Financial Services

How to Minimize Business Risk as a Buy Now Pay Later Provider

As the digital lending industry continues to expand globally, the digital payment solutions like “Buy Now, Pay Later” (BNPL) have taken the industry by storm. As reported by Kaleido Intelligence, the BNPL market has witnessed significant growth in recent years and is “expected to exceed $760BN in 2025”1

Due to its quick onboarding process, ease of accessibility, and pay-over-time solution, customer-facing brands are experiencing wide adoption and increasing average cart values.

The ability to spread installment payments over time rather than a significant upfront cost provides an appealing option to those on stricter budgets. Although convenient for customers, this exposes lenders to new risks.

Growing Risks Associated with Offering Buy Now Pay Later

Default Risk in Consumers

While the concept of borrowing money isn’t new with the availability of small loans and credit cards, the specific use of BNPL transactions is dominantly used in smaller purchases as an alternative to other sources of credit such as credit cards.

However, when customers cannot make timely payments on BNPL purchases, debt accumulation can become too great for those beyond their means, and ultimately they default on their loans. The BNPL provider then spends resources on monitoring and debt collections, leading to losses for booking bad loans. But approving bad loans can also lead to larger implications for the future.

Potential Regulatory Risk

When businesses cannot accurately gauge intent to pay or onboard customers who have the propensity to default, harboring bad loans can become an issue in the future when it is expected that the BNPL industry will face increased regulation.

According to the U.S. Consumer Financial Protection Bureau (CFPB), they plan to “issue guidance to oversee BNPL vendors and have them complete ‘supervisory’ exams in line with credit card company reporting requirements.”2

But these new regulations targeting BNPL aren’t just limited to the United States but are also a growing concern globally. For example, the Reserve Bank of India recently established new guidelines prohibiting non-banks from loading prepaid digital wallets/stored-value cards using credit lines as the first step toward growing regulation.3

With increased consumers leveraging BNPL offerings and unforeseeable regulations, how can financial organizations best prepare to mitigate risks?

Credit Risk Modeling Insulates BNPL Providers From Defaulters

As regulators are focused on the financial safety of consumers, the key to minimizing risk is to ensure you onboard the right customers. 

In emerging markets where credit data isn’t readily available, financial companies rely on complex machine learning (ML) techniques to create reliable credit risk models. These models leverage relevant data and predictive features to understand consumer behavior and assess risk to disqualify potential defaulters. 

However, relying solely on proprietary data does not reap enough information to accurately predict default risk. 

Instead, both the risk and growth teams at financial institutions are now working with alternative data providers to supplement their business intelligence with additional information like demographics and behavior patterns and tweak their algorithms to build a 360-degree risk profile of their consumers. 

By taking this data-centric approach to developing their ML strategy, they improve their overall data quality, breadth, and depth, proving crucial to building accurate predictive models. 

Read our case study to understand how fintech companies use third-party data to prevent fraud and decrease defaulters. 

Mobilewalla Helps Fintech Companies Improve Risk Decisioning Models

More than a dozen BNPL providers rely on Mobilewalla’s data for assessing default risk. Mobilewalla is a leading innovative data solutions provider offering distinct AI-driven data attributes and features to help minimize the risk of lending to thin-file customers by boosting the efficiency and accuracy of predictive models. Mobilewalla teams up with risk teams, growth teams, and data scientists within the financial industry to help them:

  • Verify customer identity and manage the risk of fraudulent applications 
  • Use predictive features to help create risk scores and understand the propensity to default
  • Enrich internal data with Mobilewalla data to improve the debt collections process and recovery


Ready to leverage Mobilewalla’s robust portfolio of alternative data and predictive modeling features to improve your predictive models?

Contact our data experts and let us discuss data solutions for your organization.


1Kaleido Intelligence, Buy Now Pay Later & EPOS Credit Spend to Exceed $760 Billion in 2025, as the Installment Market Booms. (2021, April).

2TechCrunch, CFPB Signals That Regulation is Coming for BNPL. (2022, September).

3Washington Post, Why India is No Fan of Buy Now, Pay Later. (2022, June).

Picture of Laurie Hood

Laurie Hood

As Chief Marketing Officer, Laurie Hood is responsible for all aspects of Mobilewalla’s marketing strategy including messaging and positioning, brand awareness, demand generation and sales enablement. She brings extensive experience in technology marketing and product management to Mobilewalla most recently holding leadership roles Equifax and IBM, through their acquisition of Silverpop a marketing automation company.