Case Study: How a Leading Indian Microfinance Company Handles 2M+ Calls/Month with AI Voice Agents

Company Overview

[COMPANY NAME] is one of India's leading microfinance and digital lending institutions, serving millions of borrowers across 15+ states. With a loan book exceeding ₹10,000 crore, they manage one of the largest collections operations in the Indian NBFC sector.

Industry: Microfinance / Digital Lending / NBFC
Geography: Pan-India (15+ states)
Borrower Base: Millions of active accounts
Collections Volume: 2M+ calls/month at peak

The Challenge

Scaling Collections Without Scaling Costs

[COMPANY NAME] faced a challenge common to every fast-growing NBFC in India: their loan book was growing faster than their ability to collect.

The core problems:

Agent capacity ceiling. The collections team of 400+ agents was fully utilised. Adding more agents meant more office space, more hardware, more training, more management — a linear cost curve with diminishing returns.
Coverage gaps. India's 22 official languages meant agents could only cover 4-5 major languages. Borrowers in states like Odisha, Assam, and Kerala were underserved. Collection rates in these regions lagged 15-20% behind Hindi-speaking states.
Rising costs. Agent salaries were increasing 10-15% annually. Attrition ran at 45%, meaning constant recruitment and retraining cycles. Fully loaded cost per connected minute exceeded ₹20.
Compliance pressure. RBI guidelines on collection practices were tightening. Every human call was a potential compliance risk — wrong timing, aggressive language, missing disclosures. QA could audit only 5% of calls.
Borrower reach. With 400 agents making ~15,000 calls/day, [COMPANY NAME] could contact only a fraction of overdue accounts in any given cycle. Many borrowers who would pay if contacted were never reached.

The goal: Increase collections reach by 5-10x without proportional cost increase, while maintaining or improving compliance and recovery rates.

The Solution

Breeze AI Voice Agents for Collections

[COMPANY NAME] partnered with Simpragma to deploy Breeze — a ready-to-deploy AI voice solution purpose-built for high-volume, multilingual collections.

Key solution components:

1. Own Telephony Infrastructure

Breeze runs on a proprietary Asterisk-based SIP stack with direct trunking to Indian telcos. This eliminated Twilio dependency and reduced telephony costs by 80% compared to cloud telephony alternatives.

2. Multilingual Conversation Engine

AI voice agents trained on real collections conversations, supporting:

Hindi (including regional dialects)
Tamil
Telugu
Kannada
Marathi
Bengali
Gujarati
Malayalam
Odia
Punjabi
English (Indian)

Language selection is automatic based on borrower region and preference data from the LMS.

3. Collections-Specific AI

Unlike generic voice AI platforms, the Breeze agent was trained specifically for collections workflows:

Payment reminder delivery with borrower-specific details (name, amount, due date)
Promise-to-pay capture and scheduling
Objection handling (disputes, hardship claims, incorrect amount queries)
Payment plan negotiation for eligible accounts
Real-time payment link delivery via SMS/WhatsApp
Escalation to human agents for complex cases

4. Deep LMS Integration

The AI agent connects in real-time to [COMPANY NAME]'s Loan Management System to access:

Borrower profile and contact details
Loan details (amount, EMI, tenure, overdue days)
Payment history and past interactions
Account classification and risk scoring
Compliance flags (DND, time restrictions, frequency limits)

5. Campaign Management Engine

A sophisticated campaign engine manages:

Borrower segmentation by delinquency bucket (1-15, 16-30, 31-60, 60+ DPD)
Optimised call scheduling (time of day, day of week)
Retry logic with configurable intervals and limits
A/B testing of conversation flows
Real-time throttling based on telco and regulatory limits

6. Compliance Framework

Every call is:

Recorded and transcribed
Checked against time-of-day restrictions (state-specific)
Monitored for script adherence
Flagged for DND compliance
Available for full audit trail
100% QA coverage (vs 5% with human agents)

Implementation Timeline

Phase	Timeline	Scope
Discovery & design	Weeks 1-3	Requirements, LMS integration spec, conversation flow design
Pilot deployment	Weeks 4-8	10,000 calls/month, Hindi only, 1-15 DPD segment
Production rollout	Weeks 9-14	200,000 calls/month, 4 languages, 1-30 DPD
Scale expansion	Weeks 15-24	2,000,000 calls/month, 11 languages, full portfolio

Total time from kickoff to 2M calls/month: 6 months.

Results

Volume & Reach

Metric	Before	After	Change
Monthly calls	350,000	2,000,000+	5.7x increase
Borrower accounts contacted/cycle	~120,000	~650,000	5.4x increase
Languages supported	4-5	11	2.2x increase
Daily call capacity	15,000	80,000+	5.3x increase

Cost

Metric	Before	After	Change
Cost per connected minute	₹20-22	₹7-9	-60%
Monthly collections OpEx	₹85L+	₹37L	-56%
Telephony cost per minute	₹3-4 (cloud)	₹0.80 (own stack)	-78%
Human agents required	400+	80 (escalation only)	-80%

Recovery Performance

Metric	Before (Human)	After (AI)	Notes
Promise-to-pay rate (1-15 DPD)	42%	44%	AI slightly outperforms
Promise-to-pay rate (16-30 DPD)	35%	32%	Within 10% of human
Promise-to-pay rate (31-60 DPD)	24%	20%	Hybrid model (AI + human)
Payment link click-through	N/A	28%	New capability via AI
Overall recovery rate	Baseline	+12% vs baseline	More accounts contacted

Compliance & Quality

Metric	Before	After
Calls audited (QA)	5%	100%
Compliance violations	12/quarter	0
Time-of-day violations	Occasional	Zero
Script adherence	~85%	100%
Borrower complaints (per 100K calls)	8.2	2.1

Key Insights

1. AI Outperforms Humans for Early-Stage Collections

For 1-15 DPD accounts, AI agents slightly outperformed human agents on promise-to-pay rate. These are reminder calls where consistency, timing, and reach matter more than negotiation skill. AI excels here because it contacts more borrowers, at optimal times, without fatigue.

2. The Hybrid Model Is Essential

For accounts past 30 DPD, human agents still outperform on conversion. The optimal model: AI handles volume and pre-qualification, then routes complex cases to a smaller, specialised human team. The 80 remaining agents handle only high-value, high-complexity calls — and they're more effective because they're not burned out on routine reminders.

3. Own Telephony Stack Is Non-Negotiable at Scale

The single largest cost saving came from Breeze's own Asterisk-based telephony. At 2M calls/month, the difference between ₹3.50/min (cloud telephony) and ₹0.80/min (own stack) is ₹54 lakh per month — ₹6.5 crore annually.

4. Language Coverage Drives Regional Recovery

Adding Odia, Assamese, and Malayalam — languages previously unsupported — increased contact rates in those regions by 3x and recovery rates by 18%. Borrowers respond dramatically better when contacted in their native language.

5. 100% QA Coverage Transforms Compliance

Moving from 5% audit sampling to 100% automated QA eliminated compliance violations entirely. Every call is checked against every rule. The compliance team shifted from reactive auditing to proactive policy improvement.

About Breeze by Simpragma

Breeze by Simpragma delivers ready-to-deploy AI voice solutions for enterprises — collections, lead qualification, appointment reminders, and more. Unlike infrastructure tools that require months of engineering, Breeze provides pre-built solutions that go live in minutes. We don't sell APIs — we deliver outcomes.

Key differentiators:

Battle-tested: 60M+ calls processed, 2M/month in production
Own telephony stack — 3-5x lower cost at scale
12+ Indian languages with dialect support
Collections-specific AI, trained on real production calls
100% compliance coverage
Solutions that launch in minutes, not months

Want results like these for your collections operation?

→ Talk to our team

→ Read: AI Voice Agents for Debt Collection — How Microfinance Companies Cut Costs by 60%

→ Read: The Real Cost of AI Voice Agents in 2026

Case Study: How a Leading Microfinance Company Handles 2M+ Calls/Month with AI