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

Scaling debt collection from 350,000 to over 2 million calls per month — while cutting costs by 60% and achieving zero compliance violations.

471%
Call Volume Increase
From 350K to 2M+ calls/month
60%
Cost Reduction
Per connected minute
0
Compliance Violations
100% call QA coverage
11
Languages Supported
Including regional dialects

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 / NBFC

Geography

Pan-India (15+ states)

Borrower Base

Millions of accounts

Peak Volume

2M+ calls/month

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.

Agent Capacity Ceiling

400+ agents fully utilised. Adding more meant linear cost growth with diminishing returns.

Language Coverage Gaps

Agents covered only 4–5 languages. Borrowers in Odisha, Assam, and Kerala were underserved — recovery lagged 15–20%.

Rising Costs

Agent salaries rising 10–15% annually. 45% attrition meant constant recruitment. Cost per minute exceeded ₹20.

Compliance Pressure

RBI guidelines tightening. Every human call a potential compliance risk. QA could audit only 5% of calls.

THE SOLUTION

Breeze AI Voice Agents for Collections

[COMPANY NAME] partnered with Simpragma to deploy Breeze — an AI voice agent platform purpose-built for high-volume, multilingual collections in India.

Own Telephony Infrastructure

Proprietary Asterisk-based SIP stack with direct trunking to Indian telcos. Eliminated Twilio dependency and reduced telephony costs by 80%.

11 Indian Languages

Hindi (including dialects), Tamil, Telugu, Kannada, Marathi, Bengali, Gujarati, Malayalam, Odia, Punjabi, and English — automatic language selection based on borrower region.

Collections-Specific AI

Payment reminders, promise-to-pay capture, objection handling, payment plan negotiation, real-time payment link delivery, and smart escalation to human agents.

Deep LMS Integration

Real-time borrower data: loan details, payment history, risk scoring, compliance flags. The AI knows exactly who it's calling and why.

100% Compliance Coverage

Every call recorded, transcribed, and checked against time-of-day restrictions, script adherence, DND compliance, and full audit trails.

IMPLEMENTATION

From Kickoff to 2M Calls in 6 Months

1

Discovery & Design

Weeks 1–3

Requirements gathering, LMS integration spec, conversation flow design

2

Pilot Deployment

Weeks 4–8

10,000 calls/month, Hindi only, 1–15 DPD segment

3

Production Rollout

Weeks 9–14

200,000 calls/month, 4 languages, 1–30 DPD

4

Scale Expansion

Weeks 15–24

2,000,000 calls/month, 11 languages, full portfolio

THE RESULTS

Results That Speak for Themselves

Volume & Reach

MetricBeforeAfterChange
Monthly calls350,0002,000,000++471%
Accounts contacted/cycle~120,000~650,000+442%
Languages supported4–511+120%
Daily call capacity15,00080,000++433%

Cost

MetricBeforeAfterChange
Cost per connected minute₹20–22₹7–9−60%
Monthly collections OpEx₹85L+₹37L−56%
Telephony cost/min₹3–4 (cloud)₹0.80 (own stack)−78%
Human agents required400+80 (escalation only)−80%

Compliance & Quality

Calls audited (QA)
5%100%
Compliance violations
12/quarter0
Script adherence
~85%100%
Borrower complaints (per 100K)
8.22.1

Key Insights

AI Outperforms Humans for Early-Stage Collections

For 1–15 DPD accounts, AI agents slightly outperformed human agents on promise-to-pay rate. Consistency, timing, and reach matter more than negotiation skill for reminders.

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, routing complex cases to a smaller, specialised human team.

Own Telephony Stack Is Non-Negotiable at Scale

The difference between ₹3.50/min (cloud) and ₹0.80/min (own stack) at 2M calls/month is ₹54 lakh per month — ₹6.5 crore annually.

Language Coverage Drives Regional Recovery

Adding Odia, Assamese, and Malayalam increased contact rates in those regions by 3x and recovery rates by 18%.

“We evaluated multiple voice AI platforms. Most could demonstrate a compelling pilot. Breeze was the only one that could prove scale — 2 million calls per month with the reliability and compliance framework we needed. The cost structure, with their own telephony stack, made the economics work at our volume.”

— Senior VP, Collections Operations, [COMPANY NAME]

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