Monetizing Financial Data with AI Ethical Considerations and Business Strategies in the Era of Large-Scale Machine Learning - Rajesh Kamisetty (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The explosion of financial data created by digital transactions, consumer behavior analytics, and market intelligence has made new doors open for monetization through artificial intelligence (AI) and large-scale machine learning (ML). Fintech companies and banks are increasingly using AI-powered predictive analysis, algorithmic trading, fraud detection, and automated decision-making to drive profitability and fine-tune financial services. But, with the monetization of financial data, comes serious ethical considerations around data privacy, algorithmic bias, regulatory compliance and transparency. So in this paper discuss different business strategies for the use of AI-powered financial data, look at possible dangers of a risk associated with AI decision-making, analyze the effects of the new regulations taking effect, including the GDPR and CCPA. It further introduces strategic frameworks for deploying responsible AI, encompassing ethical AI considerations, XAI 1 and compliance mechanisms to drive sustainable and equitable monetization of financial data. By analysing business models, ethical challenges, and future research avenues, this will elucidate whether financial organisations can cultivate a balance of innovation, profit, and ethical compliance in the emerging AI-as-a-service finance culture.