Microfinance borrowers are among India's most financially vulnerable. The RBI's microfinance regulations and MFIN Code of Conduct set strict standards for how MFI field and call centre staff must communicate. AI call auditing ensures those standards are met — on every call, not just the ones a manager happened to listen to.
Microfinance institutions typically operate with large field staff teams making collection and disbursement calls in Hindi and regional languages — Tamil, Telugu, Bengali, Marathi, Kannada, Odia. These interactions are high-volume, geographically distributed, and take place in the borrower's local language.
Most MFI quality managers do not have the tools or capacity to audit these calls systematically. Manual review of a fraction of calls by a supervisor at head office misses the vast majority of customer interactions — including the ones where coercive language, incorrect interest communication, or misleading recovery threats occur.
The consequences of undetected violations are severe: RBI regulatory action, NBFC licence risk, media exposure, and the real harm to vulnerable borrowers that these regulations are designed to prevent.
Phrases that threaten seizure of household assets, public shaming, or involving local panchayat or police to pressure repayment are prohibited. AI transcript review flags calls containing threat language — "ghar ka saman uthwa lenge," "gaon mein batayenge" — for immediate QA review and agent action.
RBI guidelines prohibit recovery calls before 7am or after 7pm. Call records with timestamps outside these windows, combined with transcripts confirming the nature of the call, create a documented compliance audit trail.
If agents communicate interest rates, processing fees, or EMI amounts that differ from the loan agreement, the transcript documents the discrepancy. Misquoted loan terms — even without deliberate intent — create disputes and regulatory risk.
Calling family members, neighbours, or employers of a borrower without documented consent is a violation. Transcripts of third-party calls — identified by the call log — can be reviewed to confirm whether the borrower authorised the contact.
Every outbound collection call must open with the agent identifying the MFI and the purpose of the call. AI flags calls where this identification was absent or incomplete — a basic compliance requirement that surprisingly fails in 10 to 20% of collection calls in unaudited teams.
No coercive language, no third-party pressure, no out-of-hours contact
Agent identified organisation, stated purpose of call, confirmed borrower identity
Any amounts, rates or dates communicated match the borrower's loan agreement
Borrower informed of grievance channels if they raised a concern or dispute
Tone remained respectful throughout — no intimidation, humiliation or disrespect
MFI borrowers in rural and semi-urban India communicate in their local language — Bhojpuri, Chhattisgarhi, Odia, Bundeli, Maithili, and dozens of other regional dialects and languages. A QA tool that only transcribes Hindi or English accurately will miss the majority of compliance-relevant interactions.
Bolo Aur Likho uses OpenAI Whisper — a model trained on multilingual audio — to process calls in Hindi, Bengali, Tamil, Telugu, Marathi, Kannada, Odia, Gujarati, Punjabi, and 90+ other languages. MFI compliance officers can audit calls from their most geographically diverse borrower interactions with the same confidence as Hindi urban calls.
Upload any collection call in Hindi, Tamil, Bengali, or any regional language. Instant transcript for compliance review. No signup.
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