Dynamic Financial Recommendation System Using Segmented Modules
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Solution Overview
Problem
Traditional financial product/service recommendations by banks are not collaborative and rely solely on customer financial portfolios, lacking insight into customer behaviors, attitudes, and future life events, leading to inaccurate and incomplete recommendations.
Innovation Solution
An interactive and collaborative customer experience system that dynamically builds financial solution recommendations based on inputs such as customer behaviors, attitudes, life events, interests, and financial data, allowing real-time adjustments and non-linear data entry to accommodate specific needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional financial product recommendations rely solely on customer financial portfolios, then the recommendation process is simple and quick, but the accuracy and completeness of recommendations deteriorates due to lack of insight into customer behaviors, attitudes, and future life events
Solution Approach 1:
The patent segments the recommendation system into multiple independent modules: a collaborative filtering module that analyzes customer behaviors and attitudes, a life event prediction module that forecasts future needs, and a traditional portfolio analysis module. Each module processes specific types of data independently and their results are integrated to form comprehensive recommendations, thereby improving accuracy without overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary layer that bridges traditional portfolio-based recommendations and modern behavioral analytics. This intermediary module synthesizes data from multiple sources including customer surveys, transaction patterns, and life event predictions, transforming raw data into actionable recommendation insights that improve accuracy while managing complexity through structured data processing
2Loss of information
If traditional systems use multiple personal questions to gather customer information, then comprehensive data is collected, but customer involvement and trust in the collaborative process deteriorates
Solution Approach 1:
The patent implements self-service mechanisms where customers can voluntarily share additional information about their behaviors, attitudes, and life events through interactive interfaces. The system provides immediate feedback showing how each piece of information contributes to personalized recommendations, enabling customers to control their own data sharing process while ensuring comprehensive information collection
Solution Approach 2:
The patent incorporates real-time feedback loops where the system presents preliminary recommendations based on initial portfolio data, then guides customers through optional supplementary questions. After each customer input, the system immediately updates and re-presents recommendations to show the direct impact of customer responses, creating an engaging collaborative experience that naturally encourages complete information provision
3Measurement precision
If financial recommendations are provided in real-time based on multiple inputs, then personalized and accurate recommendations are achieved, but the processing time and system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing customer behavioral patterns, attitude profiles, and life event predictions in advance during off-peak periods. When generating recommendations, the system retrieves these pre-computed insights rather than calculating them in real-time, enabling personalized recommendations to be delivered quickly without excessive processing delays
Solution Approach 2:
The patent employs dynamic processing where the system adjusts its analysis depth based on real-time requirements. For routine recommendations, it uses lightweight algorithms that provide fast results, while for complex scenarios involving multiple life events or significant portfolio changes, it dynamically activates more comprehensive analysis modules, balancing personalization accuracy with processing time efficiency
Data Source
AI summary
Systems, methods, and computer program products are provided for dynamically building financial solution recommendations based on received inputs. These inputs, which may be received from the customer, an associate collaborating with the customer or from a customer database, may include customer behaviors, customer attitudes, customer life events, customer life interests, customer personal/profile data, customer financial data, such as portfolio data and the like. Based on the received inputs, financial solution recommendations are determined and presented to the customer in real time or near time. As such, the customer is able to instantaneously see how each input of recommendation criteria impacts the financial solution recommendations. Further, embodiments of the present invention provide for inputs to the financial solution recommendation tool/module to be received in a non-linear fashion, such that a customer or a collaborating associate may provide inputs at various entry points to accommodate specific financial solution needs of the customer.