ANJALI SHRIVASTAVA
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About
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UX Case Study
AI Chatbot for Smarter Customer Support
Designing an intelligent AI-powered chatbot interface that transforms customer support experiences in the BFSI sector through natural language understanding, automated response generation, and seamless human handoffs.
AI/ML
Customer Support
Chatbot
NLP
Financial Services
UX Research
Role
UI UX Designer
Duration
6 Months
Team
1 Designer, 2 Developers
Overview
India's BFSI sector serves millions of customers daily, and with the surge in digital adoption, users now expect instant, secure, and intelligent support experiences—without long waits or repeated calls. Recognizing this shift, one of India's top banks set out to reimagine its customer support through a conversational AI chatbot.
As the designer, I was tasked with building a chatbot experience that could handle complex financial workflows—from balance checks and loan inquiries to credit card blocking—while maintaining the clarity, trust, and empathy essential to financial communication.
Role
UI UX Designer 1 | Exotel
Timeline
6 months
Responsibilities
User Research, Interactions, Visual Design, Prototyping and User Testing
The Problem
The challenge wasn't just technical—it was human. Previous bots had failed to understand context, often frustrating users and leading to abrupt escalations. Our goal was to design a smart, self-service AI assistant that not only solved problems but felt approachable, secure, and human when needed.
This project aimed to automate high-volume queries, reduce the load on support teams, and bring confidence back into digital banking conversations—all while meeting strict legal and compliance standards.
User Research
To design a context-aware chatbot for one of India's top banks, we conducted extensive user research with key stakeholders including customer service agents, product managers, compliance officers, and actual banking customers across multiple regions.
Our research focused on understanding:
- The types of customer queries handled by support teams
- Frequency and complexity of financial tasks (e.g., loan inquiries, card blocking)
- Breakpoints in existing support channels, including IVR and legacy chatbots
- Emotional and behavioral patterns during stressful or high-stakes conversations
- Compliance and security requirements at each step of the interaction
Fig: Picture from the workshop conducted to understand the hierarchy and card sorting for the product IA
Quantitative Interviews
Understanding the unique workflows and customer support challenges across major banks was essential to shaping a conversational AI solution that truly fits real needs. We conducted in-depth interviews with employees at multiple levels from Axis Bank, ICICI Bank, and State Bank of India (SBI).
These interviews helped us uncover gaps in current support systems, pain points in handling customer queries, and opportunities for automation via intelligent chatbots.
We receive hundreds of balance check and card block requests daily. The current chatbot often loops back or misunderstands context. Agents end up intervening too often, creating unnecessary load.
Customer Support Lead, Axis Bank
Our IVR deflects users to chat, but we see drop-offs when the chatbot can't understand follow-up questions. There's a trust gap, especially with sensitive requests like PIN resets or failed transactions.
Service Manager, SBI
Handling loan-related queries is still very manual. Customers want real-time answers, but the bot can't handle multi-step processes like application tracking or eligibility checks smoothly.
Product Manager, ICICI Bank
Key Insights
Through our comprehensive research and interviews, we uncovered critical insights that shaped our design approach and informed our strategy for creating a truly effective AI-powered customer support solution.
Context Loss is the Primary Pain Point
Users repeatedly expressed frustration when chatbots failed to understand follow-up questions or remember previous interactions, forcing them to restart conversations.
High-Volume Queries Need Instant Resolution
85% of customer queries involve routine tasks like balance checks, transaction history, and account updates that could be automated with proper design.
Trust is Built Through Transparency
Customers want clear communication about what the AI can and cannot do, with obvious paths to human agents when needed.
Security Concerns Drive User Behavior
Users hesitate to share sensitive information with chatbots, requiring careful design of authentication flows and security messaging.
Design Process
01
Research & Discovery
Conducted user interviews with bank customers and support agents to understand pain points and opportunities.
02
Problem Definition
Identified key challenges in current support systems and defined success metrics for the AI solution.
03
Design & Prototype
Created conversational flows, designed the interface, and built interactive prototypes for testing.
04
Test & Iterate
Conducted usability testing, gathered feedback, and refined the design based on user insights.
Key Features
Natural Language Processing
Advanced NLP capabilities that understand customer intent and context, enabling more accurate and helpful responses.
Intent recognition
Context awareness
Multi-language support
Smart Escalation
Intelligent routing system that seamlessly transfers complex queries to human agents with full context.
Automatic escalation triggers
Context preservation
Agent handoff protocols
Financial Compliance
Built-in security and compliance features specifically designed for banking and financial services.
Data encryption
Audit trails
Regulatory compliance
Analytics Dashboard
Comprehensive analytics and reporting to track performance, identify trends, and optimize the chatbot.
Performance metrics
User satisfaction tracking
Conversation analytics
Eva: The Complete Experience
The final Eva interface represents a comprehensive solution that bridges the gap between automated assistance and human support, delivering a seamless experience for HDFC customers across multiple touchpoints.
Intelligent Welcome Flow:
Personalized greeting with contextual quick actions based on user behavior and common banking needs.
Multi-Channel Integration:
Seamless transition between WhatsApp Business and web interface while maintaining conversation context.
Proactive Support:
Smart FAQ suggestions and search functionality that anticipates user questions before they're asked.
Seamless Voice Call Integration
Eva's voice call feature bridges the gap between digital convenience and human connection. When complex issues require detailed discussion, customers can instantly connect with live agents while preserving complete conversation context.
Instant Voice Connection:
One-tap access to live agents with immediate call acceptance notifications and transparent wait times.
Context Preservation:
Complete chat history and customer data automatically shared with agents for informed conversations.
Multi-Modal Support:
Seamless switching between chat, voice, and quick actions for optimal customer experience.
Multilingual Support & Feedback Integration
Eva's intelligent interface breaks language barriers with comprehensive multilingual support for Indian customers, while seamlessly integrating feedback collection to continuously improve the user experience and service quality.
Native Language Support:
Dynamic language switching supporting Hindi, Tamil, Bengali, Malayalam, and other regional languages for authentic customer interactions.
Comprehensive Feedback System:
Multi-dimensional feedback collection covering communication ease, satisfaction levels, response quality, and overall experience metrics.
Contextual Assessment:
Smart feedback prompts that appear at optimal moments in the conversation flow, ensuring high response rates and meaningful insights.
Mobile-First Conversation Design
Our mobile interface prioritizes accessibility and ease of use, ensuring customers can access support seamlessly across all devices. The design maintains conversation context while providing quick action buttons for common banking tasks.
Seamless Agent Handoff:
Smooth transition from AI to human agents when complex issues require personal attention.
Quick Action Interface:
One-tap access to frequently requested services like reporting issues, card blocking, and account inquiries.
Real-time Status Updates:
Live indicators showing agent availability and response times for transparent communication.
Ready to explore more work?
This project showcases how thoughtful design can transform complex financial interactions into seamless customer experiences.
View all Projects
Next Case Study
2025
Anjali Shrivastava.
All rights reserved.
ANJALI SHRIVASTAVA
Work
About
Contact
UX Case Study
AI Chatbot for Smarter Customer Support
Designing an intelligent AI-powered chatbot interface that transforms customer support experiences in the BFSI sector through natural language understanding, automated response generation, and seamless human handoffs.
AI/ML
Customer Support
Chatbot
NLP
Financial Services
UX Research
Role
UI UX Designer
Duration
6 Months
Team
1 Designer, 2 Developers
Interface States
Welcome, conversation, and handoff flows
Interaction Design
Quick actions, typing indicators, and status updates
Visual Hierarchy
Clear distinction between bot and human responses
Overview
India's BFSI sector serves millions of customers daily, and with the surge in digital adoption, users now expect instant, secure, and intelligent support experiences—without long waits or repeated calls. Recognizing this shift, one of India's top banks set out to reimagine its customer support through a conversational AI chatbot.
As the designer, I was tasked with building a chatbot experience that could handle complex financial workflows—from balance checks and loan inquiries to credit card blocking—while maintaining the clarity, trust, and empathy essential to financial communication.
Role
UI UX Designer 1 | Exotel
Timeline
6 months
Responsibilities
User Research, Interactions, Visual Design, Prototyping and User Testing
The Problem
The challenge wasn't just technical—it was human. Previous bots had failed to understand context, often frustrating users and leading to abrupt escalations. Our goal was to design a smart, self-service AI assistant that not only solved problems but felt approachable, secure, and human when needed.
This project aimed to automate high-volume queries, reduce the load on support teams, and bring confidence back into digital banking conversations—all while meeting strict legal and compliance standards.
User Research
To design a context-aware chatbot for one of India's top banks, we conducted extensive user research with key stakeholders including customer service agents, product managers, compliance officers, and actual banking customers across multiple regions.
Our research focused on understanding:
- The types of customer queries handled by support teams
- Frequency and complexity of financial tasks (e.g., loan inquiries, card blocking)
- Breakpoints in existing support channels, including IVR and legacy chatbots
- Emotional and behavioral patterns during stressful or high-stakes conversations
- Compliance and security requirements at each step of the interaction
Fig: Picture from the workshop conducted to understand the hierarchy and card sorting for the product IA
Quantitative Interviews
Understanding the unique workflows and customer support challenges across major banks was essential to shaping a conversational AI solution that truly fits real needs. We conducted in-depth interviews with employees at multiple levels from Axis Bank, ICICI Bank, and State Bank of India (SBI).
These interviews helped us uncover gaps in current support systems, pain points in handling customer queries, and opportunities for automation via intelligent chatbots.
We receive hundreds of balance check and card block requests daily. The current chatbot often loops back or misunderstands context. Agents end up intervening too often, creating unnecessary load.
Customer Support Lead, Axis Bank
Our IVR deflects users to chat, but we see drop-offs when the chatbot can't understand follow-up questions. There's a trust gap, especially with sensitive requests like PIN resets or failed transactions.
Service Manager, SBI
Handling loan-related queries is still very manual. Customers want real-time answers, but the bot can't handle multi-step processes like application tracking or eligibility checks smoothly.
Product Manager, ICICI Bank
Key Insights
Through our comprehensive research and interviews, we uncovered critical insights that shaped our design approach and informed our strategy for creating a truly effective AI-powered customer support solution.
Context Loss is the Primary Pain Point
Users repeatedly expressed frustration when chatbots failed to understand follow-up questions or remember previous interactions, forcing them to restart conversations.
High-Volume Queries Need Instant Resolution
85% of customer queries involve routine tasks like balance checks, transaction history, and account updates that could be automated with proper design.
Trust is Built Through Transparency
Customers want clear communication about what the AI can and cannot do, with obvious paths to human agents when needed.
Security Concerns Drive User Behavior
Users hesitate to share sensitive information with chatbots, requiring careful design of authentication flows and security messaging.
Design Process
01
Research & Discovery
Conducted user interviews with bank customers and support agents to understand pain points and opportunities.
02
Problem Definition
Identified key challenges in current support systems and defined success metrics for the AI solution.
03
Design & Prototype
Created conversational flows, designed the interface, and built interactive prototypes for testing.
04
Test & Iterate
Conducted usability testing, gathered feedback, and refined the design based on user insights.
Key Features
Natural Language Processing
Advanced NLP capabilities that understand customer intent and context, enabling more accurate and helpful responses.
- Intent recognition
- Context awareness
- Multi-language support
Smart Escalation
Intelligent routing system that seamlessly transfers complex queries to human agents with full context.
- Automatic escalation triggers
- Context preservation
- Agent handoff protocols
Financial Compliance
Built-in security and compliance features specifically designed for banking and financial services.
- Data encryption
- Audit trails
- Regulatory compliance
Analytics Dashboard
Comprehensive analytics and reporting to track performance, identify trends, and optimize the chatbot.
- Performance metrics
- User satisfaction tracking
- Conversation analytics
Eva: The Complete Experience
The final Eva interface represents a comprehensive solution that bridges the gap between automated assistance and human support, delivering a seamless experience for HDFC customers across multiple touchpoints.
- Intelligent Welcome Flow: Personalized greeting with contextual quick actions based on user behavior and common banking needs.
- Multi-Channel Integration: Seamless transition between WhatsApp Business and web interface while maintaining conversation context.
- Proactive Support: Smart FAQ suggestions and search functionality that anticipates user questions before they're asked.
Seamless Voice Call Integration
Eva's voice call feature bridges the gap between digital convenience and human connection. When complex issues require detailed discussion, customers can instantly connect with live agents while preserving complete conversation context.
- Instant Voice Connection: One-tap access to live agents with immediate call acceptance notifications and transparent wait times.
- Context Preservation: Complete chat history and customer data automatically shared with agents for informed conversations.
- Multi-Modal Support: Seamless switching between chat, voice, and quick actions for optimal customer experience.
Multilingual Support & Feedback Integration
Eva's intelligent interface breaks language barriers with comprehensive multilingual support for Indian customers, while seamlessly integrating feedback collection to continuously improve the user experience and service quality.
- Native Language Support: Dynamic language switching supporting Hindi, Tamil, Bengali, Malayalam, and other regional languages for authentic customer interactions.
- Comprehensive Feedback System: Multi-dimensional feedback collection covering communication ease, satisfaction levels, response quality, and overall experience metrics.
- Contextual Assessment: Smart feedback prompts that appear at optimal moments in the conversation flow, ensuring high response rates and meaningful insights.
Mobile-First Conversation Design
Our mobile interface prioritizes accessibility and ease of use, ensuring customers can access support seamlessly across all devices. The design maintains conversation context while providing quick action buttons for common banking tasks.
- Seamless Agent Handoff: Smooth transition from AI to human agents when complex issues require personal attention.
- Quick Action Interface: One-tap access to frequently requested services like reporting issues, card blocking, and account inquiries.
- Real-time Status Updates: Live indicators showing agent availability and response times for transparent communication.
Ready to explore more work?
This project showcases how thoughtful design can transform complex financial interactions into seamless customer experiences.
View all Projects
Next Case Study
2025
Anjali Shrivastava.
All rights reserved.