Credit Risk Management Solutions

We work with organizations that extend credit, including banks, consumer finance companies, fintech lenders, buy-now-pay-later providers, leasing companies and businesses offering trade credit. The scope ranges from scorecards for a single digital lending product to expected credit loss engines covering several portfolios. Our approach is adapted to your size, regulatory environment and data maturity rather than applying a one-size-fits-all framework.

Yes. We support expected credit loss implementations including portfolio segmentation, PD, LGD and EAD modeling, staging and significant increase in credit risk criteria for IFRS 9, lifetime loss methods for CECL, macroeconomic scenario integration and management overlays. We work with your finance, risk and audit stakeholders and accounting advisors, and deliver documentation and reconciliations that support review by your auditors.

As a general guide, models need enough historical applications or accounts with observed outcomes over a defined performance window, including a sufficient number of defaults or bad outcomes. When history is limited, for example for a new product, we can use expert-based scorecards, external bureau data, proxy portfolios or simpler segmentation as a starting point and replace them with statistical models as data accumulates.

We use machine learning where it adds measurable value and can be explained appropriately, for example in fraud detection, early warning or as a challenger model. For many regulated uses, transparent methods such as logistic regression scorecards remain the preferred choice because they are easier to validate and explain. We always compare approaches openly and document the reasons for the final choice.

We work inside your environment wherever possible, apply least-privilege access, encryption and audit logging, and use pseudonymized datasets for model development when full identifiers are not needed. Engineer access is individually named and revoked at the end of the engagement. We follow your information security and data protection policies and can support data protection impact assessments where required.

A scorecard development project usually takes two to four months from data preparation to a validated model, with integration into a decision engine following. An expected credit loss engine implementation commonly takes four to six months, depending on the number of portfolios and the state of risk data. We plan around your reporting calendar and validation cycles and provide weekly written progress updates.