Collection Transformation across ₹732 Crore Portfolio in 6 languages using Skit.ai’s Automated Collections Software

How Skit.ai’s Automated Collections Software Redefined Debt Collection Management for IndusInd Bank

The Company

IndusInd Bank, a leading NIFTY 50 private institution, manages a massive portfolio ranging from credit cards to agricultural finance across 2,015 branches. This operation involves navigating complex delinquency patterns and strict RBI regulatory codes across six languages and nine distinct use cases. To handle this scale with precision, the bank integrates Al for debt collections to ensure real-time intelligence and compliant customer engagement. This automation allows IndusInd to bridge the gap between its vast account volume and the need for localized, multi-channel recovery strategies.

What The Data Showed – Problem Statement

• Special Mention Account (SMA) Escalation Risk : Lack of frequent automated outreach caused SMA-1 (31-60 days) and SMA-2 (61-90 days) accounts to slip into Non-Performing Asset (NPA) classification. Inadequate contact intensity during these stages meant missing the critical window for cost-effective early intervention.
• Escalating Campaign Costs with No Performance Ceiling : High-volume outreach via legacy automation and third-party vendors caused costs to scale with account volume without improving recovery. Consequently, campaign expansions increased spending without enhancing results.
• Agent Dependency Capping Portfolio Coverage : Heavy agent reliance forced portfolios to compete for bandwidth, preventing simultaneous, high-volume coordinated campaigns across all nine use cases.
• Regional Language and Omnichannel Deficit : Legacy outreach primarily utilized one-way voice blasts in limited languages. Contacting consumers across India’s linguistically diverse regions in non-preferred languages suppressed response rates, especially in non-Hindi and non-English markets.
• Customer Experience (CX) vs. Recovery Tension Under RBI’s Fair Practices Code : Scale-driven aggressive outreach risked harming brand reputation and customer relationships. Aligning recovery with RBI compliance and IndusInd’s customer-first values required sophisticated intelligence beyond the capabilities of manual processes.

Customer Context – Our Approach To Fix The Challenges

The Solution

Use Cases

• Performance-based pricing covered four use cases:
  • Post-due credit card
  • Over-credit-limit
  • Personal loan post-due
  • Reminder call
• Engagement-based pricing covered five use cases:
  • Pre-due personal loan
  • Agricultural pre-due
  • Agricultural post-due
  • Loan against property
  • Settlement

The Results

To Summarize

IndusInd Bank needed more than scale to manage nine use cases and six languages under strict RBI frameworks. By integrating Skit.ai’s automated debt collection software, the bank delivered calibrated outreach and regional language support through real-time intelligence. This system utilized a reinforcement learning loop to adapt performance across SMA classifications and settlement negotiations. The results were record-breaking: ₹600 crore recovered at an 82% liquidation rate. This partnership effectively transformed a complex recovery challenge into a high-resolution success story.