Expand Your Risk Decisioning Universe

Discover how advanced analytics and automated decisioning can help you win in the SME Lending, Auto Lending, Credit Card Management, BNPL, and Consumer Lending markets.

We could list every process or strategy change financial institutions, fintechs or payment providers made last year, but one, we’d be here forever, and two, no one would make it past the first page. So, while we won’t inflict that on you right now, it would show one thing: the true scale of the agility needed to succeed in a digital-first world. It won’t come as a surprise to anyone that digital-centric transactions are the new norm, and increased consumer choice is putting customer loyalty to the test. But organizations should not lose sight of the human experience in the rush to go digital. Making the customer delighted and optimizing risk decisioning can go hand in hand. One doesn’t come at the expense of the other. It’s time to embrace a world where risk decisioning agility and world class customer experiences are must haves, expanding your businesses universe means succeeding at both. According to McKinsey’s December 2020 article on shifting customer attitudes, 42% of US financial decision makers use at least one fintech and more than 6% reported becoming a fintech user within the last 12 months. 1 This study went on to say that payment, investments, lending,

and overall banking have grown across all age groups and income brackets. With this level of competition in the industry, it is hard to determine who the challenger is. Every player in the industry must be focused on the right investments to deliver value to both the customer and the organization. And Jim Marous of The Financial Brand agrees, “banks and credit unions needed to review their entire business model, from products offered to back-office processes that had been in place for decades. With an industry and economy still in a state of flux, organizations need to find ways to serve consumers faster, with less friction, and at a lower cost. More than ever, there will be a focus on data and analytics, modern technology and the ability to deliver an improved customer experience.” 2 The one thing we know for sure is that there is a sense of urgency and digitally native fintechs have an opportunity to influence the direction. The formation of powerful partnerships between financial services organizations, with growing customer bases, and fintechs, who offer sophisticated data, analytics and the decisioning capabilities necessary for retention and gaining market advantage, can define a new industry benchmark. Placing a bet on the right solution is key.

As industry dynamics continue to evolve, do you find yourself asking these questions?

• How can I make immediate credit card decisions that satisfy both the consumer and the issuer? • What can I do to enhance consumer lending practices to keep or win business? • How can I make my Buy Now Pay Later (BNPL) offerings more competitive? • Can I reduce time to funding to better support the SME lending market? • Is auto lending as thorough as it should be without impacting the customer experience? If your goal is to grow or solidify your position as an industry leader this year, then you probably ask these questions daily. Let’s discuss the use cases for credit card management, consumer lending, BNPL, SME lending, and auto lending and the impact the right risk decisioning and analytics technology can have on your risk decisioning universe.

another; is it based on rewards, interest rates or travel affiliation? As we enter 2021, there are many questions within the Credit Card sector that need consideration. 2021 brings forward the challenges of the last year, especially within the Credit Card sector. With a few exceptions, issuers have traditionally been laggards in the risk technology race, relying on staid and true legacy systems to process new applications in line with their established risk policies. We learned in 2020 that the daily changes in our economy have forced issuers to reconsider their approach to risk analytics, creating new models, new policies, utilizing traditional risk data plus alternative data sources not previously considered. On top of that, in the digital world, the need to create a positive customer experience plays a critical role in whether they remain loyal to one card or another. Issuers are forging new paths, considerations and, ultimately, opportunity.

If you look in your wallet, how many credit cards do you have? Of those, how many do you use daily? Paying for your daily coffee purchase, groceries at the local market, or gas for your car? Based on the latest available research from The Global Economy, in 2017, almost 66% of Americans possess one or more credit cards. 3 This is a competitive sector and one that continues to grow. In the same research, it is estimated that by 2025, consumers will utilize digital payments to the tune of $132.5 billion dollars. How do you differentiate one card from

Is there a way to tie the old world with the new? The Cloud offers issuers the opportunity to rapidly move forward in the risk analytics and decision space. Traditionally, issuers utilized “on premises” technology that requires IT’s assistance for updates to their risk infrastructure. Changes that require, time, effort, testing, and governance approval prior to production. This has resulted in changes taking six months to a year before they are put into production. Utilizing Cloud Technology and no/low code processing provides issuers the ability to be more nimble, efficient, and effective in making necessary changes to their evolving risk strategies. Accessing multiple data resources via APIs increases the availability of current data that can be used to power smarter decisioning. Credit risk data (Bureaus), compliance data, internal performance data, alternative data, etc., all play a role in today’s risk analytics practices. For issuers to grow, compete, and thrive they need to reassess their current legacy systems and consider a focus to cloud-based decision solutions.

During 2008-09 many banks that offered a personal loan product to consumers pulled back from the market, and into that void stepped fintech consumer finance companies. The banks sat on the sidelines of this market for years and as they watched the growth within the space, many have decided to reenter the market. Personal loan products are primarily used by consumers to consolidate high interest credit card debt into a lower interest loan product. These consumer-friendly loans are also used to finance elective medical procedures or make large ticket purchases, but upwards of 85% of all loans in the space are issued for debt consolidation. The market for consumer finance is now divided between traditional players looking to re-enter and compete alongside fintechs that established themselves during the period when banks pulled back. The competition in this market is fierce and consumer finance companies are focused on

reinventing and improving the customer experience for onboarding and underwriting their loans. In order to stay competitive, fast decisioning utilizing multiple data sources and reduction of latency inherent in the application process is a major focus. Lenders need to be able to develop new models and deploy them quickly as well as make changes to their decisioning strategies rapidly in response to changes in the marketplace. Lenders also need to be able to evaluate new and emerging data sources alongside traditional credit bureaus and to integrate alternative data sources into their decision strategy quickly and easily.

One of the most overburdened resources within a FinTech is the development team, and as such, lenders need options beyond building it themselves. Establishing a decisioning partnership allows lenders to free up IT and development resources to focus on the customer UI, which is the most important aspect of their business and is key to staying ahead of the large Banks/FIs that have been steadily moving back into the space. For banks looking to reenter consumer lending, a strong need is to automate portions of their onboarding process in order to compete.

Consumer financing goes beyond traditional credit cards or other consumer lending products. Buy Now, Pay Later (BNPL) is a rapidly growing ecommerce payment option. BNPL is growing in popularity among US consumers for the following reasons: 1) low or interest free financing, 2) avoidance of incurring credit card debt (some BNPL customers do not have a credit card), and 3) ease of experience at checkout. Consumers are more willing to try new products that provide a better experience - driven by convenience, value, and choice. Merchants are eager to offer BNPL to consumers. Some of the top benefits include Average Order Value (AOV) lift, sales lift, and cart abandonment reduction. First-time BNPL customers are also more likely to make more BNPL purchases over time. It also allows merchants to avoid the interchange fees incurred with more traditional credit products. However, implementing a BNPL offering is not trivial. Critical requirements of a BNPL experience are a frictionless checkout process and flawless execution in the transaction process. Merchants have many considerations when it comes to implementing BNPL, as not one size fits all. Whether the merchant decides to partner with a direct- to-consumer (consumer branded) platform, or a white-labeled platform, the back-end technology considerations are similar. While consumers may expect that a given BNPL process should be more lightweight than applying for a credit card, many of the same back-end processes that occur for other forms of credit also need to happen with BNPL. Credit issuers that offer BNPL products must have the ability to tailor decisioning and adjust to constantly changing product offerings.

Some common requirements for BNPL back-end platforms: • Embedded in the purchase flow • Proven ability to

A major US-based BNPL fintech was challenged with a slow credit process that took minutes for a credit decision, and an inefficient process for making changes and updates that took weeks. After evaluating the options to update this critical part of the business, the BNPL fintech chose Provenir to power the credit underwriting decisioning, and the results included: • Reduced decisioning time from minutes to seconds, decreasing cart abandonment and increasing sales • Significantly cut time to operationalize models, transforming the credit policy execution strategy resulting in more competitive campaigns and products Due to the ease of use and ability of the credit, risk, and data teams to control the process, the BNPL fintech was able to reduce the number of people needed to support the credit decisioning platform, as the manual work and development that was previously needed was no longer necessary. Provenir continues to play a significant role in providing a competitive edge and optimal user experience for the BNPL fintech, and others in the credit underwriting market.

provide a simple and compelling user experience

• Ability to provide a credit decision in real-time • Flexible underwriting models; support a broad range of credit profiles • Low friction to implement; ensure product can be rapidly implemented and scale across different merchant sizes with low effort Given the appetite in the market, BNPL is an enticing and valuable strategy for credit issuers to employ. It is critical that BNPL lenders empower the key user experience requirements and address the critical technical requirements to compete for the fast-growing BNPL segment. Provenir offers the market-leading solution to address the need for a robust decisioning platform, which automates complex decisions in seconds while managing the credit risk.

Bureau companies often have sparse data on small businesses, and SME Lenders struggle to make sense of incomplete and disparate data. To make things more difficult, bureaus often cannot provide the proper level of confidence that the data is accurate; or assure that the data belongs to the business being inquired upon. This makes underwriting and account reviews manually intensive and challenges the ability to create cost efficiencies as an SME Lender attempts to scale. Another reality is that many SME business units are aligned within their company’s consumer vertical, making it a challenge to secure the technical resources needed to support agility. SME leaders struggle to tell an ROI story that can equal that of a consumer ROI business case given the difference in volumes, making it easy for companies to deprioritize SME initiatives in favor of consumer projects. Without a proper technology commitment, SME business units cannot efficiently launch new products, test new data, partners and strategies. SME Lenders are often forced to “make their needs work” within infrastructures and capabilities designed for consumer processes that don’t allow for the complexity of, or the inherent differences between consumer and SME transactions. This includes common issues such as: differing bureau results; more complex identity resolution processes; managing multiple borrowers; evaluating entities with multiple names and locations; personal versus business guarantors etc. SME Lenders are falling behind in their ability to use AI and Machine Learning as they lack easy access to the available universe of data necessary to feed AI driven model development. Even though bureaus have a deep data set of elements and attributes, legacy systems and consumer infrastructures often

A leading SME Lender in North America, which processes around 10,000 small and mid-sized loan applications per month, was struggling with challenges created by legacy systems. They faced an inability to evolve segmentation strategies or modify existing risk strategies, limiting their ability to power instant decisioning. Most of their loan decisions were produced manually and took up to five days. Using Provenir’s decisioning and data capabilities, the lender deployed advanced, segmented underwriting rules and gained access to additional data. This increased their automated decisioning rate to 80%. This SME Lender can now dedicate manual underwriting resources towards complex issues and exception

cannot handle the wealth of data. And, many potentially predictive elements and attributes end up on the cutting room floor before they can be evaluated. The best way to combat the outlined challenges is by incorporating technology that automates manual effort while removing dependencies on technology to deploy. Provenir has enabled SME’s in this way across the globe. Provenir understands the complexities of business data, automates manual decisions, and provides tools that enable our partners to connect to data, change strategies, and deploy new products with little to no technology intervention.

cases, and can scale without proportionally increasing staff.

As a consumer contemplating the purchase of a new vehicle, walking into the auto dealership today is a very different experience of years gone by. Consumers are heavily armed with information based on their countless hours of research – they’ve consulted Kelly Blue Book or Canadian Black Book and already know the car’s value that they want to acquire, the estimated value of their trade-in, in the case of a used car purchase they likely know the car’s history, or with a new vehicle they already have an idea of all the upgrades and features they want included in the price of the car. What has changed dramatically in recent years is that consumers may also know their credit score and are more prepared when walking into the Finance Manager’s office to secure financing for their purchase. Now, more than ever, the savvy consumer is seeking a smooth, frictionless buying experience. In today’s ever-changing, dynamic and digitized economy, the expectation of “add to cart and check out” is starting to transition into the world of auto buying. Consumers who encounter friction in the Finance Manager’s office, being asked to produce documents to validate identity or income, or who aren’t able to secure the best rates or pricing incentives based on their research and knowledge of their credit score, may move on to your competitor who has removed those barriers and made the purchasing process a seamless experience. As a lender, how do you overcome those challenges that result in lost sales in the financing office? For auto lenders, the challenge to digitize has been hampered by legacy, outdated platforms. Getting access to the latest tools and data assets in the market are virtually impossible, or the charges from your vendor too cost prohibitive to contemplate. The inability to provide faster application decisioning, and rapidly make changes results in lower dealer satisfaction results and lost sales.

For those auto lenders looking to compete in the next generation of auto buying the search for a platform that can accommodate rapid data integrations, orchestrate complex processes and strategies, and operationalize custom scorecards and models quickly and easily may seem overwhelming. At Provenir, however, these are the cornerstones of our best-in-breed risk decisioning and data analytics platform.

Provenir’s data, analytics, and AI risk decisioning capabilities are setting the new industry standard for driving a higher level of customer service with lower risks. It’s a combination that is propelling the industry in new directions. Technology, combined with an innovative spirit will help define the winners as we emerge from the challenges of the global pandemic. No business process will be immune from addressing weaknesses identified and spotlighted by the dramatic

shifts in operations and expectations. The solution will be investments in technology that deliver business value through reinvented processes. Banking models for risk decisioning will forever be transformed. Why not start by looking at how you can transform credit card management, consumer lending, BNPL, SME lending, and auto lending processes where you can expand your risk-decisioning universe and see a rapid return on your investment?

Endnotes 1 McKinsey, “How US customers’ attitudes to fintech are shifting during the pandemic” December 17, 2020. 2 The Financial Brand, “Banking Must Commit to Increased Tech Spending in 2021” December 14, 2020. 3 The Global Economy, “Percent of people aged 15+ who have a credit card, 2017 - Country rankings” 2017.

Provenir offers no-code, cloud-native SaaS products and solutions that enable fintechs, financial institutions, and payment providers to make smarter risk decisions, faster. Provenir works with disruptive financial services organizations across 20+ countries and processes over 100 million transactions annually. Clients use Provenir’s no-code technology to easily design, build, and deploy solutions to solve complex business challenges such as digital onboarding, retail financing, BNPL approvals, SME lending, insurance, and credit card management. Fully configurable SaaS products empower businesses to rapidly enhance decisioning processes with a wide variety of alternative data and powerful machine learning tools, without sacrificing speed or simplicity.

Authors: John Graff, Credit Card Management; Graham Terry, Consumer Lending; Dom Schaffer, Buy Now Pay Later; Mike Rini, SMB Lending; Julie Mannella, Auto Lending

With a cloud infrastructure spread across 33 countries and a global marketplace for seamless integration, Provenir has simplified data access, supercharged decisioning speed, and eliminated vendor reliance, so financial institutions can take control of local and global strategies. Say hello to data, faster product launches, and smarter decisioning with Provenir. Provenir has helped fintechs grow from startup to decacorn and empowered banks and payment providers of all sizes to transform their decisioning processes and generate measurable business value.

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