By Maaz Ansari, Co-Founder and CRO, Oriserve
For many years, financial institutions treated contact centers as cost centers. They were necessary for support but expensive and hard to scale because growth depended on hiring more people. That model is changing. Banks, NBFCs, and fintech companies are moving beyond basic IVR systems and deploying autonomous AI agents as core operational infrastructure.
This shift is not limited to answering common questions. AI is now handling critical financial tasks such as debt recovery, KYC onboarding, lead qualification, and even loan disbursement. A clear signal of this shift is Bajaj Finance planning to use voice bots to disburse ₹5,300 crore in loans in FY26. This shows AI is no longer a support layer but part of the transaction engine itself.
Other key benefits include:
1. Separating Growth from Headcount
The traditional BFSI operating model does not scale well. Higher call volumes require more hiring, which becomes risky during seasonal spikes or market volatility. Autonomous AI agents remove this dependency. Institutions can scale operations 4 to 5 times during peak periods without adding staff.
Digital agents do not face burnout or attrition. They handle up to 80 percent of routine calls such as balance checks, EMI schedules, payment reminders, and status updates from start to finish. This level of call containment frees human agents to focus on complex, high value cases. The contact center shifts from being a bottleneck to becoming a predictable and scalable growth function.
2. Eliminating the Compliance vs Empathy Trade-off
In regulated sectors like banking and collections, compliance is mandatory. Under pressure, human agents can miss required disclosures or use inappropriate language, which can lead to regulatory penalties.
Modern voice AI systems enforce strict adherence to approved scripts. These agents are policy grounded and respond only using information from official SOPs and loan agreements. Hallucination rates stay below 1 percent.
AI also closes the audit gap. Human supervisors typically review only 2 to 5 percent of calls. AI-driven interaction intelligence audits 100 percent of calls. It generates audit-ready reports and flags violations instantly, ensuring full compliance with RBI and other regulatory standards.
3. Transforming Debt Recovery Through Polite Persistence
Collections is one of the most sensitive areas in financial operations. Traditional approaches often lack empathy or context, which lowers recovery rates and increases customer friction.
AI agents are changing early-stage collections, especially in the 0 to 30 DPD range. Using sentiment detection, they identify stress, hesitation, or anger and adjust tone in real time. The agent remains firm but empathetic. It negotiates payment plans based on past behavior, captures promises to pay, and handles objections without sounding forceful or threatening.
The impact is measurable. Institutions report 15 to 25 percent higher recovery rates along with 40 to 60 percent reductions in cost to collect. AI absorbs high volume early delinquencies, allowing human teams to focus on sensitive edge cases and legal proceedings.
4. Data Hygiene as an Operational Advantage
A less visible but critical issue in BFSI operations is poor data quality. Human agents misclassify call outcomes up to 40 percent of the time. Examples include tagging “call me later” as a refusal or mislabeling incorrect numbers.
AI voice bots deliver over 95 percent accuracy in call disposition tagging. They distinguish clearly between confirmed actions such as “I paid via UPI yesterday” and vague responses such as “I will try next week.” Each outcome is tagged correctly for verification or follow-up. This precision reduces follow-up waste by nearly 90 percent and ensures collections and sales pipelines are built on reliable data rather than assumptions.
Conclusion: From Support Function to Strategic Asset
Voice AI in financial operations is no longer experimental. It is now a competitive requirement. Institutions are automating large parts of the customer lifecycle, from lead qualification to loan disbursement, collections, renewals, and retention.
The scale and impact are already visible. AI systems are disbursing thousands of crores in loans, scaling operations multiple times during peak demand, improving recovery outcomes, and cutting operational costs significantly. The core question has shifted from whether AI can handle calls to how much of financial operations can be safely and effectively run by autonomous, compliance-first AI systems.