Fraud Risk Index Calculation for Money Transfer Agents
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Money-transfer systems face challenges in assessing fraud risk due to the complexity of identifying fraudulent activities across multiple agents and locations, with existing mechanisms being costly, under-inclusive, or over-inclusive, and struggling to associate transactions with specific agents, especially when transaction information is incomplete or misleading.
Innovation Solution
A computer-implemented method and system that calculates a fraud-risk index for money-transfer agents based on customer complaints, considering factors like complaint frequency, transaction history, and transaction amounts, using a risk processor to retrieve and analyze data from a complaint database to predict fraudulent activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing fraud assessment mechanisms are used, then fraud detection can be performed, but the cost is high and the coverage is either under-inclusive or over-inclusive
Solution Approach 1:
The system segments the fraud assessment process into distinct components: data collection from multiple sources (complaints, transactions, agent information), data storage in structured databases, and analysis through the risk processor. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining comprehensive fraud detection coverage.
Solution Approach 2:
The risk processor acts as an intermediary between raw data (complaints, transactions) and fraud assessment results. It mediates the complex analysis by retrieving relevant data, applying assessment criteria, and generating risk scores, thereby simplifying the overall assessment mechanism while improving reliability.
2Reliability
If comprehensive data collection is performed across all agents and locations, then fraud risk assessment coverage is improved, but the complexity of associating transactions with specific agents increases
Solution Approach 1:
The system segments transaction data by associating it with specific agents through unique agent identifiers. Each agent's transactions, complaints, and risk factors are stored as separate record sets in the database, allowing comprehensive data collection without increasing association complexity. The risk processor retrieves only the segmented data relevant to each agent being assessed.
Solution Approach 2:
The system creates structured copies of agent information and transaction data in a standardized format within the database. Instead of directly querying complex source systems, the risk processor works with copied, pre-organized data records that contain all necessary association information, thereby simplifying the assessment process while maintaining comprehensive coverage.
3Measurement precision
If detailed transaction information is analyzed, then fraud detection precision is improved, but the cost and time required for assessment increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing transaction information, agent data, and complaint records in the database before fraud assessment is needed. The risk processor retrieves this pre-organized data and applies assessment criteria without requiring real-time data gathering, thereby maintaining high detection precision while reducing assessment time and cost.
4Reliability
If multiple data sources are integrated for fraud assessment, then the reliability of fraud prediction is improved, but the device complexity increases
Solution Approach 1:
The system merges multiple data sources (customer complaints, transaction records, agent information) into a unified database structure. The risk processor combines these diverse data types through standardized retrieval and analysis processes, achieving reliable fraud predictions while managing integration complexity through centralized data management rather than distributed integration logic.
Data Source
AI summary
In some embodiments, a non-transitory computer-implemented method of assessing a fraud risk is provided. The method can include: receiving data associated with a plurality of customer complaints, wherein data associated with each of the customer complaints at least suggests that a money-transfer transaction did not complete as expected, and wherein data associated with each of the customer complaints is associated with one or more of a plurality of money-transfer agents; electronically storing the data related in a complaint database; and for each of the plurality of money-transfer agents, using a risk processor to: retrieve data associated with the money-transfer agent from the complaint database, and determine a fraud-risk index for the money-transfer agent based on the retrieved data.