AI in Credit Scoring: Developing Fair and Transparent Models for Assessing Loan Eligibility

Credit scoring is employed by the financial institutions to compute the credit rating of the people and the eligibility for the loans and credit dangers. However, credit scoring models includes challenges like biases, lack of interpretability, and inadequate accuracy, which becomes clear in dealing with unbalanced or missing records. They can cause biased credit decisions. Based on these challenges, we introduce Auto encoder-GAN Hybrid Model which helps to create a transparent and non-discriminatory credit scoring and loan eligibility model. The problem of data imbalance is solved using auto encoder-based feature learning and data augmentation using an advanced Generative Adversarial Network (GAN). Thus, the accuracy observed in Autoencoder-GAN was 99.3%. The use of GAN is to generate synthetic data in the model increases its capability to mean fair across samples, thereby providing a good balance of samples for every demographic group. Finally, this paper aims to demonstrate that the integration of auto encoder and GANs is an effective solution for credit scoring and also address fairness and interpretability issues with high accuracy. The proposed model is applicable to real-world financial systems by enhancing capacities of risk assessment and loan eligibility fairness as compared to the existing methods and models it can play a part in enhancing the smarter and more efficient decision making in banking and financial sector.