While the principles by Thomas et al. hold true, the "application" side is evolving. Modern scoring now includes:
How can a data science team apply L.C. Thomas’s principles today? Here is a checklist derived from his lectures (available on YouTube from the Credit Scoring and Credit Control conference in Edinburgh, which Thomas co-founded).
“Credit Scoring and Its Applications” is the authoritative reference for the mathematical and operational research foundations of credit scoring. It excels in behavioral scoring, reject inference, and survival analysis—topics most applied books ignore. However, its dated examples, lack of code, and thin coverage of deep learning and algorithmic fairness prevent it from being the single go-to text for modern data scientists.
For a cutting-edge practitioner, the book feels at publication—and more so now.
: Scoring is used to predict which customers will be most profitable, not just which ones are least risky. Public Policy
In developing economies, traditional credit data is scarce. The industry is aggressively adopting the "applications" logic but with new data. For instance, Experian India launched the "Grameen Score" specifically for rural borrowers, leveraging diverse data points like repayment patterns on microloans and migration trends to offer a holistic view. Similarly, Kenyan startup PEMiG acts as a credit intelligence platform specifically for African lenders, helping people with no formal credit history access loans. Furthermore, South Africa’s ADMiT now predicts an applicant’s willingness to repay based on alternative data, mitigating decisioning risks for lenders in environments with no bureau data.
Thomas identifies two fundamental decision points that lenders face when managing risk:
Before Thomas, credit scoring was mostly (should we lend at application?). Thomas championed behavioral scoring , which uses a borrower’s transaction and payment history over time to predict future risk.