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When you're managing more than 600 customer service agents and nearly 300,000 monthly calls, even small inefficiencies become expensive problems. A leading EU financial institution faced exactly this: routine balance checks and payment confirmations consuming millions in labor costs, peak-hour bottlenecks frustrating clients, and talented agents stuck in repetitive work.
They needed technology smart enough to handle complex banking conversations securely and naturally. Master of Code Global delivered precisely that. Here's how we made it happen.
Henrique Gomes
CX & CD Team Lead
The success of this project relied on a strong connection between the conversational experience and the client’s APIs. This allowed customers to self-serve in real time without human intervention. When you enable the right use cases in the right channel, you not only improve containment but also boost customer satisfaction by giving people faster, more reliable answers.
Python
AWS KMS
Amazon Polly
Amazon Lex
Amazon DynamoDB
Amazon Transcribe
Amazon Web Services
Amazon Connect
AWS Lambda
The financial institution’s 600+ agent workforce managed approximately 285,000 calls monthly, with over 65% involving routine inquiries that followed predictable patterns: balance checks, payment confirmations, due date questions, credit limit requests, and transaction disputes.
Despite this volume, average handle time remained high at 7.2 minutes per call, with agents spending significant time on authentication, system navigation, and repetitive information delivery. Customer wait times during peak periods stretched beyond 9 minutes, driving satisfaction scores down and increasing call abandonment rates to 14%.
The organization calculated they were spending $14.8 million annually on handling routine inquiries that didn’t require complex human judgment. The solution needed to autonomously manage standard credit account interactions while operating 24/7 and scaling during volume spikes. Human agents would shift to complex financial counseling and relationship building – work that genuinely required their expertise.
An enterprise-grade Voice AI agent capable of conducting natural, secure credit account conversations while handling authentication, information delivery, and transaction processing autonomously.
Master of Code Global developed a comprehensive Voice AI solution that manages end-to-end credit account interactions with natural conversational flow, robust security protocols, and intelligent routing to human agents when situations require specialized attention. The system integrates deeply with the institution’s core banking platforms, authentication systems, and compliance frameworks while delivering conversations that customers find helpful and genuinely useful.
We collaborated closely with client stakeholders to identify customer needs, top call drivers, business priorities, and available system integrations. Using these insights, we created a clear conversational roadmap that prioritized high-impact, low-effort use cases. This approach allowed us to design optimized conversation flows that maximized containment while delivering fast, consistent support experiences.
The system is built around 58 different conversational paths that cover the majority of credit account needs. These range from checking balances and transaction history to processing payments, starting disputes, requesting credit increases, asking about rewards, activating cards, setting travel alerts, and dealing with security issues. Each path includes points where the AI decides if it can handle the request alone or if it’s best to bring in a human specialist.
Security and identity checks use several layers. Voice biometrics are used for repeat callers, along with question-based verification and secure account number confirmation. Any exchange of private details uses encryption stronger than PCI DSS standards require, and sensitive data like account numbers are automatically removed from records. The AI is also trained to recognize 28 specific types of security-related inquiries that prompt extra verification or an immediate handover to staff.
The language engine understands the context of spoken requests, telling apart similar phrases like wanting to “make a payment” versus “dispute a payment.” It manages interruptions well, asks for clarification if the user’s meaning isn’t clear, and changes how it communicates based on the customer’s perceived mood – for example, slowing down if someone seems confused or moving quickly through standard confirmations if they seem familiar with the process.
Direct connections to the bank’s systems allow the bot to access up-to-the-minute details like balances, recent transactions, due dates, credit limits, reward points, and current offers during the call. It can process payments right away, set up future activities, change contact details, submit credit review requests, and create dispute case IDs – all as part of the conversation, without making users switch to another app or repeat information.
