Life Event Bank Ledger Transaction Clustering
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Solution Overview
Problem
Conventional bank/credit card statements are dull and inefficient, failing to provide users with a comprehensive overview of their spending habits by merely presenting account activity in chronological order without insight into transaction patterns or relatedness.
Innovation Solution
A system and method that uses machine learning to group transactions into clusters based on relatedness, assigning life events to these clusters, and generating a ledger that includes life events and associated transactions, providing insights into user spending habits through an interactive graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional chronological listing of transactions is used, then the ledger is simple to generate and maintain, but it fails to provide comprehensive insights into spending habits and transaction patterns
Solution Approach 1:
The patent replaces manual analysis of transaction patterns with machine learning algorithms that automatically cluster transactions and identify life events. This substitution enables comprehensive spending habit insights without proportionally increasing operational complexity, as the automated system processes data efficiently at scale.
Solution Approach 2:
The system performs self-service by automatically analyzing transaction data, clustering related transactions, identifying life events, and generating the enhanced ledger without requiring manual intervention. This automation maintains simplicity while delivering comprehensive spending insights through unsupervised machine learning processes.
2Loss of information
If transactions are grouped into clusters with life events, then user understanding of spending patterns is enhanced, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary clustering and life event identification on transaction data before final ledger generation. By pre-processing and organizing transactions into meaningful groups in advance, the system reduces the computational burden during final ledger assembly, thereby minimizing overall processing time while maintaining comprehensive spending pattern analysis.
3Adaptability or versatility
If machine learning clustering is applied to identify life events, then the ledger becomes more informative and engaging, but the system complexity and computational requirements increase
Solution Approach 1:
The patent employs machine learning algorithms to automatically perform clustering and life event identification, replacing complex manual analysis processes. This substitution delivers highly informative and adaptable ledgers that capture nuanced spending patterns while managing system complexity through automated, scalable computational approaches.
Solution Approach 2:
The machine learning system operates autonomously to cluster transactions and identify life events without requiring manual configuration or intervention. This self-service capability enhances ledger informativeness and adaptability to different user spending behaviors while keeping the system manageable through automated processes that adapt to data patterns independently.
Data Source
AI summary
A system and method for generating a ledger is disclosed herein. A computing system receives, from one or more third party vendors, a plurality of transactions associated with a user. The computing system parses the plurality of transactions to identify one or more parameters associated with each transaction of the plurality of transactions. The computing system groups the one or more transactions into one or more clusters based on the identified one or more parameters. The computing system associates a life event to each cluster of the one or more clusters. The computing system interfaces with a client device associated with the user to confirm an associated life event. Upon receiving a confirmation from the user regarding the associated life event, the computing system generates a ledger. The ledger includes the life event and the one or more transactions associated therewith.