BFSI

Self-Servicing AI for Intelligent Customer Assistance

A leading bank partnered with Ganit to pilot a self-servicing AI solution aimed at reducing operational load on collections and sales teams while improving customer experience. By combining retrieval-augmented generation (RAG) with account-level intelligence, Ganit enabled faster resolution, consistent responses, and scalable support operations.

We made a visible and measurable impact to our client's business

• Reduced routine query load on collections and sales teams
• Faster response times through instant self-service interactions
• Consistent, context-aware answers across channels
• Improved agent productivity by eliminating repetitive lookups
• Better customer experience with guided next-step recommendations

The bank shifted from agent-dependent servicing to an AI-assisted self-service model.

Challenge

Industry overview

Banks managing large customer bases face continuous pressure to provide faster support while controlling operational costs. Collections and sales teams frequently handle repetitive queries related to account status, payments, and next actions, reducing time available for high-value interactions.

The problem

The existing servicing model relied heavily on manual intervention:

Customers primarily called or visited branches for routine questions
Agents searched across multiple internal systems for information
Responses varied depending on agent knowledge and experience
High query volumes created operational bottlenecks

This led to high servicing workload, inconsistent customer responses, delayed resolution for routine requests, and limited scalability during peak demand periods.

Our role

Ganit was engaged to design and pilot a self-servicing AI assistant that could provide accurate, context-aware responses while integrating seamlessly with existing banking workflows.

Our approach

Methodology

1. Retrieval-Augmented Generation (RAG) Assistant - We implemented a RAG-based AI chatbot capable of retrieving information from approved knowledge sources and account systems, ensuring grounded and reliable responses.

2. Account-Level Intelligence - The assistant leveraged contextual account insights to deliver personalized responses and recommend guided next actions instead of static answers.

3. Omnichannel Deployment - The solution was designed to support multiple engagement channels, enabling consistent customer experiences across digital interfaces and assisted workflows.

4. Smart Escalation Framework - When queries required human intervention, conversations were escalated seamlessly to agents with full context preserved, reducing handling time and repetition.

Enabling consumption

• Instant responses for common customer queries
• Guided action suggestions based on account context
• Unified experience across servicing channels
• Smooth handoff to human agents for complex cases

A valuable difference

Our impact

The transformation delivered an intelligent self-service layer integrated with operational workflows.

• Routine interactions were deflected away from agents
• Customers received faster, consistent guidance
• Teams focused on high-impact engagement instead of repetitive tasks
• AI-driven servicing remained controlled, explainable, and scalable

By combining RAG-powered intelligence with contextual account insights and governed escalation workflows, Ganit enabled the bank to modernize customer servicing while improving operational efficiency and experience quality.