In this exclusive CXO Digital Pulse interview, Ms. Jaya Vaidhyanathan, CEO of BCT Digital, shares transformative perspectives on the role of AI in reshaping risk, resilience, and trust across the BFSI landscape. From operationalizing AI-driven governance to redefining fraud detection at scale, her insights are a must-read for every financial leader navigating today’s complex risk terrain.
CXO Digital Pulse: As AI becomes integral to enterprise risk strategy, how can financial institutions reframe risk—not just as a compliance function, but as a source of competitive advantage?
Ms. Jaya Vaidhyanathan: In today’s rapidly evolving regulatory and business landscape, where stakeholder expectations continue to rise, financial institutions must reimagine risk as a strategic enabler rather than a cost centre or compliance function. This means shifting from reactive processes to proactive, intelligence-led frameworks that drive both resilience and growth.
AI accelerates this shift by delivering predictive insights, contextual risk scoring, and real-time decision-making. For instance, AI-powered early warning systems analyse massive volumes of transactional and behavioural data to detect early signs of financial stress across individual borrowers, sectors, or even macroeconomic trends long before defaults occur. This empowers banks to intervene proactively, restructure portfolios and significantly reduce the Non-Performing Assets (NPAs).
Similarly, AI-driven risk models can segment customers with far greater precision, allowing leaders to make risk-informed lending decisions, tailor credit products, and frame pricing strategies aligned with the institution’s risk appetite.
On the enterprise risk side, AI can analyse risk indicators and suggest appropriate risk controls based on (i) regulation, (ii) history of incidents and risks, and (iii) organisation’s risk policy framework. This holistic approach towards Enterprise Risk Management not only drives operational efficiencies and lowers cost, but more importantly, consolidates risk and presents it in an intuitive, unified view like never before.
AI isn’t just optimizing existing risk processes; it’s transforming risk into a source of competitive differentiation. Institutions that embrace this shift will lead not just in compliance, but in profitability through mitigation of financial risk, consequential capital provisioning, and deeper stakeholder trust.
CXO Digital Pulse: What does it truly mean to embed AI into operational risk frameworks—and how can BFSI leaders ensure these systems remain adaptive, explainable, and regulation-ready?
Ms. Jaya Vaidhyanathan: Embedding AI into operational risk frameworks means moving beyond surface-level adoption. It requires predictive, adaptive intelligence to be integrated into the core architecture of risk governance. In the BFSI space, this is essential to managing risks stemming from process failures, data inaccuracies, regulatory non-compliance, and third-party dependencies. When strategically deployed, AI helps detect these risks early, map root causes, and recommend corrective action, enabling institutions to shift from reactive controls to proactive resilience.
AI enables institutions to move beyond static checklists toward intelligent, real-time monitoring. For instance, transaction monitoring systems can now evolve beyond rule-based alerting (which fraudsters have increasingly learned to decode over time, for example, by breaking up suspicious transactions into smaller chunks below threshold limits). AI-driven approaches such as pattern matching and anomaly detection algorithms flag deviations from typical behaviour patterns in real-time, enabling institutions to detect and intercept fraud proactively. This shift transforms operational risk from a reactive control mechanism into a strategic resilience driver.
However, the sophistication of AI must be matched with strong model governance, defined ownership, routine validations, transparent assumptions, and audit-ready documentation.
CXO Digital Pulse: With traditional fraud detection reaching its limits, how can AI-led, real-time predictive models redefine trust, while maintaining precision and privacy at scale?
Ms. Jaya Vaidhyanathan: Fraud today is dynamic, complex, and no longer governed by static rule-bound system, instead it demands adaptive fraud detection mechanism. AI-led models can process vast volumes of structured and unstructured data, from transaction histories and geo-behavioural patterns to device-level signals, with much higher precision and speed. More importantly, they help minimize false positives, enhancing the customer experience and improving operational efficiency.
In this new environment, trust is no longer built solely on how much fraud is prevented but on how seamlessly and responsibly it is done.
Scaling AI-driven fraud detection must go together with responsible data stewardship. Federated learning, for instance, allows models to be trained across decentralized datasets, drawing insights without ever exposing sensitive customer information. Techniques such as anonymization and tokenization further ensure regulatory compliance while maintaining model performance. This balance between intelligence and responsible data use is critical to building secure digital ecosystems.
CXO Digital Pulse: As AI systems grow in autonomy and impact, what should boards and CXOs focus on to build accountable, future-proof governance structures for AI in finance?
Ms. Jaya Vaidhyanathan: AI is no longer just an enabler, it now influences strategic decisions, customer outcomes, and an institution’s overall risk profile. Boards and CXOs need to establish AI governance frameworks that anchors innovation and accountability.
Effective AI governance begins with ethical design principles, ensuring transparency, fairness, and explainability integrated across the AI lifecycle. Leadership must drive cross-functionality accountability across data science, compliance, legal, risk, and executive functions. Robust oversight mechanisms such as AI risk committees, periodic model audits, and escalation protocols are essential.
Data governance is equally critical. Ensuring data quality, mitigating bias, and enforcing privacy compliance are non-negotiables. As AI continues to evolve rapidly, governance frameworks must remain agile, capable of adapting to new technologies, regulatory shifts, and changing societal expectations.
Future-ready governance also means investing in AI literacy at the board level. CXOs must be equipped to ask the right questions, not just about how the AI works, but about its ethical, reputational, and systemic implications.
At its core, responsible AI governance is about enabling the sustainable and trustworthy adoption of financial ecosystem. Boards and CXOs who lead with foresight and accountability will be best positioned to harness AI’s full potential and build future-ready institutions.
