Mitigate Credit Risks in BNPL

Buy Now, Pay Later (BNPL) is a fascinating financing tool that presents incredible value potential to lenders, merchants, and consumers. Growing into a $100 billion industry globally with over 85% rise in user base (from 2020 to 2021), BNPL has evolved into an inevitable trend in the retail credit space.

Win-Win-Win scenario

There are no losers in the BNPL ecosystem. Whether it is consumers or merchants or lenders, all have clear winning potential. Consumers adore the power BNPL gives them to shop instantly and cost-efficiently, even if they have no cash at hand. And merchants prefer BNPL payments as it provides a great opportunity to drive sales. Sizeable BNPL players have attributed their 20% revenue increase to this consumer credit phenomenon. They’ve even reported that BNPL helped offset the generally higher transaction fees (appr. 4% to 7% per transaction) charged by service providers. Lending institutions love chiming into the BNPL tune, as it broadens and optimizes their credit issuance pipelines and drives scalable shorter-tenure small-ticket financing.

Secret sauce to success

Even though BNPL is one happy party, it still comes with some risk factors. Not just from external scamsters, but also from inherent vulnerabilities. Credit risks, chargeback scams, and Account Take Overs (ATOs) are typical issues that plague this consumer financing phenomenon. Managing and mitigating such risks is the secret sauce to successful BNPL operations. Implementing the right mitigation strategies can get rid of underlying risks without hampering customer experience.

Mind your risk exposure

A typical BNPL transaction value encompasses credit sanctioned to the customer and the amount reimbursed to the merchant. Usually, a consumer’s intent to pay, repayment capability, soft credit checks, and merchant recommendation are factored in before granting credit access. But this process doesn’t make the system immune to credit risks.

Credit risk occurs when a consumer defaults on the repayment after the transaction. The tiniest exposure to default risk can put any lending institution in a vulnerable position. It could even snowball into insolvency and liquidity issues for the BNPL service provider.

Strategies to mitigate credit risk

Evaluating creditworthiness plays a critical role in controlling credit risk. To make credit assessment as effective and secure as possible, best-in-class risk management parameters and credit profiling are required.

As smooth and secure customer experiences are non-negotiable for successful BNPL transactions, customer-centric strategies and efforts need to be directed towards effective credit evaluation. State-of-the-art technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics are crucial tools to manage inherent and external risk factors. These technologies help in reducing fraud and default risks by analyzing data patterns across metrics, business performance, cash flows, and past payment track records derived from varied data sources.

To mitigate credit risks effectively, the following strategies can be adopted.

Do a credit bureau check

In Nov 2021, RBI amended the Credit Information Companies Regulation 2006, allowing fintechs to access individual consumer credit information from credit information bureaus like TransUnion Cibil, Equifax, Experian, and CRIF. With this amendment fintechs and ecommerce players can partner with lenders directly to issue BNPL on the platform, without acquiring an NBFC license. This enables first-level checks (parameters listed below) for each consumer to determine the risk associated with lending.

• Credit rating
• Frequency of recent credit inquiries
• Tracking pattern of Days Past Due (DPD) and settlements
• Active credit lines
• Past and active credit frauds

Interlink data points

Interlinking data points associated with risk indicators such as invalid contact numbers, email addresses, or multiple card ownership will help detect irregularities. As per the privacy laws, the sensitive details of consumers need to be stored securely. So, without violating data breach and privacy laws, BNPL players can apply data analytics to gather valuable information on risk indicators, purchasing trends, and behavior.

Look for hidden linkages

Processing BNPL transactions is not a simple task. It involves multiple steps that include linking IP, devices, email, phone numbers, and other details, in order to track and validate a customer. There is a high possibility of hidden linkages in cases that involve multiple names with common phone numbers or email addresses. Machine Learning plays a crucial role in establishing this linkage and sorting out disorders such as delivery address discrepancies and multiple account sign-ins from a single device.

Check for sudden changes in transaction velocity

Credit risks and fraudulent transactions typically go hand-in-hand. Online frauds can be easily detected when there is a series of unusual high-value transactions taking place at consumer or merchant end. Cybercriminals indulge in this activity by using stolen card credentials to quickly spend the money or even create multiple accounts with fake user details.

Keep a lookout for shady delivery address patterns

When it comes to ecommerce and BNPL, one must never underestimate the power of delivery address tracking in risk mitigation. Machine Learning tools check on delivery address patterns and take risk identification to the next level. With minimal human intervention, ML effectively detects mismatches and erratic patterns in shipping and billing addresses. This data plays a key role in detecting BNPL risks.

Trust alternate data

Asked to choose between the efficacy of traditional data and alternate data, most leading credit check houses will vouch for the latter. When a customer is credit-invisible, alternate data such as utility bills, rental bills, social media usage, travel history, employment record, and even property ownership come in handy to save the day. Alternate data supplies critical information for AI-based credit decisioning engines to determine a customer’s credit risk. Parameters such as income, nature of expenses, FOIR (Fixed Obligations to Income Ratio), location checks, negative list scrub (for PIN Code, credit check), and merchant data must be prioritized over other data points.

How we help lenders overcome BNPL risks

Fintech companies and lenders today are vying to scale up their BNPL offering by investing in in-house technology capabilities and building risk modeling expertise directed toward the automation of credit decisions. A major hiccup here is the exorbitant cost and effort involved in technology upgrades.

This is where a 3rd party partnership is required to curb costs and mitigate risks without compromising on customer experience. The collaboration will manage BNPL operations and risk management in totality while you can focus on the core business.

Our BNPL Platform is a perfect example of the synergy scenario. We deliver the best risk management strategies for diverse consumer journeys via our loan management & loan origination System. Our credit decision engine hunts and solves credit risks associated with each consumer. Using multiple cutting-edge APIs, we cover New to Bank, New to Credit, and of course existing credit data consumers at the quickest time possible.

Our proprietary API stack enables a secure and seamless shopping experience by pairing customer onboarding with a lender credit decision engine. We deliver risk management strategies that cater to Merchants, Lenders, and Fintechs across distribution channels such as card-based pay later, digital payment lines (multiple merchants), and closed-loop lending platforms.

Want to know more about how your BNPL program can benefit from our credit decisioning engine?

Write to us at business@m2pfintech.com. We’ll be glad to help.

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