Legal and Ethical Frameworks for AI in Financial Services: Balancing the Innovation with Responsibility and Effective Protection

The dawn of artificial intelligence (AI) is transforming the financial services industry, providing massive efficiencies and unlocking innovation and personalized capabilities. However, despite its benefits, AI in financial services raises major legal and ethical issues, which this study seeks to address. Responsibility for the decisions and actions driven by AI in financial services by augmenting AI's legal and ethical accountability can guarantee financial leverage. The study endeavours to evaluate the ethical and legal aspects of artificial intelligence in finance by providing global insights; however, the primary focus is on India. The study hinges on the regulation of artificial intelligence and considers whether current data protection laws like the Digital Privacy and Digital Protection (DPDP) Act, the General Data Protection Regulation (GDPR), and GDPR + EU AI Act, or the Federal Trade Commission (FTC) guidelines work to mitigate such risks (algorithmic bias/misuse of personal information generally aimed at gaining a competitive edge at the cost of invasion into privacy). Ancillary instruments may be useful tools within these broader regulatory remits; however, there is also the argument that they fail to be precise enough about what algorithmic elements are determinants/which ones must always sit with human review. Through a comparative and exploratory analysis, the study identifies the understating and overlapping of India's legal frameworks with relevant international norms. We consider ethical issues, such as fairness and transparency in AI systems and accountability principles, to seek a balance between finance and innovation with an emphasis on the protection of individual rights. The paper concludes with several recommendations that recognize the need for legislation ramifications by amending India's competition law. Although India is stepping forward in its regulation efforts, there remains a lot of work to be done in terms of drawing parallels with global benchmarks so that AI innovation is accompanied in a way that effective protection and responsibility are maintained.