In an interaction with Adlin Pertishya Jebaraj, correspondent of Finance Outlook Magazine, Anurag Jain, Co-Founder of Oriserve, shares how the use of conversational AI is transforming BFSI Companies to provide support for customers with high volume activity, while offering a significant cost savings, better risk management, and improved efficiency and profitability, by utilizing AI agents trained through policies established for these specific industries.

Anurag Jain, with more than 20 years of experience consulting for banks, financial technology companies, and payment processors around the globe, Anurag is an expert in compliance and risk management in financial services, particularly fraud investigation and prevention (AML), preventative controls to protect systems from fraud and money laundering, screening for compliance with government sanctions, and regulatory compliance.

How do you see conversational AI adoption transforming financial services, and which sectors are leading this transformation?

In finance, the integration of Conversational AIs into business processes has evolved to production level where it is able to operate on demand, improving the top and bottom lines of the business.

This is not only attributable to expected gains such as decrease in operation cost, improvement in customer experience, and predictable variables but we also take into consideration the gains made as a result of bridging gaps where demand is high and there are no available customer care agents. At this stage, organizations are able to manage the automation of customer support, lead qualification, onboarding and activation, upsell and cross-sell, reactivation of old clients, debt collection, and subscription renewals.

These processes are still able to maintain, though seamlessly, a hybrid approach where a human expert is able to take over to perform some of the more complex decision making.

The first organizations to lead in these processes include retail, banks, NBFCs, and insurers and, along with Fintechs, are still the first to implement more advanced Conversational AIs with the ability to seamlessly converse with customers in multiple human languages and switch to another language on demand.

Conversational Ai in debt collection has been linked to operating at a cost loss of 30 to 40 % of a normal human agent while at the same time increasing the closure rate on debt collection.

Bottom line productivity: According to McKinsey, loss of productivity in the banking sector that could be covered through implementation of AI is in the range of 200-340B$ in a year.

Service Automation and Cost: As regarding automation and cost, analysts at Gartner point out that agentic AI, at its current rate of evolution, will automate and resolve close to 80% of prevailing service requests, driving cost containment of 30%. This is an evolution that is relevant to contact centers in BFSI.

Onboarding Efficiency: Juniper, in its reports, indicates that due to AI, banks will save, in operational cost, approximately 900 million dollars by 2028, and that including tens of millions of hours, the operational savings are a “no-brainer”.

Voice for Revenue Operations: Public domain data indicate that the use of AI in collection strategies yields a recovery rate of 25% while customer satisfaction apparently increases due to the compliant/escalated outreach.

The most significant expansion is apparent soon after the go-live. With the application of week to week learning loops in operational silos, control and observability, while balanced, are established to mitigate CX risks, thereby, progressive containment, conversion and recovery compounded over time, resulting in an evident expanding ceiling of reliable ROI. The initial burst is transformed into a durable return.

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What are the financial viability aspects of integrating conversational AI, and how do they affect cost management, risk mitigation, and ROI for financial institutions?

The Money Making Mechanics of Conversational AI: Being Cost Efficient, Lowering Risk, and Expanding Value.

Cost Management happens when we automate 60-70% of repetitive employee tasks. Costs stay manageable when we use route-modelling depending on whether we are dealing with low, mid, or high complexity tasks. Over time and with scaled tasks, institutions see measurable reductions in cost-to-serve, cost per collection, onboarding costs, and renewals handling.

Trust by Design: Engaging policy guardrails with programmable tools and call tracking are meant to mitigate/contain risk while maintaining quick governance with flight recorder level trace frameworks. Mechanisms to Store and Audit Conversations strengthen compliance and mitigate risk by removing the increased potential of human error.

ROI takes time to accumulate: The bigger factors in collections are promise-to-pay conversions and full-base coverage. In onboarding, the decrease of abandonment and the increase of activation takes priority. We’re very much in the early stages of development, and the initial benefits will materialize. Building a learning cycle will improve conversion and containment every several weeks, so an initial 10-20% increase can turn into several long-lasting benefits in a quarter or two.

Pricing/market context: The cutting-edge models, efficient tools, and economic advancements, in addition to our internal investment, have made it possible to have a service cost 20-30% lower than the cost of people in India, and which is 70-80% lower than the marginal cost of people in advanced countries, depending on the task.

Bottom line: Banks need to evaluate a project’s potential to transform automation from a cost to an ROI generator, on the basis of potential savings from automation, as well as on potential to lower calculated risks, and to improve unit economics, especially after the project is fully operational.  That’s what Conversational AI is all about.

What are the key financial KPIs businesses should track to measure the ROI of conversational AI implementation?

Organizations should center their evaluations on KPIs that encapsulate both efficiency and value creation. For AI Agents, think of them as new team members that you train, and you assess their progress based on how quickly they learn and how much supervision they require. Then, when they reach the maturity stage, you assess based on their outcomes and how often they can be relied on. For conversational AI, use the same thinking. Early on, be patient and methodical and don’t set your expectations too high. Once your AI is experienced, you can begin holding it to your business KPIs.

Training/Ramp KPIs (First 4-8 weeks)

• Containment/Automation Rate: the percentage of questions or inquiries that were resolved by the AI without the assistance of a human agent.
• First Contact Resolution (FCR): the percentage of problems or questions that were resolved in the first interaction.
• Average Handling Time (AHT): Should be decreasing as work patterns become more consistent.
• Latency SLO (including barge-in): The speed of interaction from the customer’s/voice user’s perspective.
• Escalation Quality: The success rate of warm-via handoff transfers.
• Trust & Safety: the number of safety incidents during the 1000 calls; off-policy rate should be decreasing.

Steady-State Efficiency KPIs (After stabilization)

• Cost per Interaction (CPI): What is the cost when the task is completed by AI vs. a human? Aim to maintain a substantial dollar difference.
• Cost-To-Serve: The overall service cost reduction across the entire contact center’s operational value.
• Operational Coverage: Initial contact reaches on day 0, core coverage for collections, and after-hours coverage.
• Time Saved/Workforce Uplift: The percentage of human hours shifted to more complex and valuable work.

Value-Creation KPIs (topline & retention)

• Lead Conversion Rate: Out of all the qualified leads.
• Revenue Growth: AI-prompted offers and nudges that generate money.
• Collections Efficiency: Cost of each collection; promise-to-pay and cure rates.
• Onboarding & Activation: Reduction of the drop-off rate; increase of activation and system usage.
• Renewals & Reactivation: Rate of reactivations and renewals.
• Customer Retention: Reduction of churn after deployment of AI.

Experience KPIs (board-friendly)

• CSAT/Sentiment: Value of experience post AI interaction.
• NPS: Advocacy after AI assisted journeys.
• Complaint/Opt-Out Rate: Indicates customer compliance and frustration.

Governance KPIs (make trust measurable)

• Audit Completeness: Percentage of calls with a complete “flight-recorder” trace (inputs, tools, grounding).
• Resolution Latency SLA: Duration from risk flag to human action.
• Policy Adherence: Percentage of interactions that remain within the allowed intents and tools.

How to review it like a CFO/COO:

• Expand carefully: Predict the early changes, and monitor the progress-learning rate and safety improvements.
• Lock unit economics: Monitor the CPI and costs to serve and compare those to control groups.
• Prove value creation: Tie the AI touchpoints with conversion, recovery, activation, renewal.
• Make trust visible: Publish a weekly “Trust & ROI” scorecard that combines business, experience, and governance metrics so finance, ops, and risk stay aligned.

Can you provide examples of financial risk mitigation strategies when investing in conversational AI initiatives

The financial institutions can best leverage AI Conversational Agents like Control Systems and not just a demo. Engage AI Conversational Agents using policy-first design AI Conversational Agents defines what the agent can and cannot say or do, and binds all actions to schema-gated tools and sanctioned knowledge sources. Incorporate internal and detachable flight-recorders of each session with immutable logs of input tokens, grounding artefacts, tool invocation, tool inputs confidence, and handoff timestamps so that each session can be rendered fully explainable to internal compliance and external regulators.

Gradually deploy AI Conversational Agents Systems with progressive rollout with shadow deployments, through to cohort deployments and then to full-scale deployment. Warm handover playbooks and handover SLAs apply to human intervention and require control over AI systems with low confidence, complaints, or risk triggering behaviours.

AI Conversational Agents protect both customers and risk mitigation strategies of the firm by imposing user authentication and step-up verification prior to any critical action, plus providing multilingual quality controls to cater India’s code-switching reality. AI Conversational Agents systems designed governance to banking model risk standard practice (validation, control of change, etc) and reduction of data along with encryption and data residency.

Finally, managing the P&L with model routing and cost caps and publish a weekly “Trust & ROI” to pair business KPIs (containment, conversion, recovery) with safety and latency metrics. The outcome: measurable ROI with auditable trust.

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How do you foresee conversational AI evolving in finance efficiency over the next 5-10 years in terms of capabilities, operational efficiency, and ROI?

Due to the changes to the role of Conversational AI from general assistant to domain specialists, Banking, lending, insurance, and fintech, Conversational AI has gone through some of the most comprehensive changes.

These AI agents can now hold the types of conversations with customers that are goal-oriented, multi-lingual and human-like. They are able to switch languages with ease and can be live monitored and controlled with soft governance policies, outlier policies and manual actions. The Micro agents that qualify, verify, negotiate, and collect still help operations be more efficient. The self-healing policies that improve governance-as-code (flight-recorder logs, versioned policies) help reduce downtime.

The most notable efficiencies that are able to compound are those that become self-sustaining after going live. There are active learning loops that drive efficiencies in containment, activation, and conversion/recovery, and renewal beyond the human-only limits. The activation of these loops also improve model routing and lower computational costs which drive down the cost per interaction.

From the specialized policy compliant, multilingual and auditable voice agents, enterprise scale receives dramatic efficiency improvements. These improvements are also predictable and result in compound ROI from the improvement of the overall customer experience (CX).