CaSE STUDY: A Leading BNPL Platform in Europe.

An over-the-top Buy Now, Pay Later platform letting consumers shop at almost any retailer — using mobile-first technology to help them manage cash flow and access credit responsibly.

INDUSTRY
Buy Now, Pay Later / Alternative Finance
BUSINESS MODEL
Over-the-Top Payments Platform
REGION
Europe
CUSTOMERS
Mobile-first, multi-retailer shoppers
PRODUCTS
Point-of-sale BNPL, short-term credit
CHANNEL
Reduce early-stage defaults without adding friction at checkout or lowering approval volume

THE CHALLENGE

Operating in a high-velocity credit environment, the client faced mounting pressure to refine their short-term risk assessment

problem 1
Repayment Risk
The portfolio was experiencing a 12% bad rate on First Payment Default (FPD30).
problem 2
Fraud Vulnerability
Identifying fraudulent or high-risk applications at the point of entry was becoming increasingly complex.
problem 3
Balanced Growth 
The primary goal was to reduce these early-stage defaults without friction or lowering the existing approval volume.

Why Incomplete Data Still Creates Growth Barriers for Lenders

To grow responsibly, lenders need a broader and more predictive view of applicant risk. Even with alternative data sources in play, many current models are still missing one critical layer: behavioural data that reveals true intent and repayment capacity.

Credit bureaus provide broad coverage of consumers. However, not every repayment behaviour is consistently reflected in bureau data, particularly for newer credit products such as BNPL.

Credolab adds an independent behavioural signal that can be generated for virtually every checkout applicant, helping lenders make decisions alongside traditional bureau information.
Traditional Bureau Coverage
85-100%
BNPL Repayment Visibility to Bureaus
Limited / Market-dependent
Sources: Macroeconomic Analysis of Credit Bureau Coverage, Mobile Device Proliferation, and Emerging Consumer Credit Vulnerabilities in Europe (2026); Buy now, pay later: a cross-country analysis (Bank for International Settlements, 2023

THE SOLUTION

Credolab Added an FPD30 Behavioural Risk Layer to the Existing Decision Engine.

STAGE 01
Acquisition
Checkout & retailer integration
STAGE 02
Onboarding
Identity, KYC, SDK init
STAGE 03 · CREDOLAB
Decisioning
FPD30 behavioural
risk model
STAGE 04
Disbursal
Order funding &
merchant payout
STAGE 05
Servicing
Repayment & portfolio
monitoring

IMPLEMENTATION

Credolab was integrated into the client's decisioning workflow and delivered measurable
business impact within the first benchmarking cycle.

Historical data review

The client's existing portfolio and performance data were
analysed to establish a benchmark.

Signal generation

Credolab generated behavioural and device-based risk signals from applicant data.

Model validation

The enhanced model was tested against the client's current decisioning approach.

Cut-off optimisation

Approval strategies and score thresholds were refined to match risk appetite.

Deployment

The solution was integrated into production decisioning
flows.

PERFORMANCE DETAIL

Side-by-side comparison of bureau-only baseline vs. bureau plus Credolab on the
same population, same observation window.

Metric
Before
After
Default Rate
12%
8.8% (−27%)
Gini
39
45 (+6)
Approval Rate
79%
81% (+2.5%)
Visibility beyond traditional credit data
Stronger thin-file assessment
Richer data variables and feature engineering
Improved predictive power and model lift
Real-time decisioning support
Fraud signal detection via device intelligence

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