Financial Recommendation Engine Cohort Segmentation
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Solution Overview
Problem
Financial Service Providers lack effective recommendation systems to understand user profiles and recommend suitable financial products, as existing systems are limited or non-existent in the financial industry.
Innovation Solution
A financial-based recommendation engine that gathers financial-related data from multiple sources, clusters users into cohorts based on their financial data, creates user profiles with a temporal dimension, and matches them with relevant financial products, recommending products based on current and potential cohort progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recommendation systems are implemented in the financial industry, then user profile understanding and product recommendation capability are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments users into cohorts based on financial data characteristics, creating distinct user groups that can be analyzed and targeted independently. This segmentation approach simplifies the complexity by breaking down the heterogeneous user base into manageable segments with similar traits, enabling more effective recommendation strategies without overwhelming system complexity.
Solution Approach 2:
The system changes parameters by introducing temporal dimensions to user profiles, allowing profiles to evolve over time. This enables the system to capture dynamic financial behaviors and transitions between cohorts, improving recommendation adaptability while managing complexity through structured parameter evolution rather than complete reanalysis.
2Measurement precision
If temporal dimension is added to user profiles to track progression, then user progression tracking capability is improved, but data processing complexity and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining cohorts based on financial data characteristics before tracking user progression. Users are assigned to cohorts in advance, and their transitions between predefined cohorts are tracked over time. This approach enables precise progression tracking by having predetermined categories to measure against, reducing the complexity of real-time analysis.
3Measurement precision
If financial data is gathered from multiple sources for each user, then user profile accuracy is improved, but data collection complexity and storage requirements increase
Solution Approach 1:
The system applies universality by creating a unified cohort classification framework that can accommodate multiple data sources and user characteristics. Instead of building separate analysis systems for each data source, a single cohesive cohort structure handles diverse financial data, reducing collection complexity while maintaining profile accuracy through integrated multi-source data processing.
Data Source
AI summary
As described herein, a system, method, and computer program are provided for a financial-based recommendation engine. In use, financial-related data is gathered for a plurality of users from a plurality of sources. Additionally, the plurality of users are clustered into a plurality of cohorts, based on the financial-related data. Further, a plurality of user profiles are created for the plurality of cohorts, including for each cohort of the plurality of cohorts, creating a corresponding user profile based on the financial-related data for the users in the cohort. Still yet, each user profile of the plurality of user profiles is matched to one another and to one or more financial products. Moreover, the one or more financial products matched to each user profile of the plurality of user profiles are recommended to the users in the cohort that corresponds to the user profile.