In this exclusive interaction, Mr. Anil Chawla explains why compliance, explainability, and trust have become the defining challenges for agentic AI adoption in the BFSI sector. He highlights how Verint’s AI-powered interaction analytics enable transparent, auditable CX automation—helping financial institutions scale innovation while meeting stringent regulatory and governance expectations.
IT Voice– Why are compliance and trust emerging as the biggest barriers to the adoption of agentic AI in the BFSI sector?
Anil Chawla- For BFSI organisations, the challenge with agentic AI is not the technology itself, but compliance, trust and accountability. Financial institutions operate in highly regulated environments where decisions must be explainable, traceable, and defensible to regulators, auditors, and customers.
Agentic AI systems can act autonomously across workflows, which introduces inherent risk if decision logic is unclear or difficult to audit. This risk is often amplified when institutions rely on generic AI or agentic frameworks, which are designed as broad platforms rather than fit-for-purpose BFSI solutions, requiring significant additional layers of governance, security, and controls. As these systems begin influencing customer interactions, compliance processes, and risk-related outcomes, the absence of transparency and governance becomes a major adoption barrier across the industry.
BFSI organisations are far more willing to adopt and scale agentic AI when it operates within clear governance frameworks, with built-in transparency and human oversight. From Verint’s perspective, responsible AI adoption in financial services depends on embedding explainability, auditability, and control directly into AI-driven workflows—so institutions can innovate confidently without compromising regulatory trust. Verint’s approach to AI reflects broader industry concerns about responsible deployment. We have developed privacy-focused tools and emphasize the need for human oversight alongside AI automation—principles that align with the trust and compliance requirements emerging as adoption barriers across the BFSI sector.
IT Voice– How can interaction analytics help make AI-driven decisions more explainable, transparent, and auditable for regulated financial institutions?
Anil Chawla- As BFSI institutions accelerate the use of agentic AI to automate decisions across customer service, collections, and operations, the industry is moving beyond experimentation toward AI systems that directly influence customer outcomes and financial decisions. Industry bodies have highlighted that AI-driven CX and chatbot deployments in India are now delivering measurable ROI at scale, particularly in BFSI—while also surfacing new challenges around governance, transparency, and control as automation deepens. A survey of 500+ Indian CXOs shows that ~60% believe AI will disrupt their business within three years, yet only ~25% have deployed AI solutions, underscoring both rapid interest and the execution gap that makes governance critical in BFSI. In highly regulated environments, the ability to trace why an AI system acted—not just what it did—has become as critical as efficiency gains.
This is where Verint comes in as our Interaction Analytics provides the audit trail that regulated institutions need to trust AI-driven decisions. Verint’s AI-powered interaction analytics analyse 100% of customer interactions across voice and digital channels, creating a consistent, verifiable record of customer intent, agent behaviour, and outcomes.
By converting unstructured conversations into structured insights—such as intent, sentiment, compliance indicators, and behavioural patterns—Verint enables institutions to trace AI-driven actions back to specific interactions and contextual signals, rather than relying on opaque model outputs. Unlike framework-led AI implementations that require additional tooling to achieve explainability, this capability is inherent to Verint’s fit-for-purpose BFSI solutions.
This shift from sample-based monitoring to full-coverage analysis has been critical in real-world BFSI environments. In deployments such as Bank of Baroda, Verint’s interaction analytics helped move quality and compliance from partial visibility to complete transparency—supporting explainability, audit readiness, and stronger governance at scale.
IT Voice– What are the key risks BFSI organisations face when deploying autonomous or agentic AI systems without robust governance and oversight frameworks?
Anil Chawla- As BFSI leaders closed out the year, regulators and industry bodies increasingly underscored that explainability, auditability, and accountability are non-negotiable for agentic AI systems—particularly in financial services, where autonomous decisions can directly affect customers’ money, rights, and trust. As AI systems evolve from advisory tools to self-directed actors, the absence of strong governance foundations significantly raises risk across the industry.
Without robust governance and oversight, agentic AI can introduce material regulatory, operational, and reputational risks. Systems that cannot clearly explain why a credit application was approved, why a transaction was flagged, or how a customer interaction was routed create transparency gaps that are difficult to justify to regulators and customers alike—undermining compliance confidence and eroding trust.
Operational risk also increases at scale. In high-volume environments such as contact centres or digital service channels, even small decision errors—whether related to intent detection, compliance logic, or escalation rules—can quickly propagate across thousands of interactions if left unchecked.
From Verint’s perspective, this is why governance, visibility, and continuous oversight are essential to responsible agentic AI adoption. When auditability and explainability are missing, organisations often struggle to defend AI-driven outcomes, leading CIOs and BFSI leaders to slow down or pause deployments altogether. In regulated industries, strong governance frameworks are no longer optional—they are a prerequisite for scaling agentic AI safely, confidently, and at enterprise scale.
IT Voice– How do Verint’s AI-powered interaction analytics and compliance solutions enable BFSI firms to scale CX automation while mitigating regulatory, operational, and reputational risks?
Anil Chawla- Verint enables BFSI organisations to scale CX automation within a governance-first framework. Through its AI-powered interaction analytics, Verint continuously monitors every customer interaction for quality, compliance, and risk indicators—rather than relying on manual or sample-based reviews.
Powered by Verint Da Vinci™ AI, the platform follows a “glass-box AI” approach designed for transparency and auditability. Compliance checks, policy adherence, and customer-treatment standards are embedded directly into workflows, with clear visibility into how insights and recommendations are generated.
In large BFSI deployments, this approach has delivered measurable outcomes. In the Bank of Baroda engagement, Verint’s Quality Bots and Speech Analytics helped improve quality scores to over 90%, raise Net Promoter Scores above 50, and strengthen compliance confidence—demonstrating how CX automation can scale without increasing regulatory or reputational risk.
IT Voice– From your perspective, what should responsible, enterprise-grade agentic AI look like for highly regulated sectors such as banking, financial services, and insurance?
Anil Chawla- From Verint’s perspective, responsible agentic AI must be built with governance, transparency, and control at its core. It should operate within clearly defined boundaries, with continuous monitoring and the ability to explain, review, and intervene in decisions when required.
Enterprise-grade AI must also be domain-specific—trained on real customer interactions and aligned to regulatory and operational realities. Rather than relying on generic hyperscaler frameworks, BFSI organisations benefit from fit-for-purpose solutions built on decades of sector experience, with security, redundancy, and compliance controls embedded by design. At Verint, this means designing AI that supports human decision-making rather than replacing accountability altogether.
Ultimately, in regulated sectors, success is not defined by how autonomous AI can be. It is defined by whether AI can deliver measurable CX and business outcomes while remaining transparent, auditable, and trusted by regulators, institutions, and customers.
