AI Neural Networks In Credit Risk Assessment: Redefining Consumer Credit Monitoring And Fraud Protection Through Generative AI Techniques

Abstract

Today, the acceptance of AI and deep learning models is increasing rapidly to learn and make complex predictions from large volumes of data. The primary aim of this research is to embrace and implement innovative generative AI techniques in creditor financial institutions for consumer credit monitoring and fraud detection. We create a context of the current paradigm, emphasizing the need to re-investigate and redefine the robustness of the traditional outcomes against the state-of-the-art deep learning models in assisting decision-makers in quantifying and improving credit risk estimation. Changes in lending scenarios, such as regulatory excellence, provide an opportunity to shift and utilize deep learning models to validate current practices of origination policy to control credit risk. The research discusses new-age algorithms and their applications.
AI models and behavior digitization processes, along with the proposed Generative Adversarial Networks, are blooming tools for new-age researchers to provide future estimates of how credit behavior would alter. It is an FMCG adoption of the non-FMCG retail credit process with added flavors of generative AI models to read consumer minds. The new credit world, based on GANs, changes the rules of the credit game. The GAN provides boosters, eVouchers, and spending limits for credit cards. At the same time, they are 'minimally' capable of understanding when you would turn against paying bank dues. Similarly, reinforcement learning using Actor-Critic Networks is proposed over AI misbehavioral supply chain techniques to highlight "do's" and, most importantly, conclude "don'ts" for an effective policy framework on retail assets. Regulatory concerns and ethical issues of employing a suggestive adversarial framework are discussed to keep abreast of the ethical usage of neural networks, predominantly on black box applications.