Most "AI voice agent for collections" articles tell you why to automate recovery calls. This one is different: it walks through an actual, ready-to-deploy template — our Hindi EMI Collection agent — so you can see exactly how the call flows, what it captures, and how to launch it.
If you run collections for an NBFC, bank, or microfinance lender in India, this is the template to start from.
What this template is
The EMI Collection Agent (Hindi) is a pre-built voice agent powered by Gemini Live 2.5 (a native speech-to-speech model), tuned for natural Hindi conversations on overdue-EMI calls. It ships in three variants:
- Hindi — for Hindi-first borrowers
- English — for metro / English-speaking segments
- Hindi + English (bilingual) — switches to match the borrower's language mid-call
You plug in three details — your company name, the loan type (personal / home / vehicle…), and an escalation helpline number — and the agent is ready to dial.
The call flow (stage by stage)
The template runs a structured recovery conversation, not a free-for-all:
- Greeting & identity verification — confirms it's speaking to the right borrower before discussing anything sensitive.
- Context — states the institution, the loan/EMI in question, and the overdue position, in plain Hindi.
- Conversation & reason capture — listens for why the payment is pending (forgot, cash-flow, dispute, hardship) and responds appropriately.
- Promise-to-Pay (PTP) capture — the heart of the call: it secures a commitment and records the date and amount (full or partial).
- Disposition & close — tags the outcome and ends politely, with the helpline for escalations.
What it captures (and hands to your CRM)
Every call returns structured data you can route into a CRM, Google Sheet, or collections system:
- PTP Date — when the borrower committed to pay
- PTP Amount — full or partial
- Disposition code — one of
PTP,CALLBACK,REFUSAL,DISPUTED,HARDSHIP
That disposition set is what makes the output usable at scale: your team can instantly filter who promised to pay, who needs a callback, and which accounts are disputed or in genuine hardship. (Pair it with our post-call data extraction workflow to push these fields automatically.)
Compliance is built into the prompt, not bolted on
Collections is regulated, and the template reflects that. The agent is instructed to never threaten, abuse, or use coercive language, to verify identity before disclosing account details, and to offer an escalation path. This aligns with the conduct expectations in India's collections rules — read our deep dive on RBI guidelines for AI collection calls and the broader how to automate debt collection in India guide for the full compliance picture.
Compliance still depends on how you deploy it — calling hours, consent, and DNC handling are your responsibility. The template gives you a compliant conversation; you own the campaign policy.
Why Gemini Live 2.5 for Hindi
EMI calls live or die on whether the borrower feels they're talking to a real, respectful person. A native speech-to-speech model like Gemini Live 2.5 keeps latency low and Hindi prosody natural — no robotic STT→LLM→TTS seams. We cover the model trade-offs in Gemini Live 2.5 HD voice AI explained.
Who should use it
- NBFCs & microfinance running high-volume EMI follow-ups — see voice AI for NBFC & microfinance collections
- Banks & lenders wanting consistent, compliant first-touch reminders
- Anyone moving early-bucket collections off manual tele-calling
For the business case (recovery rates, cost, scale), see our overview of AI voice agents for debt collection.
Deploy it
- Open the financial voice bot on the Edesy voice AI platform.
- Pick the EMI Collection (Hindi) template (or the English / bilingual variant).
- Fill in company name, loan type, and helpline number.
- Connect your telephony (Twilio / Exotel / Plivo) and upload your contact list.
- Launch the campaign — PTP and disposition data flow back per call.
Want it tailored to your collection policy, languages, and CRM? Our team deploys it end-to-end — see AI voice agent services.