Adding transaction-based insight to European credit decisions

Every lending decision rests on a critical question: how can lenders build a more complete view of credit risk?

Traditional credit data answers it well, but often with a lag. Transaction-based intelligence adds a more current, behavioral view — helping European lenders decide with greater confidence, without replacing the tools they rely on.

Across Europe, lenders are operating in an environment shaped by a higher cost of borrowing, evolving consumer preferences, and the rapid rise of new pay-later options. Consumers increasingly expect fast, seamless credit decisions, while institutions must continue to lend responsibly. Balancing these priorities — growth on one side, prudent risk management on the other — has rarely been more demanding.²

A few features of the European market bring this into focus:

Each tells part of the same story. A predominantly debit market means many customers are effectively new-to-credit or thin-file; the surge in pay-later borrowing adds exposure that traditional data does not always capture; and resilient demand means lenders must keep growing while managing risk. Traditional credit data remains a critical foundation — but by its nature it describes largely what a consumer has done in the past. Transaction-based insights add a more current, behavioral perspective, and combining the two helps institutions make more informed decisions across the lifecycle.¹

Custom Credit Score (CCS) helps address the interval between changes in credit risk and when they may be reflected in conventional data. 

Part of the Consumer Credit Analytics family, CCS uses relevant transaction-based indicators to provide an additional spend-based indication of creditworthiness. Models are calibrated using a lender's portfolio and historical delinquency outcomes, providing an additional risk insight that can complement existing credit information and decisioning tools.¹

Where applicable, the customer provides the required information through the lender’s application, with data processed in accordance with applicable requirements and the agreed purposes of the service. The resulting score is delivered through an API and can be refreshed monthly, allowing it to integrate with a lender’s existing decisioning processes. It provides an additional risk input that institutions can use at relevant points across the customer relationship.¹

The value of transaction-based insight is its ability to provide a more current complement to traditional credit information. 

CCS can provide an additional risk input at relevant points across the lending lifecycle, from underwriting to credit line management, portfolio monitoring and collections. Used alongside a lender's existing data and approved decisioning tools, it can support more informed credit decisions without replacing established risk frameworks.

The value of a more current view is best seen in practice. Two Mastercard engagements — one in Europe, one in a comparable emerging market — show how a transaction-based score can widen access to credit and tighten risk control at the same time, always working alongside the systems already in place.

A Czech issuer wanted to grow its cash-loan business without loosening its grip on risk — but had limited visibility on new-to-bank and existing-to-bank applicants. Working with Mastercard, it built a Custom Credit Score combining its own delinquency history with behavioral signals from Mastercard transaction data. model demonstrated strong differentiation across delinquency risk levels and, crucially, reframed the reject pile: around one in ten previously declined applicants were identified as lower risk by the model, pointing to incremental revenue the issuer would otherwise have left on the table, all through a monthly, API-based score that fitted its existing flow.²

A leading Egyptian bank set out to cross-sell credit to its existing debit customers while keeping delinquency in check. Many had thin conventional records, so Mastercard built a model on the bank’s debit transaction data and linked loan performance, provisioned via API for new and existing cardholders. It concentrated the great majority of likely delinquents into the riskiest segment and roughly doubled the delinquency rate the bank could identify there — giving it a dependable way to support responsible credit growth within the bank’s existing customer base.³

Different markets, different products — but a consistent lesson: a more current, behavioral view does not simply reduce risk, it uncovers responsible growth that traditional screening alone would leave untapped.⁴

This is essential positioning. CCS is not intended to replace credit bureau scores, internal models, or established risk frameworks. It is a complementary intelligence layer that can enhance those inputs — adding behavioral context where traditional data may be less timely and helping lenders act with greater confidence.¹

It also works across customer relationships. For existing cardholders, CCS can inform credit-line management, collections, and relationship growth; for new prospects, it can strengthen underwriting decisions. In each case, the score is one considered input among several — used alongside a lender’s approved decisioning tools, never in isolation.¹