Collections automation

Collections is the process of recovering overdue payments from borrowers. Automation applies AI agents to the early and middle stages: reminding customers before and after due dates, explaining outstanding amounts, offering approved payment options, taking a promise-to-pay date, and sending payment links. Voice agents handle calls in the customer's language, and WhatsApp agents handle the same conversation in text, with a human collector taking over when the case needs judgement.

The system decides who to contact, when and through which channel based on the delinquency bucket and past responses, then logs every interaction, outcome and promise. Rules encode what the agent can offer, such as a waiver of late fees or a restructured schedule, and anything beyond that is escalated. Consent, call-time windows and recording are enforced in software.

It is not a replacement for field collections or legal recovery on hard cases, and it is not a dialler with a script. The difference is that the agent understands the response, adapts and records structured outcomes.

• CFO: earlier, more consistent contact improves recovery in the early buckets at a fraction of the cost per contact.
• Compliance officer: contact windows, consent, recording and language are enforced by the system and evidenced in logs, which matters under RBI conduct expectations.
• Operations head: human collectors focus on the cases that need negotiation rather than dialling reminders all day.
• CTO / Head of Engineering: the agent must be policy-gated, tested in shadow mode against real call outcomes and integrated with the loan system.

Manual collections do not scale, and the cost of a human call exceeds the value of many small reminders, so early-stage delinquency often goes uncontacted until it is serious. Automation exists to make early contact cheap, consistent and polite, in the borrower's own language, while keeping people for the difficult cases. The trade-off is the regulatory and reputational exposure: an agent that calls outside permitted hours, misstates a balance or pressures a customer creates real harm. Every offer must be rule-bound, every call recorded with consent, and autonomy should be widened only after shadow-mode results support it.

• An NBFC running pre-due and early-overdue reminder calls in Hindi, Kannada and Tamil with payment links sent on WhatsApp.
• A two-wheeler financer's WhatsApp agent negotiating promise-to-pay dates within approved rules and escalating hardship cases.
• An education group's fee-reminder agent for instalment plans, with counsellor hand-off for families in difficulty.
• A utility or broadband provider automating dunning before disconnection with payment self-service.
• A BNPL provider coordinating reminders across SMS, WhatsApp and voice based on customer response history.

Technique / practiceAn applied AI agent pattern combining voice and messaging channels with policy gates and compliance controls. Eazyware delivers it under Voice Agents with usage-based voice pricing per minute, launched in shadow mode before autonomy.

Eazyware service that covers it: AI Voice Agents (multilingual). Starting prices are on the pricing page.