Interactive Chatbot Built for a Global Bank to Improve Customer Support at Scale
NeoSOFT delivered this project. It is shown as an example of work, not as Sthenos past performance.
Updated
Delivering secure, always-on customer assistance across 2,000+ branches
Client Overview
The client is a globally recognized banking institution with more than 2,000 branches serving customers across multiple regions. With millions of customer interactions each year, the bank required a customer support model that could scale without compromising security, speed, or service quality.
Challenge
- High dependency on manual support teams, resulting in longer response times during peak periods
- Repetitive customer questions, which consumed agent capacity without adding proportional value
- Limited after-hours support, affecting customer experience across time zones
- Strict security and compliance requirements, leaving no room for compromise in authentication or data handling
Our Approach
NeoSOFT designed and implemented an interactive AI chatbot focused on practical problem-solving rather than scripted responses.
Understanding Customer Intent
The chatbot was built to recognize customer intent and common query patterns, enabling it to respond accurately to frequently asked questions and routine service requests.
Secure Identity and Access Handling
Customer identification and authorization were integrated directly into the conversational flow using secure authentication mechanisms, ensuring sensitive information always remained protected.
Multi-Channel Accessibility
To extend reach and convenience, the solution was integrated with voice assistants such as Alexa and Google Assistant, allowing customers to interact through familiar platforms.
Scalable and Resilient Architecture
The backend services were designed to handle large interaction volumes reliably, ensuring consistent performance even during high-traffic periods.
Technical Spotlight
- Context-Aware Conversations: The system was built to understand user intent and conversational context rather than relying on fixed decision trees. This allowed the chatbot to respond naturally to varied customer queries while maintaining accuracy across thousands of interactions.
- Secure Identity Management: Customer identification and authorization were handled through OAuth-based authentication. This ensured every interaction followed strict access controls and complied with enterprise banking security standards.
- Voice Assistant Enablement: To improve accessibility and convenience, the chatbot was integrated with Amazon Alexa and Google Assistant. This allowed customers to interact using voice commands while receiving the same level of accuracy and security as text-based channels.
- Scalable Serverless Architecture: AWS Lambda enabled the chatbot to scale automatically based on demand. This ensured consistent performance during high-traffic periods without the need for constant infrastructure management.
- High Availability and Reliability: Deployed on a Linux-based environment with a Node.js and Express backend, the system was built for resilience and uninterrupted service, even during peak usage.
Solution Delivered
- Customers received real-time responses, 24/7, without waiting for agent availability
- Routine queries were resolved without human involvement
- For complex cases, the chatbot captured relevant context and routed requests to the appropriate support teams
- Agents received better-prepared tickets, reducing resolution time and customer frustration
Results
- Containment rate, significantly reducing pressure on customer service teams
- Adherence to security and compliance standards
- Faster response times and improved agent productivity
Tech Stack
An interactive AI chatbot for a bank serving customers across multiple regions, answering routine questions in real time and passing complex cases to support teams with the context already captured. The bank needed a support model that could scale without compromising security, speed or service quality.
A banking institution serving customers across multiple regions.
Amazon Alexa and Google Assistant give voice the same accuracy and security as text.
Customers are identified and authorized inside the conversation itself.
An Express backend on a Linux-based environment, scaling automatically with demand.
On this page
- Why support was straining, and what the chatbot delivered
- How a customer conversation runs
- Channels, identity and the serverless backend
- Related banking and AI pages
Why support was straining, and what the chatbot delivered
The four pressures the case study lists on the bank's support model, beside the four items it lists under Solution Delivered.
The support problem
Challenge- High dependency on manual support teams, resulting in longer response times during peak periods
- Repetitive customer questions, which consumed agent capacity without adding proportional value
- Limited after-hours support, affecting customer experience across time zones
- Strict security and compliance requirements, leaving no room for compromise in authentication or data handling
What the chatbot changed
Solution delivered- Customers received real-time responses around the clock, without waiting for agent availability
- Many incoming queries were fully resolved without human involvement
- For complex cases, the chatbot captured relevant context and routed requests to the appropriate support teams
- Agents received better-prepared tickets, reducing resolution time and customer frustration
How a customer conversation runs
The case study describes the chatbot by capability. Followed through one conversation, from the first message to a hand-off, they read as four stages.
- The customer asksBy text, or by voice through Amazon Alexa or Google Assistant, with the same accuracy and security on each channel.Customer
- Identity is confirmedOAuth-based authentication identifies and authorizes the customer inside the conversation, so every interaction follows strict access controls.OAuth
- Intent is understoodThe chatbot reads intent and conversational context instead of following fixed decision trees, and answers common queries and routine service requests.Chatbot
- Complex cases are handed onWhen a case needs a person, the chatbot captures the relevant context and routes it to the right support team, so agents get better-prepared tickets.Support teams
Channels, identity and the serverless backend
The chatbot's channels, the identity check inside the conversation, the two routes a query can take, and the stack the case study names.
Figure 1. One conversation path, from channel to answer or hand-off
Channels, the OAuth step, the two routes a query can take and the technologies are all named on the case study. AWS Lambda scales the backend with demand; Node.js and Express run on a Linux-based environment.
Related banking and AI pages
Financial industry pages, AI, Node.js and cloud services that match the chatbot's design, and two other banking case studies.
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