How Credit Reporting Models Are Failing Modern Lending

Designed for a time when long-term, secured borrowing was the norm, credit scoring is struggling to adopt to BNPL. As a result, consumers are being punished not for being risky, but for engaging with modern credit tools.

By Alex Naughton, CEO of Qlarifi

Published on April 23rd, 2025 in Loan Growth

Simple Subscribe

Subscribe Now!

Imagine a world where responsible borrowers, those who use credit products wisely, repay on time, and follow the terms as designed, are penalised by the very system that is meant to assess their financial health. This is not a hypothetical. It is the daily reality for millions of Buy Now, Pay Later (BNPL) users.

The credit reporting system is no longer fit for purpose. Built for a different era, it fails to account for the dynamics of modern lending, particularly short-term, high-frequency credit products like BNPL.

Instead of helping lenders assess risk and enabling consumers to access fair credit, the current system misrepresents creditworthiness. This is not a niche issue with almost 100 million BNPL consumers in the US alone, it has wide-ranging consequences for financial inclusion, consumer protection, and fintech innovation.

However, the pace and old-fashioned infrastructure of credit bureaus as they stand, don’t just affect BNPL customers, but all credit users. Think about how much better decision making would be for credit card companies if it were real-time, or how lenders could cater to so many more consumers with a more comprehensive understanding of them.

An Outdated Scoring Model

BNPL is designed to let consumers spread the cost of purchases through short-term, interest-free instalments. Used as intended, it promotes responsible borrowing. Yet if fully integrated into traditional scoring models, it would lower credit scores for these very users.

Why? Because scoring models were built around long-term, low-frequency products like mortgages and personal loans. They penalise patterns typical of BNPL users:

High application frequency: Like credit card usage, many people use BNPL multiple times per month, but BNPL transactions are usually individual loans, not part of a larger balance. This means each transaction is its own line of credit and – legacy models flag frequent loan applications as high-risk behaviour.
Recent credit activity: Recent loan applications skew models that associate recency with financial stress.
Credit mix bias: Traditional scoring prefers a spread of long-term, secured credit, categorising BNPL as high-risk unsecured debt.

This current approach fails to distinguish between distress-driven borrowing and usage consistent with the intended product design. As a result, consumers are punished not for being risky, but for engaging with modern credit tools. It creates a perverse incentive structure where low engagement with innovative financial products is rewarded, while responsible, consistent use is flagged as negative.

-- Article continued below --

Inflexible Infrastructure

The systems underpinning credit reporting, such as the Metro 2 Format, were created decades ago. They simply were not designed to handle the scale, frequency, and nature of BNPL data, or really most modern technologies and the financial products that are now emerging. This is because:

Traditional frameworks capture account balances monthly. They cannot record per-transaction details or payment timeliness with the necessary granularity.
Without a standardised method for reporting BNPL data globally, lenders face inconsistencies that distort risk assessments.
The lack of API-driven, real-time integration means lenders often operate with stale or incomplete data, undermining confidence and fairness.

Without a fit-for-purpose infrastructure, reporting BNPL data leads to inconsistent interpretations and outcomes. And for providers, this becomes a cost issue. Poor data leads to suboptimal underwriting, increased defaults, and unnecessary friction. Risk models calibrated on incomplete or misaligned data cannot be trusted, which forces lenders to either tighten credit unnecessarily or overextend and absorb losses.

The Impacts Of Delayed Reporting

Traditional credit data is typically reported monthly. That works for products with long cycles, like car loans or mortgages. But BNPL operates on timescales of minutes.

A consumer could apply for multiple BNPL loans in a day. A traditional bureau wouldn’t register this until weeks later so someone making ten BNPL purchases in an hour might seem responsible to a lender without that visibility, despite being potentially financially overextended.
A missed BNPL payment may reflect a refund or technical issue, not a risk signal. Without timely context, nuance is lost.

This creates blind spots. BNPL lenders cannot detect stacking behaviour, nor respond quickly to changes in consumer affordability. The result is more risk for lenders and fewer protections for consumers.

Real-time data is essential in this context. It enables dynamic risk assessment, early intervention, and fairer outcomes. For example, a lender equipped with real-time insight could pause new approvals for a customer showing signs of financial stress, preventing further harm.

Dig deeper:

Rethinking The System From The Ground Up

The solution is not to exclude BNPL data from credit reporting. Nor is it to shoehorn new behaviours into old models. We need structural change.

A credit reporting system designed for the 2020s should include:

Modern scoring logic: Models should focus on actual repayment behaviour and differentiate between product types. High frequency, short term credit such as BNPL should be assessed in its own context.
Real-time data infrastructure: Data should be delivered instantly, through APIs and event driven systems, enabling lenders to see the full picture in the moment it matters. Real time capabilities also allow for responsible product design, like adaptive credit limits and instant affordability checks.
Fit for purpose standards: Data formats must accommodate transaction-level detail, flexible repayment schedules, and user context. Standards should evolve with innovation, not lag behind it. A shared taxonomy for short-term credit products is also essential to ensure consistent interpretation.

-- Article continued below --

The Scale Of The BNPL Market

Understanding the urgency of reform requires acknowledging the scale of the BNPL market. Globally, BNPL volumes are forecast to hit $700 billion by 2028. This is not a marginal category, it is rapidly becoming a core part of consumer credit infrastructure.

What makes BNPL particularly significant is its reach and appeal among younger, digitally native consumers who are underserved by traditional credit products. For many, BNPL is their first experience of credit. If we allow outdated reporting systems to either not include, or potentially misclassify, their behaviour, we risk distorting the financial profiles of an entire generation.

The Broader Implications

BNPL is an indicator of where lending is heading; flexible and user centric. The challenges it poses to credit reporting are a preview of broader systemic misalignment.

We cannot expect a system built for mortgages in the 1990s to meet the needs of modern fintech solutions. Nor can we continue to treat consumers as risk factors based on outdated categories. When credit assessments penalise responsible users and leaves lenders blind to real risk, the system itself becomes the source of instability.

We must shift our mindset. Credit reporting is not just a compliance function, it is core infrastructure. It underpins everything from interest rates to financial inclusion, product design to regulatory oversight.

We are at an inflection point. When credit reporting is broken, everyone loses. Lenders make poorer decisions. Consumers are excluded or overcharged. Innovation is stifled. As financial services become more embedded, decentralised, and personalised, the role of consumer credit data becomes more critical. We need to invest in infrastructure that reflects today’s behaviours, and tomorrow’s expectations.