# Credit risk scoring

> Credit risk scoring estimates how likely an applicant or customer is to miss payments, using a model that learns from past data. This page describes Corrplus's approach rather than a client case study: we first measure whether your data is enough to score from, then build a model that is explainable, auditable and leaves the decision with a person.

URL: https://corrplus.net/solutions/credit-risk-scoring/
Updated: 2026-10-22
Corrplus Team

## Symptoms

- Applications or dealer limits are judged on experience and a handful of rules.
- Different analysts reach different decisions on similar profiles.
- Assessment takes longer as application volume grows.
- Customers who defaulted look predictable in hindsight.
- Explaining why a decision was made, to an auditor or a customer, is hard.

## What waiting costs

Inconsistent decisions mean both bad debt and good customers turned away for no reason. Decision logic that is not written down is the hardest thing to explain in an audit.

## Our approach

- **Data sufficiency**: We combine past applications, the information known at decision time and later payment behaviour, and report whether it is enough to model.
- **Explainable baseline**: We first build a scorecard based on logistic regression, where each variable's effect can be read directly.
- **Comparison and fairness testing**: More complex models are compared on the same data, and we test for systematic differences between customer groups.
- **Connect to the decision flow**: The score reaches the analyst's screen with its reasons; thresholds and exceptions follow your credit policy.
- **Monitoring and documentation**: Performance and data drift are tracked, and the model, its data and its decisions are documented for audit.

## What to measure

- Assessment time per application
- Consistency of decisions on similar profiles
- Share of overdue receivables
- Share of decisions with a documented rationale

## What do you need before you start?

A credit risk model learns from the outcomes of past decisions, so the first question is data, not the model. For each application you need the information known at decision time and how the customer paid afterwards, in the same record. If information created after the decision leaks into the model, it looks good in testing and fails in practice.

The second difficulty is outcomes you never see. You cannot know how rejected applicants would have paid. The model learns only from accepted ones, which can skew its results. We do not ignore this; we state it in the data sufficiency report.

The framework comes from outside the model. In Türkiye, banks and finance companies work under the rules of BDDK, the banking regulator; in the EU, the EU AI Act and GDPR apply; companies giving trade credit to dealers follow their own credit policy. Where an institution has access to credit bureau data, such as KKB in Türkiye, it can feed the model too.

## What each model type gives you

| Approach | Explainability | Data needed | When it fits |
|---|---|---|---|
| Rule-based policy | Full, rules are written down | Low | Little data, simple product |
| Scorecard (logistic regression) | High, each variable's points are visible | Medium | Audit and explanation matter |
| Gradient boosting (XGBoost, LightGBM) | Explained with extra methods (SHAP) | High | Many variables, rich data |

We start every project with a scorecard. A more complex model is recommended only if it does meaningfully better on the same data and its reasons can still be explained.

## How we start

The first step is a data sufficiency study within an [AI consulting](/services/ai-consulting/) engagement. You get a written report on what your data shows, what is missing and whether a model is worth building. If the answer is no, we say so and recommend what to start recording so the data can support a model later. Personal data processing, automated decisions and fairness testing are handled together with our [AI governance and compliance](/services/ai-governance/) work. The model runs on your own servers or on [cloud infrastructure](/services/cloud-infrastructure-scalable-platforms/) you approve, where data must stay put.

## Frequently asked questions

### How much data do we need?

We do not quote a fixed number, because it depends on the product and customer profile. What matters is enough applications with known outcomes, and especially enough examples of missed payments. The data sufficiency study measures this first.

### Does the model make the credit decision?

No. The model produces a score and its reasons; the decision stays with the analyst under your credit policy. GDPR and Türkiye's data protection law, KVKK, give people rights around decisions based solely on automated processing, so we design the flow with your legal counsel.

### How does the EU AI Act affect this?

The EU AI Act lists creditworthiness assessment of individuals as a high-risk use, with duties on data quality, documentation, human oversight and monitoring. If it applies to you, we build those in from the start; the regulatory reading stays with your compliance team.

### Is this only for banks?

No. Manufacturers and distributors selling on credit to dealers or business customers can use the same method for credit limits and payment terms. The data usually comes from payment history in the ERP.

### Which data is never used?

Special categories of personal data such as health, religion or ethnic origin, and variables that act as proxies for them, are kept out of the model. Which variables may be used is decided with your legal counsel and documented.