As we step into 2025, the fintech landscape in India is undergoing a profound transformation driven by the integration of Artificial Intelligence (AI) and Machine Learning (ML). These technologies are not just enhancing operational efficiencies but are fundamentally reshaping decision-making processes across various fintech applications. With a projected compound annual growth rate (CAGR) of 20.5% for AI in the financial sector from 2023 to 2028, it is imperative to understand how these advancements are influencing the industry.The Evolution of AI and ML in FintechAI and ML have become integral to fintech operations, enabling companies to process vast amounts of data quickly and accurately. This capability allows for real-time decision-making, which is crucial in an industry where timing can significantly affect outcomes. According to a report by YourStory, AI investments in India surged over sixfold during the second quarter of FY25, reaching $51 million across 20 funding rounds, indicating a robust interest in AI-driven solutions within the fintech sector .Key Applications of AI and ML in Decision-Making1. Fraud Detection and Prevention:One of the most critical applications ofAI in fintechis fraud detection. Advanced machine learning algorithms analyze transaction patterns to identify anomalies indicative of fraudulent activity. AI processes millions of transactions daily, detecting fraud patterns that save billions annually while ensuring a seamless user experience. In India, fintech companies are increasingly adopting similar models to enhance security measures and protect consumer data.2. Credit Scoring and Risk Assessment: Traditional credit scoring methods often fail to capture the complete financial picture of potential borrowers. AI-driven models leverage alternative data sources—such as social media behaviour and transaction history—to provide more accurate credit assessments. A study indicated that Indian fintech firms are moving towards integrating these models to broaden access to credit for underbanked populations while minimizing risk for lenders .3. Personalized Financial Services: Hyper-personalization is another area where AI excels. By analyzing customer data and behaviour, fintech companies can offer tailored financial products that meet individual needs. For example, robo-advisors powered by AI can create customised investment strategies based on a user’s financial goals and risk tolerance. The Freshworks AI Workplace Report highlights that Indian firms plan to increase their AI spending by an average of 41% in 2025—the highest growth rate globally—indicating a strong push towards personalized services .4. Operational Efficiency through Automation: Robotic Process Automation (RPA), when combined with AI, automates repetitive tasks such as data entry and compliance checks. This integration significantly reduces operational costs and improves efficiency across various functions within fintech organizations. As reported
Ven. Gen 17th, 2025
