Gini calculation and rule performance interpretation

The Gini coefficient is a widely used measure of income inequality within a population. This paper investigates the application of the Gini coefficient concept to calculate fraud risk based transaction amount losses. The paper is essentially divided into two parts: (1) Interpretation and implementation of Lorenz curve in calculating fraud risk in a real-time scenario for online e-commerce merchants/customers for various industries. Implementing Gini is useful for verifying fraud-based metrics or saving fraud losses after a machine learning model has been implemented and deployed in production. (2) The rules automation system is helpful in optimizing and improving strategies required for deployment on a real-time transaction based platform in the financial services world where the real-time decisioning system for a customer on board plays an important role. The study concludes that Gini coefficient remains a robust tool for evaluating risk based losses, thus helping financial institutions make informed strategy-driven decisions, and further also explores the business rule sets that help to optimize the strategic decisions.