Financial Data Aggregation for Customized Customer Solutions
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Solution Overview
Problem
Current banking and financial services struggle to provide customized solutions that meet the unique financial needs of each customer, leading to dissatisfaction and unmet demands, particularly due to complex product offerings and high fees.
Innovation Solution
A system and method that utilize advanced data analysis to aggregate a customer's financial portfolio, risk profile, and relationship with the bank over time, providing personalized suggestions for optimizing earnings and expenditures based on interest rates, risk analysis, and current financial situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If banks offer multiple financial products to meet diverse customer needs, then customer satisfaction improves, but system complexity and operational costs increase
Solution Approach 1:
The patent creates a universal financial platform that performs multiple functions: it aggregates customer data from various sources, analyzes risk profiles, generates customized financial solutions, and manages relationships across different products. This single multi-functional system replaces the need for separate complex product management systems, achieving customization without proportional increases in overall system complexity
Solution Approach 2:
The system dynamically adjusts financial parameters (interest rates, fees, product recommendations) based on customer-specific data analysis. By changing parameters rather than creating entirely separate products for different customer segments, the bank achieves high adaptability while managing complexity through parameter optimization rather than product proliferation
2Measurement precision
If banks analyze comprehensive customer financial data to provide personalized solutions, then service quality improves, but data processing complexity and time increase
Solution Approach 1:
The patent segments the customer analysis process into distinct modules: data aggregation from multiple sources, risk profile analysis, financial situation assessment, and solution generation. This segmentation allows each module to handle specific tasks with focused complexity, improving overall measurement precision while managing system complexity through modular architecture
Solution Approach 2:
The system introduces an intermediary analytical layer that processes raw customer data and transforms it into actionable insights. This intermediary layer (comprising processors and algorithms) mediates between complex raw data and simple personalized recommendations, achieving high analysis accuracy while shielding the user interface from underlying complexity
3Loss of energy
If banks charge high fees to maintain profitability, then revenue increases, but customer satisfaction decreases
Solution Approach 1:
The system dynamically optimizes fee structures and interest rates based on individual customer risk profiles and financial situations. By changing pricing parameters rather than applying uniform high fees, the bank maintains profitability through risk-based pricing while reducing the financial burden on customers who qualify for lower rates, thus resolving the contradiction between revenue and customer satisfaction
Solution Approach 2:
The patent implements feedback loops where customer financial data continuously informs pricing decisions. The system monitors customer transactions, risk changes, and portfolio performance, then adjusts fees and rates accordingly. This feedback mechanism ensures profitability is maintained through data-driven pricing while automatically reducing charges when customer situations improve, balancing bank revenue with customer affordability
Data Source
AI summary
A system and method for optimizing spends of a customer are disclosed. The system includes a processor that is configured to receive financial data associated with a customer, perform an analysis of one or more interest rates associated with the customer, perform an analysis of a risk profile associated with the customer, and analyse a relationship between the customer and respective bank over a threshold time period. The processor is further configured to determine a spend optimization suggestion for the customer based on the one or more interest rates analysis, the risk profile analysis, the relationship analysis, and a current financial situation of the customer. In addition, the processor displays the spend optimization suggestion to the customer on their respective device.