Clustering System for Automated Credit Card Action Determination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods in the financial industry face challenges in providing an automated or semi-automated mechanism for determining appropriate actions for credit card transactions, especially when multiple actions are possible, such as investigating fraud or approving loans, due to the complexity of handling numerous transactions and varying customer behaviors.
Innovation Solution
A computer-implemented system that assigns entities to segments based on their characteristics, calculates membership probabilities, and uses action information associated with these segments to determine the most probable actions, allowing for automated decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated or semi-automated mechanisms are implemented to determine actions for transactions, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments the complex decision-making process into distinct components: transaction data collection, characteristic extraction, segment assignment, membership probability calculation, and action determination. Each component handles a specific aspect of the problem, making the overall system more manageable and implementable despite the complexity of the task.
Solution Approach 2:
The patent introduces segment assignments with membership probabilities as an intermediary layer between raw transaction characteristics and final action decisions. This intermediary representation simplifies the decision-making process by transforming complex multi-dimensional transaction data into probabilistic segment memberships that can be more easily mapped to specific actions.
2Adaptability or versatility
If multiple possible actions are considered for each entity, then adaptability is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system uses membership probabilities as feedback to guide action selection. By calculating the probability of entity membership in different segments, the system receives quantitative feedback that directly informs which actions are most appropriate, transforming a complex multi-action decision problem into a probabilistic selection process based on measured membership values.
Solution Approach 2:
The patent transforms the action determination problem by changing the parameter space from direct action selection to membership probability calculation. Instead of directly measuring which action to take, the system measures membership probabilities across multiple segments and derives actions from these probability values, making the measurement process more tractable.
Data Source
AI summary
Computer-implemented systems and methods for determining one or more actions to be taken with respect to a first entity. A computer-implemented method can be configured to receive data that is related to characteristics of the first entity as well as data that is related to a plurality of segments. Assignments are determined between the first entity and the segments based upon the characteristics of the first entity and the characteristics associated with the segments. A determined assignment includes a membership probability that is indicative of how probable is membership of the first entity with respect to a segment. One or more actions are determined for the first entity based upon the membership probabilities and action information associated with the assigned segments.