Adaptive Credit Scoring: A Dynamic Elo-Based Model for Real-Time Business Risk Assessment

Abstract

This paper explores how modern technology and innovation can enhance the credit scoring system. This paper aims to provide a mixture of mathematical and programmable solutions based on the Elo algorithm. The Elo algorithm, widely used in chess, dynamically ranks players based on real-time performance.Through my observations, I realized that this algorithm could be applied in finance to provide businesses with a dynamic credit score, updated quarterly, monthly and daily. In this paper there would be various mathematical proofs, python applications and the discussion of limitations to this system, however this paper is aimed to provide a proof of concept. Based on a logical assumption, this algorithm can help financial institutions reduce their risk in terms of investment and it also promotes more careful decision making among businesses to ensure a good score is maintained.