Secure Loan Approval System Using Explainable Boosting Machine (EBM) for Fairness and Transparency

This new technique will simplify and trust automated financial decision-making. The model employs interpretable machine learning to ensure fair, open, and ethical lending. Old-fashioned, confusing machine learning approaches used to determine loan eligibility would be prejudiced. Unfair consequences will result for certain groups. This makes it harder to follow the regulations, gain consumers' confidence, and employ AI morally. FTLAF (Fair and Transparent Loan Approval Framework), a fair and open loan approval procedure using Explainable Boosting Machine (EBM), safe data storage, and fair pre-processing, addresses these difficulties. EBM, a basic glass-box model, and biasreduction strategies like disparate impact remover and data anonymization may protect users' privacy and fairness during decision-making. The FTLAF, which takes effect shortly, will ensure fair, straightforward, and safe bank lending choices. Auditor, regulator, and applicant stakeholders would easily grasp and analyze the decision-making process's fairness rules using the framework's interpretable feature contributions and rule-based reasoning. It will compare the framework to fairness. The experimental findings show that FTLAF improves fairness criteria like equal opportunity and demographic parity while retaining competitive precision. Modern financial businesses that want to responsibly employ AI will do this. It encourages transparency without hurting performance, which is moral and practical.

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