By: Zach Miller

Srinivasarao Paleti is an experienced consultant who combines banking expertise with AI tools. With over fifteen years at Tata Consultancy Services, he’s worked his way up from system engineer to assistant consultant, building deep knowledge in quality assurance, risk compliance, and digital banking. Srinivasarao leads efforts in fraud detection, AML, and AI-driven financial decision-making.

As a tech expert and researcher, he has more than fifteen published papers, two patents, and multiple speaking engagements. His work focuses on how AI can make banking safer, faster, and smarter. From credit scoring to digital payments, he’s helping shape the future of finance. In this interview, Srinivasarao shares insights from his journey, explains the impact of agentic AI, and offers a glimpse into how data and automation are transforming the financial world.

Q1: Srinivasarao, thank you for being with us today. You have a distinguished career spanning over 15 years at Tata Consultancy Services and a strong foundation in both telecom and banking. To what extent have your diverse roles shaped your current vision for AI-driven transformation in risk and compliance?

Srinivasarao Paleti: Thank you. My journey over 15 years at Tata Consultancy Services—spanning both telecom and banking—has had a significant influence on shaping my vision for AI-driven risk and compliance. Telecom taught me how to work with high-throughput, real-time systems, while banking grounded me in regulatory rigor and financial responsibility. These diverse experiences gave me a dual perspective: one that values speed and automation, and another that prioritizes compliance and transparency. As a result, my current focus is on developing adaptive, explainable AI systems that blend real-time analytics with auditable frameworks, helping institutions to stay ahead in both innovation and regulation.

Q2: In your paper, “Adaptive AI in Banking Compliance: Leveraging Agentic AI for Real-Time KYC Verification, Anti-Money Laundering (AML) Detection, and Regulatory Intelligence,” you explore how agentic AI transforms traditional compliance functions. How does this adaptive approach enable institutions to better work through the rapidly changing regulatory expectations? Can you share an example of real-time AI decision-making outperforming static compliance workflows?

Srinivasarao Paleti: Agentic AI enables systems to act semi-autonomously—learning from data in context and adjusting responses without waiting for static rule updates. In compliance with regulatory environments that are constantly evolving, this approach is particularly valuable. For instance, in a real-time KYC scenario, a static rule might flag a transaction based on a hard-coded threshold. An adaptive agentic AI model, however, evaluates behavioral patterns and contextual anomalies, enabling earlier identification of risks like identity layering. I’ve worked on projects where such AI models intercepted suspicious behavior within seconds—something that traditional systems would have missed or delayed. This ability to learn and respond in real time can significantly enhance compliance agility.

Q3: With over 50 peer reviews and 15+ published research papers, you clearly focus on innovation. What is your procedure to ensure that your research stays technically sound and relevant to the real-world challenges faced by financial institutions, especially when it comes to implementing AI at scale?

Srinivasarao Paleti: My research methodology is grounded in three principles: relevance, rigor, and review. I stay closely connected to the industry by engaging in consulting and advisory roles, where I encounter the frontline challenges financial institutions face. I integrate these insights into my research agenda. Each paper I publish goes through intensive peer review—over 50 so far—and is stress-tested against both academic standards and business feasibility. I also maintain a feedback loop with practitioners to ensure that the models and frameworks I propose are scalable, secure, and implementation-ready for large-scale financial systems.

Q4: In your paper, “AI-Driven Treasury Management: Reinforcement Learning Models for Liquidity Optimization and Fraud Prevention in Large-Scale Financial Institutions,” you discuss applying reinforcement learning to traditionally conservative treasury operations. What safeguards or frameworks do you recommend to ensure such AI systems remain auditable and risk-conscious, especially when optimizing liquidity across volatile market conditions?

Srinivasarao Paleti: Reinforcement learning (RL) in treasury must be treated with caution, given its high impact. I recommend a three-layered safeguard framework. First, simulate extensively in sandbox environments to model volatility and tail risks. Second, build policy constraints into the learning algorithm to prevent unsafe exploration. Third, ensure explainability and auditability through post-decision logging, model versioning, and decision traceability. In my paper, I advocate for “digital twins” of treasury operations—allowing RL models to train safely before live deployment. These safeguards are designed to ensure that even in high-volatility markets, AI decisions remain compliant and traceable.

Q5: You have combined your professional experience with deep research and hands-on consulting. What roles do quality assurance and agile methodology play in translating theoretical models into compliant, real-world financial solutions that banks can trust?

Srinivasarao Paleti: Both are critical. Agile methodology brings flexibility and speed to AI model development, enabling iterative improvements and real-time stakeholder feedback. Quality assurance, on the other hand, enforces discipline, ensuring each model meets compliance, accuracy, and ethical benchmarks before release. I’ve led AI deployments where QA checkpoints were embedded at every sprint phase—covering data integrity, model bias tests, and compliance validations. This hybrid approach of agility plus discipline helps ensure banks receive solutions that are not only cutting-edge but also dependable and regulator-ready.

Q6: As someone who has also authored two books and holds two patents, how do you see the intersection of intellectual property and AI innovation playing out in the banking sector, particularly with the increasing demand for transparent and explainable AI models?

Srinivasarao Paleti: The intersection of IP and AI is becoming a strategic frontier for banks. As AI models become core to decision-making, owning intellectual property, such as explainable frameworks or risk control mechanisms, offers both commercial and compliance advantages. My patents focus on auditable, modular AI components, ensuring transparency without sacrificing performance. Given increasing regulatory expectations for explainability, like the EU AI Act or Basel’s AI guidance, banks must now innovate in ways that are both proprietary and transparent. This balance between IP protection and ethical openness is likely to shape the future of AI in finance.

Summary

Srinivasarao Paleti’s journey shows what’s possible when tech, experience, and curiosity come together. His deep understanding of banking and AI is backed by years of hands-on work and research. As AI continues to change how we manage money, Srinivasarao believes banks need to adapt fast. But more than that, they need to use tech wisely: balancing automation with trust, and speed with security. Through his work, he’s addressing today’s problems while also preparing for tomorrow’s challenges.

For young professionals and industry leaders alike, his advice is to keep learning, stay curious, and use technology to help make finance better for everyone.