Case Study

Intelligent call routing for a global bank

Read how we dramatically enhanced the efficiency of a bank’s call centre to reduce wait times

Finance

The problem

A large, global bank needed to improve call center efficiency without increasing costs. They sought a smarter, AI-driven approach to manage inbound calls, reduce wait times, and optimize resource allocation.

Reducing call center inefficiencies

The bank’s customer service team handled high call volumes, leading to long wait times and inconsistent prioritization. They needed a solution to streamline call routing and automate simple queries—improving both speed and customer satisfaction.

Driving efficiency with AI-powered automation

The goal was to reduce manual call handling by leveraging AI to classify, prioritize, and route inbound customer inquiries. This would free up agents to focus on high-value calls while ensuring simple requests were handled instantly.

Our solution

We implemented an AI-powered call routing system that automatically classified inbound calls by priority and generated automated responses for simple queries. This reduced agent workload and improved response times.

AI-driven prioritization and call classification

Our AI model analyzed inbound communications in real-time, identifying urgent, high-value calls and routing them to agents immediately. Less critical queries were either queued efficiently or resolved automatically.

Automated response generation

For routine questions—such as balance inquiries, branch opening hours, and transaction confirmations—AI-generated responses eliminated the need for agent intervention, resolving issues faster and reducing call volumes.

Optimizing call forwarding

When agent intervention was necessary, the AI system automatically forwarded inquiries to the appropriate department, ensuring that customers connected with the right specialist without unnecessary transfers or delays.

Results

95%

Prioritization accuracy for inbound calls

89%

Of inquiries successfully forwarded without manual intervention

By leveraging AI-powered call classification, prioritization, and automation, the bank dramatically improved call center efficiency. High-priority calls were handled faster, simple queries were resolved instantly, and agent time was optimized for complex issues.

Before implementing AI, call routing was inefficient, with high-value calls waiting in queue alongside routine inquiries. By deploying intelligent prioritization and automated responses, the bank significantly reduced agent workload while maintaining a high level of service quality.

With 95% prioritization accuracy and 89% automated forwarding, AI ensured customers reached the right service faster, leading to improved customer satisfaction and more efficient resource allocation.

This AI-powered system is now a core part of the bank’s call center operations, continuously learning and refining its approach to maintain peak efficiency.