Fraud detection using emotion-based deep learning model
US12008579B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 9, 2021 |
| Grant date | Jun 11, 2024 |
| Priority date | — |
| Expiry date | Aug 9, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/08
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are described for determining a likelihood that a customer communication is fraudulent using one or more machine learning models. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: receive a set of emotion factor values for communication data of a current communication associated with a customer, wherein each emotion factor value indicates a measure of a particular emotion factor in the current communication; classify, using an emotion variance model running on the one or more processors, the current communication into an emotional fraud category based on the set of emotion factor values for the current communication associated with the customer; and determine a risk score for the current communication indicative of a probability that the current communication is fraudulent based on at least the emotional fraud category for the current communication.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.