Optimising Customer Banking Experience Using Generative AI
About Client
RubiesMFB is a leading digital bank in Nigeria that offers innovative payment solutions and financial services to both businesses and consumers. The company is focused on simplifying and securing digital transactions, with a commitment to enhancing customer experiences through technology.
Challenges
Rubies MFB faced challenges in providing seamless digital banking experiences,especially with delays in transaction processing and inefficiencies in handling customer inquiries. Customers needed a faster, more intuitive way to perform financial transactions and access company-related information, as the reliance on manual support systems created significant bottlenecks.
How Qucoon Helped
To address these challenges, Qucoon implemented an AI-driven solution powered byAWS to enhance the customer banking experience. The solution included:
- GenAI-Powered Chat Banking System: By leveraging AWS Bedrock and Lambda, a natural language processing (NLP) model was integrated into the banking system to enable secure, automated transactions through conversational interfaces.
- RAG-Based Query System: A Retrieval-Augmented Generation (RAG)-based system was deployed to provide real-time, accurate responses to customer queries, reducing the need for manual support and improving user experience
- AWS Lambda: Used to automate workflows and scale the solution across the platform, ensuring smooth execution of banking transactions and responses.
- Pre-Trained Models: Integration of pre-trained models enhanced query resolution and transaction processing, optimizing operational efficiency and user experience.
Outcome
The implementation of the AI-powered banking solutions led to the following outcomes:
- Improved Transaction Speed: Transaction processing times were reduced by 40%, enabling quicker customer interactions and enhanced satisfaction.
- Enhanced Query Resolution Accuracy: The RAG-based query system improved response accuracy by 75%, ensuring customers received correct and timely information.
- Streamlined Operations: The integration of generative AI reduced the dependency on manual intervention, which resulted in enhanced operational efficiency and agility in handling banking operations.
- Strengthened Customer Experience: With real-time, secure, and intelligent interactions, Rubies MFB has positioned itself as a leader in AI-powered banking services, creating a smoother customer journey
DataManagement and Model Evaluation
Rubies MFB established a rigorous evaluation framework to ensure the high accuracy of both the RAG-based query system and the GenAI banking model:
- RAG System Evaluation: The system was assessed for precision and relevance, maintaining an acceptable hallucination rate of no more than 3% to ensure factual correctness in responses.
- Agentic Banking Model Evaluation: The banking model prioritized transaction security and accuracy, with strict protocols for name enquiry during transaction approvals.
Both models were continuously improved through feedback loops, adapting to changing financial queries and user behavior, ensuring optimal performance and alignment with business objectives.
Conclusion
The successful implementation of the RAG-based query system and GenAI-powered banking at Rubies MFB demonstrates the company's commitment to using cutting-edge AI technologies to enhance digital banking operations. The deployment of AWS Bedrock and Lambda has allowed Rubies MFB to deliver secure,efficient, and intelligent banking solutions, setting new benchmarks in theNigerian financial services industry.
About Client
About Client
RubiesMFB is a leading digital bank in Nigeria that offers innovative payment solutions and financial services to both businesses and consumers. The company is focused on simplifying and securing digital transactions, with a commitment to enhancing customer experiences through technology.
Challenges
Rubies MFB faced challenges in providing seamless digital banking experiences,especially with delays in transaction processing and inefficiencies in handling customer inquiries. Customers needed a faster, more intuitive way to perform financial transactions and access company-related information, as the reliance on manual support systems created significant bottlenecks.
How Qucoon Helped
To address these challenges, Qucoon implemented an AI-driven solution powered byAWS to enhance the customer banking experience. The solution included:
- GenAI-Powered Chat Banking System: By leveraging AWS Bedrock and Lambda, a natural language processing (NLP) model was integrated into the banking system to enable secure, automated transactions through conversational interfaces.
- RAG-Based Query System: A Retrieval-Augmented Generation (RAG)-based system was deployed to provide real-time, accurate responses to customer queries, reducing the need for manual support and improving user experience
- AWS Lambda: Used to automate workflows and scale the solution across the platform, ensuring smooth execution of banking transactions and responses.
- Pre-Trained Models: Integration of pre-trained models enhanced query resolution and transaction processing, optimizing operational efficiency and user experience.
Outcome
The implementation of the AI-powered banking solutions led to the following outcomes:
- Improved Transaction Speed: Transaction processing times were reduced by 40%, enabling quicker customer interactions and enhanced satisfaction.
- Enhanced Query Resolution Accuracy: The RAG-based query system improved response accuracy by 75%, ensuring customers received correct and timely information.
- Streamlined Operations: The integration of generative AI reduced the dependency on manual intervention, which resulted in enhanced operational efficiency and agility in handling banking operations.
- Strengthened Customer Experience: With real-time, secure, and intelligent interactions, Rubies MFB has positioned itself as a leader in AI-powered banking services, creating a smoother customer journey
DataManagement and Model Evaluation
Rubies MFB established a rigorous evaluation framework to ensure the high accuracy of both the RAG-based query system and the GenAI banking model:
- RAG System Evaluation: The system was assessed for precision and relevance, maintaining an acceptable hallucination rate of no more than 3% to ensure factual correctness in responses.
- Agentic Banking Model Evaluation: The banking model prioritized transaction security and accuracy, with strict protocols for name enquiry during transaction approvals.
Both models were continuously improved through feedback loops, adapting to changing financial queries and user behavior, ensuring optimal performance and alignment with business objectives.
Conclusion
The successful implementation of the RAG-based query system and GenAI-powered banking at Rubies MFB demonstrates the company's commitment to using cutting-edge AI technologies to enhance digital banking operations. The deployment of AWS Bedrock and Lambda has allowed Rubies MFB to deliver secure,efficient, and intelligent banking solutions, setting new benchmarks in theNigerian financial services industry.
Business Background
About Client
RubiesMFB is a leading digital bank in Nigeria that offers innovative payment solutions and financial services to both businesses and consumers. The company is focused on simplifying and securing digital transactions, with a commitment to enhancing customer experiences through technology.
Challenges
Rubies MFB faced challenges in providing seamless digital banking experiences,especially with delays in transaction processing and inefficiencies in handling customer inquiries. Customers needed a faster, more intuitive way to perform financial transactions and access company-related information, as the reliance on manual support systems created significant bottlenecks.
How Qucoon Helped
To address these challenges, Qucoon implemented an AI-driven solution powered byAWS to enhance the customer banking experience. The solution included:
- GenAI-Powered Chat Banking System: By leveraging AWS Bedrock and Lambda, a natural language processing (NLP) model was integrated into the banking system to enable secure, automated transactions through conversational interfaces.
- RAG-Based Query System: A Retrieval-Augmented Generation (RAG)-based system was deployed to provide real-time, accurate responses to customer queries, reducing the need for manual support and improving user experience
- AWS Lambda: Used to automate workflows and scale the solution across the platform, ensuring smooth execution of banking transactions and responses.
- Pre-Trained Models: Integration of pre-trained models enhanced query resolution and transaction processing, optimizing operational efficiency and user experience.
Outcome
The implementation of the AI-powered banking solutions led to the following outcomes:
- Improved Transaction Speed: Transaction processing times were reduced by 40%, enabling quicker customer interactions and enhanced satisfaction.
- Enhanced Query Resolution Accuracy: The RAG-based query system improved response accuracy by 75%, ensuring customers received correct and timely information.
- Streamlined Operations: The integration of generative AI reduced the dependency on manual intervention, which resulted in enhanced operational efficiency and agility in handling banking operations.
- Strengthened Customer Experience: With real-time, secure, and intelligent interactions, Rubies MFB has positioned itself as a leader in AI-powered banking services, creating a smoother customer journey
DataManagement and Model Evaluation
Rubies MFB established a rigorous evaluation framework to ensure the high accuracy of both the RAG-based query system and the GenAI banking model:
- RAG System Evaluation: The system was assessed for precision and relevance, maintaining an acceptable hallucination rate of no more than 3% to ensure factual correctness in responses.
- Agentic Banking Model Evaluation: The banking model prioritized transaction security and accuracy, with strict protocols for name enquiry during transaction approvals.
Both models were continuously improved through feedback loops, adapting to changing financial queries and user behavior, ensuring optimal performance and alignment with business objectives.
Conclusion
The successful implementation of the RAG-based query system and GenAI-powered banking at Rubies MFB demonstrates the company's commitment to using cutting-edge AI technologies to enhance digital banking operations. The deployment of AWS Bedrock and Lambda has allowed Rubies MFB to deliver secure,efficient, and intelligent banking solutions, setting new benchmarks in theNigerian financial services industry.
