Financial Transaction Data Aggregation and Fraud Detection
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Solution Overview
Problem
Current financial transaction systems are inefficient in investigating fraud and identity theft due to storage constraints and the need for manual, time-consuming data aggregation, making it difficult to detect and prevent repeated fraudulent activities across multiple sources and formats.
Innovation Solution
A system that aggregates financial transaction data from multiple sources, converts it into a single format, and uses a database-driven approach with parsing and compression techniques to enable rapid searching and reporting of suspicious transactions, allowing for proactive detection of fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If financial transaction data is stored in original transactional systems with archival storage, then data retention is maintained, but data retrieval and analysis speed deteriorates to weeks or months
Solution Approach 1:
The patent segments the large archival dataset into smaller, manageable blocks that are distributed across multiple storage locations. Each block is indexed in a separate database table, enabling parallel processing and faster retrieval. This segmentation transforms the single-point sequential access bottleneck into a multi-point parallel access system, dramatically reducing investigation time from weeks/months to minutes/hours.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and indexing transaction data into standardized formats before actual fraud investigations begin. Data is block ized, formatted, and indexed in advance, creating a ready-to-query structure that eliminates the need for time-consuming data aggregation and formatting during live investigations.
2Adaptability or versatility
If data is aggregated from multiple financial institutions with different formats, then comprehensive fraud detection capability is improved, but data processing complexity increases
Solution Approach 1:
The patent creates a universal data block format that can represent transactions from multiple different financial institutions with varying formats. The standardized block structure includes fields for institution identifier, transaction details, and metadata, allowing the same processing pipeline to handle diverse data sources without requiring separate processing logic for each institution.
Solution Approach 2:
The patent transforms heterogeneous data from different institutions by changing its parameters - converting various institutional formats into a unified block structure with standardized fields. This parameter transformation includes mapping different account number formats, transaction codes, and data structures into a common representation that maintains the essential transaction information while enabling consistent processing.
3Extent of automation
If traditional transactional systems are used for fraud investigation, then system reliability is maintained, but automated fraud detection capability deteriorates
Solution Approach 1:
The patent introduces an intermediary data layer between the original transactional systems and the fraud detection analysis tools. This intermediary layer consists of standardized data blocks and indexing structures that translate raw transactional data into a format optimized for automated querying and pattern recognition, enabling fraud detection automation without disrupting the stability of core transactional systems.
Data Source
AI summary
A database-driven software application may be provided that is configured to keep a record of mainframe activity for various financial transactions and provide relationships between various transactional features. The information of these financial transactions may originate from a single system in a single data format or may be integrated into a single consistent format from a plurality of systems in a plurality of formats. Such an application may enable the reporting of anomalous events and/or the review of activities conducted by a financial associate (e.g., an employee of the financial institution) and/or those impacting a specific customer or account. The system may operate by parsing daily feeds of raw mainframe logs and extracting relevant details and placing information about each transaction in a data warehouse.