Fuzzy Logic Address Matching for Fraud Detection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing systems face challenges in detecting high-risk fraud records due to misdirected or excessive confirmation mailings, which can mask illicit activity, and the difficulty in matching imperfectly entered address data across various databases.

Innovation Solution

A fuzzy logic system is implemented to detect anomalies by matching address fields with physical addresses using fuzzy matching, generating match scores, and presenting anomalies through a user interface, allowing for threshold adjustments to refine results.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional exact matching is used to locate suspicious address changes, then data accuracy is improved, but the ability to detect fraud across varied address formats deteriorates

Engineering Contradiction:

Improveaddress matching accuracyVSAvoidaddress format flexibility

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system changes the matching parameter from exact string equality to fuzzy string similarity. By introducing a match score threshold (e.g., 70% similarity) instead of requiring 100% exact matches, the system can identify suspicious address changes even when address formats vary due to typographical errors, different address styles, or intentional obfuscation by fraudsters.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If confirmation mailings are sent to all address changes, then user notification reliability is improved, but the risk of user harassment and fraud masking increases

Engineering Contradiction:

Improveconfirmation mailing deliveryVSAvoiduser harassment and fraud masking

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The system replaces the mechanical approach of sending mailings to every recorded address change with an intelligent filtering mechanism. By applying fuzzy matching algorithms and match score thresholds before triggering mailings, the system substitutes brute-force notification with targeted, intelligence-based delivery, reducing unnecessary mailings to users while maintaining reliable notification of legitimate changes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback loops where users can report excessive or erroneous mailings, and where the system monitors mailing outcomes. This feedback mechanism allows the system to learn from past performance and adjust its fuzzy matching thresholds and mailing strategies, improving the balance between reliable notification and reducing user harassment over time.

Inventive Principle:

Principle #23Feedback

3Quantity of substance

If multiple databases are used to maintain address data, then data completeness is improved, but the difficulty of locating suspicious activity across databases increases

Engineering Contradiction:

Improvedata completenessVSAvoiddata location complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The system implements a universal fuzzy matching engine that operates across multiple databases simultaneously. Rather than requiring separate search procedures for each database, the single fuzzy matching mechanism can query address fields across all databases using consistent similarity criteria, simplifying the location of suspicious activity while maintaining the benefits of multi-source data collection.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12301618B2Fuzzy logic modeling for detection and presentment of anomalous messaging

Publication Date: 2025.05.13 WELLS FARGO BANK NA

AI summary

Disclosed is an approach that applies a fuzzy logic model that may involve fuzzy-matching a plurality of address fields to determine a common physical address, and determining a number of communiques directed to that address with reference to a threshold that may determine an excessive number of communiques. The plurality of address fields may also be fuzzy-matched to information in a fraud-risk database which may comprise a fraud-risk address. One or more matches may be presented to a user who may adjust the views of the various matches, track various trends within the data, and harmonize the various address fields relating to a physical address.