So you want to launch a buy now, pay later platform: 3 steps for success

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If there was any doubt in the hearts and minds of retailers and lenders about the viability of the Buy Now, Then Pay (BNPL) platform, they were relieved this past holiday season. By the end of 2021, shoppers had spent more than $ 20 billion using this point-of-sale lending offer to make immediate purchases and pay for them at a future date through short-term lending.

Since then, the BNPL has been identified as one of the hottest consumer trends on the planet, projected to generate up to $ 680 billion in transaction volume worldwide by 2025 and surpass all types of banks, fintech, retailers and ecommerce platforms. . For many, however, the path to developing successful BNPL programs is fraught with obstacles that quickly expose the central challenge to the BNPL proposal: it is not like any other form of financing that has come before.

From implementing real-time credit approvals based on scant customer data to scaling loan offers to deliver a seamless customer experience, real-world BNPL implementation presents a complex set of operational challenges with which few lenders and merchants have gained much experience. As a result, many new efforts are struggling to get off the ground.

Fortunately, there have also been some successful beginnings in space that have established some of the best practices for implementing robust BNPL programs. Based on my team’s work on developing large-scale BNPL initiatives, I have learned that one of the most important lessons is to start small, take a crawl, walk, run approach to the BNPL program rollout, allowing the program to learn as it grows.

Step 1: Expand your credit spectrum, narrow your loan offer

The biggest challenge in any BNPL situation is to quickly determine the risk appetite based on minimal customer data. This is not the area of ​​traditional credit decision making with its detailed credit applications and credit bureau-based risk scoring standards. In the general BNPL scenario, most unfamiliar customers are browsing items online, adding them to the shopping cart and expecting to complete the transaction in as few clicks as possible. The retailer should be able to offer a BNPL payment option, decide on a split-second credit, and transact in a matter of seconds.

It is inherently a high-risk proposition that focuses more on building the customer’s lifetime value than immediate profitability. In the early stages of the program, the retailer will seek to conduct a comprehensive net casting that would potentially involve allowing customers at relatively high-risk levels. This may seem contradictory, but it is important to take more risks initially to maintain the attractiveness of the BNPL offer, and the customer data collected in the process will help inform and guide the future of the program.

That risk is offset by diligently controlling the amount of dollars for BNPL offers shown to each customer and by being vigilant to limit the scope of the program based on the total risk appetite.

Step 2: Include an optional data set

As the program begins and continues, it is important to start ingesting and capturing merchant-specific data, such as customer purchase history, offer acceptance behavior, loyalty membership tires, etc., which feed into the optimization of underwriting and identity verification processes. Can. . This information needs to be integrated into lending risk algorithms directly with income reporting to “train” the system based on other alternative data sources, such as bank statements, utility reporting and real-world data.

Ultimately, BNPL applications need to be comfortable moving beyond traditional credit scores by redesigning their own real-time screening and risk rating tools based on the data generated from each new transaction. This allows the system to become smarter as it grows.

Step 3: Optimize for risk management

Once the system has been operational for several months and retailers and lenders are alerted to collect and analyze consumer behavior, it will be possible for customers to develop an optimization model that aligns individual BNPL offers based on their personal risk scores. This is where the real power of the program begins to manifest itself.

With this real-time, model-based approach to underwriting, merchants and lenders offering the BNPL platform will not only be able to fine-tune special offers at the individual customer level; They may also have developed a proprietary risk structure to understand customer behavior that is more detailed and subtle than anything that has happened before.

Re-establishing our relationship with risk

Getting the BNPL formula right requires a fundamental change in our traditional understanding of credit risk. Most traditional credit products involve a one-time risk assessment for a single product, while BNPL programs require managing multiple transactions at the customer level that occur at different times over time. Where traditional consumer lending models focus on forward risk assessment, BNPL applications require a calculation of trust at the front end in exchange for a wealth of highly personal data at the back end. Well done, it has the power to revolutionize consumer engagement in traditional wisdom. Wrong, it creates risks that will upset even the most ambitious lender players. The difference between the two is the ability to use the data needed to control the risk.

Vikas Sharma is Senior Vice President and Banking Analytics Practice Lead at EXL,


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