AI Product Recommendation Models for Granular Financial Growth
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Solution Overview
Problem
Current systems for financial institutions fail to accurately capture customer data and utilize machine learning techniques to forecast customer decisions, leading to a lack of granularity and customization in product recommendations, segmentation, and advertisement strategies.
Innovation Solution
Implementing individualized systems and methods using machine learning to gather and process customer and financial institution data through trained models, enabling the optimization of product combinations, identification of growth opportunities, and development of tailored advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer systems use basic CRM data and simple market data for product recommendations, then the system complexity is low, but the measurement precision and customization capability are insufficient
Solution Approach 1:
The patent segments customer data into multiple categories (demographic data, transactional data, behavioral data, contextual data) and processes each segment through specialized machine learning models. This segmentation allows high-precision data capture while managing system complexity through modular architecture, where each segment is handled by dedicated processing components rather than a monolithic system.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw data sources and product recommendations. These models act as mediators that transform basic CRM data into refined customer profiles, enabling high measurement precision without directly increasing operational complexity. The intermediary layer abstracts the complexity of data processing from the recommendation generation process.
2Adaptability or versatility
If financial institutions offer the same products to groups of customers, then the device complexity is low, but the adaptability and customization are insufficient
Solution Approach 1:
The patent implements dynamic product recommendations that adapt to individual customer characteristics, preferences, and behaviors. The system continuously updates customer profiles based on new data and adjusts recommendations in real-time. This dynamic approach enables high adaptability while managing complexity through automated learning processes that improve over time without requiring manual reconfiguration.
Solution Approach 2:
The patent changes the parameters of product recommendations based on customer-specific factors such as demographic characteristics, transactional history, and behavioral patterns. By adjusting recommendation parameters dynamically according to individual customer profiles, the system achieves high customization capability. The parameter changes are driven by machine learning models that automatically adapt to new customer data without requiring system redesign.
3Measurement precision
If basic segmentation approaches are used, then the ease of operation is high, but the measurement precision and granularity are insufficient
Solution Approach 1:
The patent implements self-service segmentation where machine learning models automatically identify and refine customer segments based on multiple data sources. The system performs segmentation autonomously by analyzing patterns in demographic, transactional, behavioral, and contextual data, eliminating the need for manual segment definition. This self-service approach achieves high measurement precision while maintaining operational simplicity, as the system handles segmentation complexity internally without requiring user intervention.
4Productivity
If descriptive analytics are used instead of predictive analytics, then the ease of operation is high, but the productivity and forecast accuracy are insufficient
Solution Approach 1:
The patent applies preliminary action by using machine learning models to forecast customer decisions and predict future behaviors before product recommendations are generated. The system performs predictive analytics in advance to anticipate customer needs, enabling more accurate forecasts. This preliminary predictive processing improves productivity by enabling proactive rather than reactive decision-making, while the automated nature of the process manages system complexity.
Data Source
AI summary
Systems and methods for optimizing the combination of products and services a business offers to customers, identifying a combination of top markets a business offers to customers for growth opportunities, and optimizing advertisements. In one implementation, the disclosed system includes at least one processing device and at least one non-transitory memory containing software code configured to cause the processing device to: gather customer data and financial institution data from a plurality of data sources; extract a plurality of customer behavior features and a plurality of financial institution behavior features; process the customer behavior features and financial institution behavior features using one or more trained foundation models; input the foundation model outputs and a plurality of goal inputs into a trained product model; and output a natural-language market response.