LLM-Based Product Recommendation System for Financial Services
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Solution Overview
Problem
In the financial sector, customers face challenges in determining suitable products from a multitude of offerings by payment processors like MasterCard, due to the complexity of each customer's unique needs and the vast array of products available. This leads to inefficiencies in product selection and optimization, resulting in delayed implementations and administrative issues.
Innovation Solution
A computer-implemented method and system utilizing a Large Language Machine learning (LLM) model to determine suitable products and operating parameters for performing specific tasks by entities. The system receives task-specific queries, accesses product-specific data, entity-specific data, and predefined rules, and generates query response messages that include lists of suitable products and their corresponding operating parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customers conduct manual research to determine suitable products from a vast array of offerings, then they can make informed decisions, but the process becomes time-consuming and complex
Solution Approach 1:
The patent introduces an automated recommendation system that acts as an intermediary between customers and the vast array of financial products. This system uses machine learning models and natural language processing to interpret customer queries, retrieve relevant product information from databases, and generate personalized recommendations, thereby eliminating the need for customers to manually research products while maintaining high selection accuracy
Solution Approach 2:
The patent replaces the manual mechanical process of customer research and product evaluation with an automated computational system. The system uses database queries, machine learning algorithms, and natural language generation to automatically determine suitable products based on customer needs, substituting the time-consuming manual research process with efficient automated processing
2Adaptability or versatility
If product teams offer comprehensive product coverage to meet diverse customer needs, then customer requirements are better addressed, but the complexity of product selection increases
Solution Approach 1:
The patent creates a universal recommendation system that can handle diverse customer needs and product types through a single interface. The system uses natural language processing to understand various customer queries and retrieves information from a comprehensive product database, enabling one system to serve multiple product categories and customer scenarios without increasing selection complexity for end users
Solution Approach 2:
The recommendation system acts as an intermediary layer between the comprehensive product portfolio and customers. It manages the complexity by automatically filtering, retrieving, and presenting only the most relevant products based on customer queries, thereby maintaining full product coverage availability while simplifying the customer's product selection process
3Manufacturing precision
If manual tuning of operating parameters is performed for each customer-specific use case, then optimal product performance is achieved, but the process becomes time-consuming and administratively complex
Solution Approach 1:
The patent replaces the manual mechanical process of parameter tuning with an automated computational system. The system uses machine learning models to analyze customer-specific use cases, retrieve relevant operating parameters from databases, and generate optimized parameter recommendations automatically, eliminating the time-consuming manual tuning process while maintaining high optimization accuracy
Solution Approach 2:
The system performs preliminary actions by pre-storing operating parameters and product configurations in databases during system setup. When a customer query is received, the system quickly retrieves pre-prepared information and generates parameter recommendations without requiring real-time manual analysis, thereby achieving both accuracy and efficiency
4Adaptability or versatility
If multiple products are configured to work in tandem for complex customer tasks, then task capability is enhanced, but coordination and optimization become administratively difficult
Solution Approach 1:
The recommendation system provides a universal interface that handles complex multi-product configurations through automated processing. It uses natural language processing to understand customer task requirements, retrieves information about multiple products from the database, and generates coordinated recommendations with optimized operating parameters, thereby enabling complex task performance without exposing customers to coordination complexity
Data Source
AI summary
Methods and server systems for determining suitable products and operating parameters for performing a task are described herein. Method performed by server system includes receiving task-specific query from entity and determining the task based on the task-specific query. Method includes accessing product-specific data, entity-specific data, and predefined rules from a database based on entity and task. Herein, the product-specific data includes information related to each product, the entity-specific data includes information related to the entity, and the predefined rules indicates rules for implementing the plurality of products. Method includes generating and transmitting, via large language machine learning model, a query response message based on product-specific data, entity-specific data, and predefined rules. Such that the query response message indicates task-specific information that includes a list of the one or more suitable products and operating parameters corresponding to each suitable product for performing the task.