As India approaches the Union Budget 2026, artificial intelligence (AI) has emerged as a defining force in the evolution of the financial sector. According to Mr Prateek Kithania, AI is no longer an experimental technology in finance but a structural shift influencing how institutions manage risk, capital, and long-term strategy. Mr Prateek Kithania notes that the role of AI has expanded well beyond automation. Financial institutions today are deploying advanced AI systems for real-time forecasting, compliance monitoring, fraud detection, and scenario-based financial planning. This shift is transforming finance teams from operational units into strategic decision-making partners.

AI Reshaping Financial Decision-Making The growing use of predictive analytics and machine learning is enabling financial institutions to move away from static annual planning cycles. Continuous forecasting models now allow businesses and banks to respond dynamically to market volatility. As highlighted by Prateek Kithania, such capabilities are particularly critical in an environment marked by global uncertainty and rapid economic change.

AI-driven compliance and fraud detection systems are also becoming essential. These tools can analyze massive volumes of transactions, identify suspicious patterns, and adapt to emerging risks far faster than traditional systems. Mr Prateek Kithania emphasizes that this not only improves efficiency but also strengthens trust and transparency across the financial ecosystem. Key Challenges in Adoption Despite increasing investments, AI adoption in finance faces notable challenges. Data quality remains inconsistent across institutions, limiting the effectiveness of intelligent systems. Skill shortages within finance and risk teams further slow implementation. Regulatory clarity around AI accountability and explainability also remains a concern.

According to Prateek Kithania, organizations that treat AI as a strategic capability — supported by governance frameworks and skilled talent — are far more likely to realize measurable outcomes compared to those viewing it as a short-term technology upgrade.

Union Budget 2026: A Critical Opportunity The Union Budget 2026 presents a timely opportunity to accelerate responsible AI adoption in finance. Mr Prateek Kithania believes the budget should focus on long-term enablers rather than isolated incentives.

Priority areas include strengthening digital public infrastructure, expanding AI-focused skilling initiatives, and introducing clear regulatory frameworks that balance innovation with accountability. Strategic budgetary support in these areas can significantly enhance efficiency, financial inclusion, and public finance management.

Balancing Innovation and Responsibility As AI systems increasingly influence credit decisions, market behavior, and regulatory processes, responsible deployment is essential. Ethical safeguards, auditability, and human oversight must evolve alongside innovation. Prateek Kithania argues that a supervised and transparent approach to AI adoption will be critical to maintaining financial stability.

Conclusion AI is no longer optional for the financial sector — it is a strategic necessity. As India looks ahead to the Union Budget 2026, policy choices will play a defining role in shaping how AI integrates into the country’s financial architecture.

As Mr Prateek Kithania concludes, the future of finance will belong to institutions and economies that adopt AI with clarity, discipline, and long-term vision — transforming technology into a driver of resilience, efficiency, and inclusive growth.

(Disclaimer: The above press release comes to you under an arrangement with NRDPL and PTI takes no editorial responsibility for the same.). PTI PWR

AI in finance should be supported by budget measures prioritising infrastructure, skilling, and accountable regulation. The Union Budget 2026 should accelerate responsible AI integration in finance by prioritising long-term enablers: strengthening digital public infrastructure, expanding AI-focused skilling initiatives, and introducing clear regulatory frameworks that balance innovation with accountability. Effective adoption also requires addressing data quality and skill shortages, and embedding ethical safeguards, auditability, and human oversight to preserve trust, transparency, and financial stability.