How a Leading NBFC Achieved 10% Jump in Collection Efficiency with Agentic AI?

Challenges and Goals

A leading Indian NBFC serving 15+ million customers was struggling with suboptimal collection efficiency of just 85% which is significantly below industry benchmarks. Despite managing a USD 10 billion book and generating over INR 15,000 crores across diverse segments, the firm's manual collection processes and generic outreach strategies were hampering payment recovery and customer engagement, demanding an urgent transformative solution.

1. ‍Complex Customer Segmentation: - Customers had varied reasons for missing payments; from lack of funds now to forgetfulness to wilful payment default. Addressing each reason required using a different type of customer conversation and creating a unique customer journey. ‍
2. AI-Driven Profile Understanding: - The company needed to develop AI-driven understanding of customer profiles and their payment delay reasons for every individual customer in a large customer base of over 15 million. ‍
3. Personalized Customer Journeys: - Devising customer journeys aligned with the customer profile and their causes of delay was critical to improving collection outcomes. ‍
4. Multi-Channel Engagement at Scale: - Connecting with the customer at the right time, using the channel of their choice, using their language, and at the time of their convenience presented a significant operational challenge.

Rezo's Approach and Solution

Rezo AI's goal was to transform the collections process through intelligent automation and personalization. To achieve this, it leveraged its Agentic AI platform to analyze customer behavior and create dynamic engagement strategies. Rezo was presented with over 1,600 data points for each customer, which were analyzed using Rezo's Agentic AI platform for segmenting customers based on the risk of missing a payment and predicting the reason for missing it.

Rezo AI's Approach to Collections Transformation:

Rezo integrated its Agentic AI platform to automate the end-to-end collections process, creating personalized customer journeys that addressed individual payment behaviors and circumstances. This implementation transformed the firm's operations through intelligent data analysis and adaptive engagement management.

Customer Data Ingestion

The AI analyzed rich, multidimensional data for each customer, processing over 1,600 data points to create comprehensive customer profiles.

Default Risk Prediction

Advanced models identified and scored customers based on their likelihood to default, enabling proactive engagement strategies. ​

Dynamic Segmentation

Customers were instantly segmented into actionable groups reflecting payment patterns and behavioral tendencies, allowing for targeted intervention approaches.

Channel Selection

The platform chose the most effective communication channel tailored to customer preferences, ensuring higher engagement rates. ​

Language & Timing Optimization

The AI personalized each outreach by selecting the right language or dialect and determining the optimal time to connect with each customer.

Personalized Engagement Strategy

AI agents initiated and managed customer conversations, dynamically adapting language, tone, and approach based on real-time conversational cues.

Collection and Feedback Loop

Outcomes and insights from interactions fed back into the system, enabling continuous optimization and smarter future engagement strategies.

Results and Impact

After implementing Rezo AI's Agentic AI platform for collections, the NBFC achieved a remarkable transformation in its collection operations. In just 6 months, the collection efficiency of the NBFC jumped to 96-98% range, an improvement of over 10%. The solution went within two weeks, demonstrating rapid deployment and immediate value creation.

Human-like conversations, additional savings on late fees, and active assistance meant that customer experience with the brand was meaningfully improved, as reflected in higher CSAT and Customer Experience scores. This digital transformation not only enhanced operational efficiency but also improved customer relationships, setting a new benchmark for collections excellence within the organization.