – A start-up that began when its founders noticed that small businesses were getting shut out of access to much-needed credit, iwoca is now one of the fastest-growing business lenders in Europe. Working towards a goal of funding one million small businesses, iwoca wants to ensure that SMEs have more time to run and grow their business instead of being forced to fill out endless paperwork and wait for approvals. Recently, their B2B financing solution iwocaPay integrated with Quickbooks to help small businesses with their cash flow, increasing businesses’ customer base and revenue.

Faster Loan Approvals

By embracing the use of digital technology, data, and advanced analytics like machine learning and AI, these companies have been able to simplify, and in many cases completely transform application processes. They are able to automate credit decisioning to provide accurate, real-time approvals, allowing SMEs to gain access to funds quicker than ever before. By automating data collection, risk decisioning and pricing, lenders can automate approvals and ensure funding is in hand within a matter of only days – or even hours!

The capabilities these lenders are offering are not just a critical lifeline. Their products tend to be more flexible and more personalized to each SMEs unique needs, allowing them to go from mere survival, to full-blown adaptation to a changing, uncertain environment. That is the unique power of AI-fueled, data-led tech innovation.

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

Want to find out more about how to increase SME loan approvals without increasing your risks?

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