AI Requirement Mapping for Tracking Client Product Actions
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Solution Overview
Problem
Existing bank conversation guides do not automatically identify whether clients have acted on product recommendations and fail to connect current products with potential needs, leading to inefficiencies in providing personalized financial services.
Innovation Solution
A system and method using machine learning to engage clients, determine their needs, recommend products and services, and automatically track whether they have acted on these recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual conversation guides are used to identify client needs and provide recommendations, then bank employees can guide clients through product options, but the system cannot automatically track whether clients have acted on recommendations or connect current products with potential needs
Solution Approach 1:
The system implements feedback loops by continuously monitoring client actions on recommendations and using this information to update future recommendations. The machine learning model learns from client responses and behavioral data to improve the accuracy of need identification and product matching over time, creating a closed-loop system that adapts to client preferences and actions.
Solution Approach 2:
The system enables self-service by allowing clients to interact with the recommendation engine directly through digital channels. Clients can review personalized product recommendations, provide feedback on their needs, and the system automatically processes this information without requiring manual intervention from bank employees, thereby enabling automatic tracking and connection of product needs.
2Measurement precision
If comprehensive client data is collected to provide personalized recommendations, then recommendation accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments client data into distinct categories such as demographic information, financial profile, product holdings, and behavioral patterns. This segmentation allows the machine learning model to process different types of data independently and combine them to generate personalized recommendations, reducing overall system complexity while maintaining high recommendation accuracy.
Solution Approach 2:
The machine learning platform is designed as a universal system that can handle multiple data types and perform various functions including need identification, product recommendation, and action tracking. This multi-functional approach consolidates multiple specialized systems into a single platform, managing complexity while delivering comprehensive personalized service.
3Productivity
If machine learning processes are implemented to automatically determine client actions, then tracking efficiency increases, but computational resources and processing time are consumed
Solution Approach 1:
The system applies partial action by focusing machine learning processing only on specific events such as when a client views or interacts with a recommendation, rather than continuously processing all client data. This event-driven approach maintains high tracking efficiency while significantly reducing computational resource consumption compared to continuous full-system processing.
Data Source
AI summary
A system for using a machine learning model to introduce new solutions, based on data gathering and processing, to a user based on user requirements and current solutions. The system includes a back-end server having a processor for processing data and information, a communications interface communicatively coupled to the processor, and a memory device storing data and executable code. The executable code causes the processor to collect data and information from multiple interaction channels, where the data corresponds to interactions between the user and multiple nodes indicating user requirements, store the collected data and information in the memory device, process the stored data and information through a machine learning model to determine which of a set of available solutions are currently being implemented, receive a result from the machine learning model, where the result includes identifying new solutions, and transmit a communication to the user to propose the new solutions.