Actimize Ltd.

Shevale Ashish Suhas

Debabrata Pati

Akshaykumar Bhausaheb Tilekar

A computerized-method for generating a dataset for a Machine Learning (ML) model for an increased accurate financial crime detection from an initiation stage of the ML model implementation. The computerized-method includes retrieval of financial transaction records from a database of financial transaction records to arrange a dataset of financial transaction records, according to preconfigured techniques. Then, processing the records in the dataset; Then, operating feature engineering on preselected anomalous related features to yield probabilistic categorical features and to yield probabilistic numerical features, and then combining the probabilistic categorical features with the probabilistic numerical features to generate a complex features dataset, and providing the probabilistic categorical features, the probabilistic numerical features and the complex features dataset to an ML model, thus, increasing accuracy of detection that is performed right from an initiation stage of the ML model implementation.

Probabilistic feature engineering technique for anomaly detection

A computerized-method for generating a dataset for a Machine Learning (ML) model for an increased accurate financial crime detection from an initiation stage of the ML model implementation. The computerized-method includes: retrieval of financial transaction records from a database of financial transaction records to arrange a dataset of financial transaction records, according to preconfigured techniques. Then, processing the records in the dataset; Then, operating feature engineering on preselected anomalous related features to yield probabilistic categorical features and to yield probabilistic numerical features, and then combining the probabilistic categorical features with the probabilistic numerical features to generate a complex features dataset, and providing the probabilistic categorical features, the probabilistic numerical features and the complex features dataset to an ML model, thus, increasing accuracy of detection that is performed right from an initiation stage of the ML model implementation.

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Pune, India

Shevale Ashish Suhas