Automated Offer Strategy Generation Using Causal Models
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Solution Overview
Problem
Conventional methods for providing offers to customers, such as credit line increases, often require significant manual expertise and time, leading to high costs and inefficiencies.
Innovation Solution
The use of data-driven approaches, including decision trees and causal models, to determine optimal offers based on customer attributes, with the ability to modify attributes and add noise to optimize strategy performance, allowing for automated generation of strategies that maximize business efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed strategies such as fixed decision trees are used to decide credit line increases, then the strategy formation requires significant manual expertise from analysts and domain experts, but this leads to high time consumption, high effort, and high costs
Solution Approach 1:
The system enables automated strategy formation through machine learning models that self-optimize offer strategies without requiring manual expertise from analysts. The causal model automatically learns from historical data and generates optimized decision trees, eliminating the need for human experts to manually create and refine strategies.
Solution Approach 2:
The patent replaces the mechanical process of manual strategy formation with an automated computational system. Machine learning algorithms and causal models substitute human analysts in the strategy development process, transforming manual expertise into automated intelligent systems that can process data and generate strategies autonomously.
2Measurement precision
If fixed strategies such as fixed decision trees are used to decide credit line increases, then the strategy formation requires significant manual expertise from analysts and domain experts, but this leads to high effort and high costs
Solution Approach 1:
The system enables automated strategy formation through machine learning models that self-optimize offer strategies without requiring manual expertise from analysts. The causal model automatically learns from historical data and generates optimized decision trees, eliminating the need for human experts to manually create and refine strategies.
Solution Approach 2:
The patent replaces the mechanical process of manual strategy formation with an automated computational system. Machine learning algorithms and causal models substitute human analysts in the strategy development process, transforming manual expertise into automated intelligent systems that can process data and generate strategies autonomously.
3Ease of manufacture
If data-driven approaches with decision trees and causal models are used to determine optimal offers, then manual expertise requirements are reduced and costs are lowered, but computing requirements increase
Solution Approach 1:
The system performs preliminary data processing and model training in advance to prepare optimized strategies. Historical data is pre-processed and causal models are trained beforehand, so that when offers need to be made, the system can quickly retrieve and apply pre-computed strategies without requiring intensive real-time computing resources.
Solution Approach 2:
The patent uses decision trees as simplified copies or representations of complex causal models. The causal model learns from historical data and generates decision trees that capture the essential decision logic in a computationally efficient format, allowing the system to use lightweight tree structures instead of running complex models for every offer decision.
Data Source
AI summary
Optimal strategies for providing offers to a plurality of customers are generated. A plurality of categorical attributes (for example, gender and residential status) and ordinal attributes (for example, risk score and credit line utilization) can be determined. Values of one of more categorical attributes can be changed as per a transition probability table. Some probabilities can be varied to determine a first tradeoff, based on which a first updated strategy can be generate Further, noise can be added to one or more ordinal attributes. Standard deviation of a noise distribution associated with the noise can be varied so as to determine a second tradeoff, based on which a second updated strategy can be generated. The second updated strategy can be an update of the first updated strategy. Offers can be provided to the plurality of customers in accordance with the second updated strategy.