Automated Customer Scoring System with Dynamic Criteria
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Solution Overview
Problem
Banks face challenges in accurately and uniformly evaluating customer relationships due to human bias and errors in manual scoring systems, leading to inconsistent data collation and calculation, which affects the effectiveness of incentive programs and customer retention.
Innovation Solution
A system comprising a central database and server with modules for criteria configuration, data processing, and scoring, which automatically computes customer scores based on financial and personal data, using input parameters, transformation criteria, and weightage configuration to provide unbiased and uniform scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual scoring systems are used by bankers, then personalized customer evaluation can be performed, but human bias and errors lead to inconsistent and inaccurate scoring
Solution Approach 1:
The patent replaces the manual mechanical scoring process performed by bankers with an automated computer-based system. The system uses software modules to automatically calculate relationship scores based on predefined criteria and bank data, eliminating human bias and errors while ensuring consistent application of scoring rules across all customers.
Solution Approach 2:
The system enables self-service by allowing the automated scoring system to independently evaluate customer relationships without requiring manual intervention from bankers. The computer automatically retrieves data, applies transformation criteria, and generates relationship scores, making the process efficient and reliable.
2Reliability
If automated scoring systems are implemented, then scoring consistency and accuracy are improved, but system complexity increases
Solution Approach 1:
The automated scoring system is divided into distinct functional modules: a data retrieval module that collects customer information, a transformation criteria module that defines scoring rules, and a score calculation module that computes relationship scores. This segmentation makes the complex system manageable, maintainable, and easier to implement while ensuring consistent scoring across the bank.
3Productivity
If manual data collation is performed, then flexibility in data selection is maintained, but time consumption and human error increase
Solution Approach 1:
The system replaces manual data collation with automated computer-based data retrieval and processing. The system automatically accesses bank databases, retrieves relevant customer data, and processes information through predefined transformation criteria, significantly reducing processing time and eliminating human errors associated with manual data handling.
4Measurement precision
If incentive programs are designed manually, then customization to customer needs is possible, but incorrect rule codification and calculation errors occur
Solution Approach 1:
The system provides dynamic adaptability by allowing banks to configure transformation criteria and scoring rules according to their specific incentive program requirements. The automated system can be customized to reflect different bank policies, customer segments, and incentive structures while maintaining calculation accuracy through consistent application of predefined rules by the computer system.
Data Source
AI summary
Disclosed is a system for scoring customers of a financial institution based on financial data. The system includes a central database that stores a plurality of modules, a central server that processes the plurality of modules and a display unit that displays the processed plurality of modules. The plurality of modules includes a criteria configuration module, a data module, and a computation module. The criteria configuration module includes a metric module to receive the input parameters required to evaluate the score, and a measurement module for defining transformation criteria to be applied on the data corresponding to the input parameters. The computation module includes a metric evaluation module to compute and applies the transformation criteria to the values of the input parameters, and a scoring module coupled to the metric evaluation module to automatically compute and display the score of the customers based on the values retrieved from the metric evaluation module.