Financial Analytics Engine Integrating SHAP Insights Into CRM
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Solution Overview
Problem
Conventional customer relationship management systems lack the capability to provide human-readable insights explaining model recommendations to users, leading to mistrust and inefficiency in decision-making, particularly in financial institutions where understanding customer needs and predicting churn or product opportunities is crucial.
Innovation Solution
A financial data analytics engine is integrated into the customer relationship management system, utilizing statistical and machine learning models, SHAP values, and overlay rules to generate human-readable insights and expected monetary impact, enabling personalized product recommendations and attrition alerts by aggregating and transforming customer data across different levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional customer relationship management systems use machine learning models to generate recommendations, then predictive analysis capability is improved, but user understanding and trust of recommendations deteriorates
Solution Approach 1:
The system introduces an explanation layer as an intermediary between the machine learning model and the user. This layer translates complex model outputs into human-readable explanations, allowing users to understand why recommendations are made without compromising the automated predictive analysis capability.
Solution Approach 2:
The system changes the representation of model outputs from raw predictive values to explained insights with confidence scores and key factors. This parameter transformation makes the information accessible to users while maintaining the underlying automated analysis functionality.
2Loss of information
If the system provides detailed model explanations to users, then user understanding is improved, but system complexity increases
Solution Approach 1:
The explanation generation is segmented into separate modular components that process different aspects of model outputs independently. This segmentation allows the system to provide detailed explanations without requiring the entire system to be overly complex, as each component handles specific explanation tasks.
Solution Approach 2:
The system pre-processes and structures model outputs into explanation-ready formats before presenting to users. This preliminary organization of information reduces the complexity required during actual explanation generation, as the data is already prepared in an interpretable structure.
3Measurement precision
If the system aggregates and transforms customer data across multiple levels, then recommendation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The data aggregation process is divided into separate operational levels (customer level, account level, relationship level) that can be processed independently and then combined. This segmentation allows high-accuracy multi-level analysis while managing processing complexity through structured, modular operations.
Solution Approach 2:
The system performs preliminary data transformation and aggregation at each level before final recommendation generation. This pre-processing organizes complex multi-level data into manageable structures, reducing the complexity required for final analysis while maintaining high recommendation accuracy.
Data Source
AI summary
Methods, systems, and computer storage media for providing financial data analytics recommendations using a data analytics engine in a customer relationship management system. The recommendations can be a lead that is information associated with a model-generated suggested consumer solution, an alert of increased risk of attrition, or an alert of increased risk of default. The data analytics engine is configured to generate target variables associated with financial products or the customer relationship and utilize modeling techniques and apply rules to generate recommendations. Operationally, the recommendations are generated based on a data analytics model. Generating the recommendations is based on feature variables that are generated based on aggregation and transformation of customer data and utilizing machine learning models to detect patterns in the customer data using the feature variables. The recommendations can be presented via a financial data analytics interface along with insights that provide plain text explanations of the recommendations.