17528813. COMPUTER-BASED SYSTEMS CONFIGURED FOR UTILIZING MACHINE-LEARNING ENRICHMENT OF ACTIVITY DATA RECORDS AND METHODS OF USE THEREOF simplified abstract (Capital One Services, LLC)
COMPUTER-BASED SYSTEMS CONFIGURED FOR UTILIZING MACHINE-LEARNING ENRICHMENT OF ACTIVITY DATA RECORDS AND METHODS OF USE THEREOF
Organization Name
Inventor(s)
Lukiih Cuan of Washington DC (US)
Lin Ni Lisa Cheng of New York NY (US)
Michelle S. Olenoski of Washington DC (US)
COMPUTER-BASED SYSTEMS CONFIGURED FOR UTILIZING MACHINE-LEARNING ENRICHMENT OF ACTIVITY DATA RECORDS AND METHODS OF USE THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 17528813 titled 'COMPUTER-BASED SYSTEMS CONFIGURED FOR UTILIZING MACHINE-LEARNING ENRICHMENT OF ACTIVITY DATA RECORDS AND METHODS OF USE THEREOF
Simplified Explanation
The patent application describes a system and method for identifying potential rejection claims in completed activity records of users. Here is a simplified explanation of the abstract:
- The system determines if a completed activity record of a user contains a predictive characteristic that suggests a potential rejection claim.
- Based on this determination, the system generates a request for additional activity data related to the completed activity.
- The system receives multiple completed activity records from other users and uses them to train a machine learning model that can identify characteristics associated with known entities.
- When the predictive characteristic is present, the system applies the trained machine learning model to identify known-entity data related to the completed activity of the user.
- Finally, the system updates the completed activity record of the user's completed activity.
Potential Applications:
- Fraud Detection: The system can be used to identify potential fraudulent claims or activities by analyzing activity records and predicting rejection claims.
- Risk Assessment: By analyzing completed activity records, the system can help assess the risk associated with certain activities or claims.
- Customer Service Improvement: The system can provide insights into potential rejection claims, allowing companies to improve their customer service and address issues proactively.
Problems Solved:
- Identifying Potential Rejection Claims: The system solves the problem of identifying completed activities that may result in rejection claims, allowing for proactive measures to be taken.
- Efficient Data Analysis: By using machine learning, the system can quickly analyze large amounts of completed activity records and identify patterns or characteristics associated with potential rejection claims.
Benefits:
- Improved Efficiency: The system automates the process of identifying potential rejection claims, saving time and resources.
- Enhanced Risk Management: By identifying potential rejection claims, the system helps mitigate risks and prevent financial losses.
- Better Customer Experience: Proactively addressing potential rejection claims improves customer satisfaction and loyalty.
Original Abstract Submitted
Systems and methods are disclosed including determining that a first activity data of a completed activity record of a user comprises a predictive characteristic indicative of a potential rejection claim. The computing device produces a request for a second activity data of the completed activity, based at least in part on the first activity data. The computing device receives a plurality of completed activity records related to a plurality of other users and trains an entity-identifying machine learning model to identify a plurality of entity-identifying characteristics related to a plurality of known entities to obtain a trained entity-identifying machine learning model. The computing device applies, when the predictive characteristic is present, the trained entity-identifying machine learning model to identify a known-entity data related to the completed activity of the user. The computing device updates the completed activity record of the completed activity of the user.
