AI Advisor Recommendation Using Client Clustering and Classification
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Solution Overview
Problem
Conventional wealth-management computer systems struggle to analyze large volumes of client financial data in a timely manner, failing to provide proactive advisor recommendations for numerous clients.
Innovation Solution
A computerized method utilizing a clustering model and random-forest classifiers to partition and classify clients into financial solution categories, generating proactive recommendations through a computer network system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional wealth-management computer systems are used to analyze large volumes of client financial data, then the system can process data, but the analysis cannot be completed in a timely manner
Solution Approach 1:
The patent segments the large volume of client financial data by implementing a clustering model that divides clients into multiple clusters based on their financial characteristics. This segmentation allows the system to process each cluster separately using random-forest classifiers, significantly improving analysis speed while handling large volumes of data efficiently.
2Ease of operation
If manual wealth management methods are used, then personalized advisor recommendations can be generated, but the process is time-consuming and cannot scale to numerous clients
Solution Approach 1:
The patent implements self-service by training random-forest classifiers to automatically generate personalized advisor recommendations without human intervention. The system autonomously analyzes client financial data, identifies suitable financial products, and generates recommendations, thereby maintaining personalization quality while scaling throughput to handle numerous clients simultaneously.
3Reliability
If proactive wealth management analysis is implemented for all clients, then potential financial needs can be identified early, but the computational resources required become excessive
Solution Approach 1:
The patent applies preliminary action by first using a clustering model to group clients into segments based on their financial profiles before applying resource-intensive random-forest classification. This preliminary segmentation reduces the computational burden by processing clients in smaller, organized groups, thereby maintaining accurate identification of potential financial needs while reducing overall computational resource consumption.
Data Source
AI summary
Computer systems, apparatuses, processors, and non-transitory computer-readable storage devices configured for executing a method for generating proactive advisor recommendation using artificial intelligence. The method has the steps of: partitioning a plurality of clients using a clustering model based on data of the plurality of clients for clustering the plurality of clients into a plurality of client clusters; classifying the clients of at least a first client cluster of the plurality of client clusters into a plurality of client classifications by using one or more random-forest classifiers; and generating financial recommendations for the clients of at least a first client classification of the plurality of client classifications.