Interpretable machine learning models for financial fraud detection using explainable AI Available to Purchase

Fraud detection is a challenging task that demands a high degree of transparency and accuracy. While they can achieve great performance, traditional machine learning models like neural networks (NN) and ensemble approaches are frequently challenging to understand and comprehend. The field of "explainable AI" (XAI) seeks to provide techniques and instruments that can offer reliable, intelligible justifications on behalf of choices and actions of machine learning models. In this case study, we illustrate how to use XAI to detect financial transaction fraud. We evaluate the result and explain ability of several interpretable machine learning (ML) models. Including rule-based systems, decision trees, and linear models, using a real-world dataset of credit card transactions. Additionally, we show you how to apply other XAI methods as feature significance, local explanations, and acceptances to learn more about the facts and the models. We demonstrate how XAI can improve the fraud detection process by boosting stakeholder confidence and trust, including auditors, regulators, and customers.