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If there were any doubts in the hearts and minds of retailers and lenders about the viability of Buy Now Pay Later (BNPL) platforms, they were allayed this past holiday season. By the end of 2021, shoppers had spent over $20 billion using these point-of-sale lending offerings to make purchases immediately and pay for them at a later date through short-term financing.
Since then, BNPL has been recognized as one of the hottest consumer trends in the world, driving up to $680 billion in transaction volume worldwide by 2025. For many, however, the road to developing successful BNPL programs has been strewn with obstacles that quickly reveal the central challenge of the BNPL offering: it is unlike any other form of lending that has come before it.
From executing real-time loan approvals based on sparse customer data, to scaling loan offerings and delivering a seamless customer experience, real-world BNPL implementation presents a complex set of operational challenges that few lenders and merchants have much experience with. As a result, many young efforts have struggled to get off the ground.
Fortunately, there were also some successful early forays into the field that established some best practices for implementing strong BNPL programs. Based on my team’s work in developing large-scale BNPL initiatives, I have learned that the most important lesson is to start small, to take a crawl, walk, run approach to BNPL program adoption, thereby program can learn as it grows.
Step 1: Expand your range of credit, narrow down your credit offering
The main challenge in any BNPL scenario is to quickly determine risk appetite based on minimal customer data. This is not the realm of traditional credit decisions, with their detailed loan applications and credit bureau-based risk assessment standards. In a typical BNPL scenario, a largely unknown customer browses items online, places them in a shopping cart, and expects to complete the transaction in as few clicks as possible. The retailer needs to be able to offer a BNPL payment option, make a split-second credit decision, and execute the transaction within seconds.
This is an inherently high-risk endeavor that focuses more on building customer lifetime value than immediate profitability. In the early stages of the program, a retailer will want to cast a broad net that will likely include admitting customers at comparatively higher-risk tiers. This may sound counter-intuitive, but taking a higher level of upfront risk is critical to maintaining the attractiveness of the BNPL offering, and the customer data collected in the process will help inform and guide the future of the program.
This risk is offset by carefully controlling the dollar amount for BNPL offers displayed to each client and by using safeguards to limit the scope of the program based on overall risk tolerance.
Step 2: Integrate alternative datasets
When the program goes live, it is important to start recording and collecting vendor specific data such as: B. Customer purchase history, offer acceptance behavior, loyalty membership level, etc., which can feed into the optimization of underwriting and identity verification processes. This information, along with other alternative data sources such as bank statements, utility reports, and income reports, must be integrated directly into lender risk algorithms to “train” the system based on real-world data.
Ultimately, BNPL programs must be comfortable going beyond traditional credit scoring by recreating their own real-time screening and risk assessment tools based on data generated from each new transaction. This allows the system to become smarter as it grows.
Step 3: Optimize to manage risk
Once the system has been up and running for several months and retailers and lenders have carefully collected and analyzed consumer behavior, it will be possible to develop an optimization model that targets personalized BNPL offers to customers based on their individual risk assessment. This is where the true power of the program begins to reveal itself.
With this real-time, model-driven approach to underwriting, merchants and lenders offering BNPL platforms can not only optimize special offers at the individual customer level; They will also have developed a proprietary risk framework for understanding customer behavior that is far more detailed and nuanced than anything previously seen.
Realigning our relationship with risk
Finding the right BNPL formula requires a fundamental overhaul of our traditional understanding of credit risk. Most traditional lending products involve a one-time risk assessment for a single product, while BNPL programs must manage multiple customer-level transactions that occur at different points in time. While traditional consumer credit models focus on upfront risk assessment, BNPL programs require a calculated leap of faith on the frontend in exchange for a treasure trove of highly personalized data on the backend. Done right, this shift from conventional wisdom has the power to revolutionize customer engagement. Getting it wrong creates risks that make even the most ambitious lender uneasy. The difference between the two is the ability to leverage the data needed to control risk.
Vikas Sharma is Senior Vice President and Banking Analytics Practice Lead at EXL.
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