Buy Now, Pay Later (BNPL) has leapt from fringe payment option to mainstream checkout button. Fourteen percent of U.S. adults used a BNPL service in 2023, up from 12 percent the year before, according to a recent Motley Fool study. Yet millions of shoppers, especially younger consumers and those in emerging markets, have no traditional credit history. Bureau-only scores can’t see them, leaving lenders to choose between blanket rejections and blind risk. Alternative data offers a more complete, real-time picture.
The Challenge of Credit Invisibility in BNPL
What Is Alternative Data?
In credit underwriting, alternative data is any information outside traditional bureau files or bank statements. Popular sources include:
- Device & mobile signals: handset model, SIM tenure, geolocation consistency.
- E-wallets & payment-app usage: top-up cadence, peer-to-peer transfers, cash-out frequency.
- Telecom & utility payments: airtime spend, electricity or water bill timeliness.
- Bank-feed cash flow: deposit regularity, expense volatility, recurring income.
- Digital footprint: email age, IP stability, social-media tenure.
- Behavioral biometrics: typing cadence, form-fill velocity, mouse-movement patterns.
Most of these feeds refresh in real time, providing a living snapshot rather than a quarterly update.
How Alternative Data Enhances BNPL Credit Decisions
- Richer applicant view. Layering telco, utility, and device signals onto thin bureau files fills blind spots in both ability-to-pay and willingness-to-pay.
- AI-driven insights. Machine-learning models can digest thousands of fine-grained variables that manual scorecards miss.
- Tangible wins for BNPL providers
- Broader reach. Deployments of Synapse’s enrichment APIs in MENA have raised approval rates for “no-hit” applicants by ≈ 30 percent without elevating delinquency.
- Sharper risk prediction. A 2024 Nova Credit survey found that 90 percent of lenders see alternative data as crucial to approving more credit-worthy borrowers.
- Instant onboarding. With pre-mapped data pipes and automated decisions, loan approval times drop from minutes to seconds.
Real-World Impact and Case Studies
- Private BNPL data → bank loans. A VoxEU/CEPR study showed that borrowers with a solid BNPL repayment record were nearly 30 percentage points more likely to win a standard bank loan, even when their external credit scores were identical.
- Retail example. A Middle-East electronics retailer layered mobile-SDK device signals and e-wallet usage onto its legacy policy. Over six months, “credit-invisible” approval rates rose 28 percent while early-stage delinquencies fell 22 percent.
- User sentiment. The CFPB reports that BNPL use is most common among consumers already juggling multiple loans — data BNPL providers themselves can surface faster than bureaus.
Addressing Privacy and Compliance
- Consent and purpose limitation. Personal data such as telco or behavioural signals usually requires explicit user consent and data-minimization.
- Explainability matters. Regulators demand specific adverse-action notices even when AI is involved; see the CFPB’s 2023 guidance.
- Fair-lending checks. Regular bias testing and clear “model cards” ensure that proxy variables do not create disparate impacts.
Alternative data is transforming BNPL from a blunt instalment tool into an inclusive, data-rich credit channel. By embracing consent-based data streams and transparent AI, lenders can unlock vast new segments, lift approval rates, and cut fraud, without compromising compliance. Those who stick to bureau-only views risk being left behind as BNPL matures.
Ready to see what alternative data can do for your BNPL program? Talk with Synapse about our plug-and-play enrichment APIs and decisioning models today.
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