Automated Transaction Data Clustering for Financial Institutions

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Solution Overview

Problem

Customers of financial institutions often struggle to track their account activities, leading to missed opportunities and unfulfilled potential due to unawareness of account details and policies, while institutions face challenges in organizing and analyzing large volumes of data to inform customers effectively.

Innovation Solution

A system and method that transforms historical transaction data by identifying patterns, grouping textual values into clusters, and applying similarity gauges to classify and filter data, using algorithms like double metaphone for phonetics and Jaccard distance to assign cluster IDs, and optionally searching the internet for accurate matches to enhance data clustering and information value.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If financial institutions manually organize and analyze large volumes of transaction data to inform customers, then customer service quality improves, but operational cost and time consumption increase significantly

Engineering Contradiction:

Improvecustomer service qualityVSAvoidoperational complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system enables automated self-service through algorithms that independently perform data organization, pattern recognition, and customer notification tasks. The automated trigger system processes transaction data, identifies patterns, and sends notifications without human intervention, allowing the system to serve itself and eliminating manual operational complexity while maintaining service quality

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical processes of data analysis and customer communication are replaced with automated computational algorithms. The system uses pattern recognition algorithms, clustering algorithms, and automated triggering mechanisms to substitute human operators, thereby reducing operational complexity while maintaining or improving service reliability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If financial institutions deploy automated data analysis systems, then productivity improves, but system complexity increases

Engineering Contradiction:

Improvedata processing efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The automated system is segmented into distinct functional modules: data collection module, pattern recognition module, clustering module, trigger determination module, and notification module. Each module performs a specific function independently, making the overall complex system manageable through modular design while maintaining high productivity through automated processing

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The automated analysis system is designed as a universal platform that can handle multiple types of transaction data, identify various patterns, perform different clustering operations, and generate diverse notifications. This multi-functional design consolidates multiple capabilities into a single system, improving productivity without proportionally increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Loss of energy

If financial institutions fail to timely notify customers of account activities and offers, then operational cost decreases, but loss of information and missed opportunities increase

Engineering Contradiction:

Improveoperational costVSAvoidmissed opportunities

Core Design Contradiction:

Loss of energyVSLoss of information

Solution Approach 1:

The system implements continuous feedback loops where transaction data is constantly monitored, patterns are recognized in real-time, and automated triggers are activated based on predefined conditions. This feedback mechanism ensures timely customer notifications about account activities and offers, preventing loss of information and missed opportunities while maintaining cost efficiency through automation

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary actions by pre-defining trigger conditions and notification templates before events occur. When specific transaction patterns are detected, pre-configured actions are automatically executed, ensuring timely customer communication without requiring real-time human decision-making, thereby preventing missed opportunities while controlling operational costs

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10049155B2System for mending through automated processes

Publication Date: 2018.08.14 BANK OF AMERICA CORP

AI summary

Systems and methods are provided for transforming historical data collected in response to one or more triggering events, in order to classify textual values. Embodiments access a plurality of textual values from historical transaction data; identify one or more distinct patterns within the plurality of textual values; group the textual values based on the one or more distinct patterns, thereby forming one or more clusters; apply a similarity gauge to the textual values of each of the clusters to determine similarity or dissimilarity among the textual values of each cluster; and filter the textual values of each cluster to determine which textual values belong in each cluster, wherein the textual values that belong are cluster values. Some embodiments also remove undesired characters from the textual values, and in some cases identifying the distinct patterns includes comparing pronunciations and/or phonetics of the textual values.