Lentera Microfinance Loan Officer Assistant

The challenge

Before

Lentera is a Jakarta-based microfinance NGO serving 12,000 women-owned micro-enterprises across Java and Sumatra with average loan sizes of IDR 8M. Their 34 field loan officers were spending more time on paperwork than with borrowers — KYC documentation, credit memo drafting, repayment follow-up, group-meeting notes. Each officer was managing 350 active borrowers on average, and the paperwork load capped their ability to grow the portfolio without losing service quality.

• 34 field officers managing 350 active borrowers each on paper-heavy workflows
• KYC documentation captured on forms, photographed, then re-keyed at branch
• Credit memos drafted on laptops in evening hours after field work
• Group meeting notes captured by hand, transcribed weekly
• Repayment follow-up done by SMS templates and phone calls
• No way to surface borrowers at risk of repayment slip before it happened
• Promotion to larger loan tiers slow because credit history compilation was manual
• Donor reporting consuming three weeks per cycle for portfolio analyst team

The solution

What we built

We deployed a mobile-first loan officer assistant that runs on the officer's phone in the field, with offline capability for remote villages. KYC captures borrower identity through ID photo and a structured questionnaire; the agent extracts and validates fields, flags anomalies, and produces a clean KYC pack. Credit memos are drafted by the agent from the structured intake plus business assessment notes; the officer reviews and signs off. Group meeting attendance, savings deposits, and discussion notes are voice-recorded and transcribed by the agent in Bahasa Indonesia with structured tagging. A risk-watch model flags borrowers showing early signals of repayment stress (missed group meetings, smaller-than-usual deposits, sentiment shifts in officer notes) so the officer can intervene supportively before a default. Donor-facing portfolio analytics roll up automatically from the same structured dataset.

How the system flows

1. Borrower Visit→ID Capture→KYC Validation→Anomaly Flagging
2. Business Assessment→Structured Capture→Credit Memo Draft
3. Officer Sign-off→Credit Committee Submission→Approval→Disbursement
4. Group Meeting→Voice Recording→Indonesian Transcription→Tagged Notes
5. Repayment Watch→Risk Signal→Officer Intervention Prompt
6. Promotion Eligibility→Credit History Compilation→Tier Upgrade
7. Donor Reporting→Portfolio Roll-up→Outcome Metrics Surfaced

• Offline mobile capability for remote village field work
• Risk-watch flagging early signals before repayment default
• Voice-first input respecting field-officer workflow realities