Clustering-based data selection for optimization of risk predictive machine learning models
US12141806B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 30, 2021 |
| Grant date | Nov 12, 2024 |
| Priority date | — |
| Expiry date | Sep 14, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/24
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A risk-prediction-preparation module to generate a risk-prediction-model, is provided herein. The risk-prediction-preparation module includes accessing a data-storage of transactions to operate a group-by operation on transactions related to data-points, according to a logical-entity into entities. Then, clustering entities of a clean-financial dataset into clusters. Selecting data-points of: (a) entities from the clusters to a first dataset and (b) a preconfigured amount of entities randomly to a second dataset. Selecting all entities that have at least one ‘fraudulent’ data-points in at least one related data-point to add all the entities to the first dataset and the second dataset. Using vectorized and scaled extracted features for training a first machine-learning-model of fraud detection on the first dataset and training a second machine-learning-model of fraud detection on the second dataset to collect results. Using the results for combining the first machine-learning-model and the second machine-learning-model to an ensemble machine-learning-model for risk-prediction.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.