Generating and Defending Against Adversarial Examples for Loan Eligibility Prediction

In this paper, we investigate the critical problem of generating and defending against adversarial examples in the context of loan eligibility prediction. Adversarial examples are deliberately crafted inputs that are designed to deceive machine learning models, and they pose a significant risk to the accuracy of loan approval decisions. We explore a variety of adversarial attacks, such as poison and evasion attacks, and their potential implications for loan eligibility prediction models. The Fast Gradient Sign Method (FGSM) is proposed as a technique to generate adversarial examples, and its effectiveness is evaluated on a variety of loan eligibility prediction models. Additionally, we assess the efficacy of different defense strategies to counter adversarial attacks. Our findings highlight the alarming effectiveness of adversarial attacks in causing model misclassifications, and they establish FGSM as a powerful tool for generating adversarial examples. Furthermore, our research underscores the importance of implementing robust defenses to mitigate the impact of adversarial examples in loan eligibility prediction. It is our hope that our work will stimulate further research into this important area of machine learning security as a valuable contribution to this field.