Machine learning anti-fraud monitoring system based on transaction data

Abstract

The invention discloses a machine learning anti-fraud monitoring system based on transaction data. The system comprises a management platform, an ETL module, a sampling engine, a stream processing engine, a training engine, a prediction engine and a decision engine. The stream processing engine rapidly extracts and calculates characteristics of the huge original transaction data through streamed big data processing, the representative characteristics are obtained from the huge original transaction data, and information in the data is sufficiently extracted. In the model training module, various machine learning models and ensemble learning frameworks optimized for the capital loss ratio and the black sample recall ratio are used, and a composite model optimized for an indicator is obtained. The over-fitting and unstable defects due to a single model are overcome, and the stability and the generalization ability of the model are improved; according to a preset update time, the model training module automatically obtain the latest data and trains the model again, accordingly the model keeps the effectiveness all the time, and the problem of model inefficiency due to fraud variation is avoided.

Description

technical field [0001] The invention relates to the financial field, in particular to a machine learning anti-fraud monitoring system based on transaction data. Background technique [0002] The vigorous development of Internet technology has created a new round of financial revolution, but too fast growth also contains great blindness, accompanied by increasingly serious risks of fraud. At present, the more common fraud monitoring models include risk policies based on big data, anti-fraud systems, and elite risk control teams. Although most payment institutions have fraud detection systems in place, most still rely on elite teams to conduct rule induction based on case analysis. However, the emergence of fraudulent means and the inconsistency of transaction behavior have brought difficulties to rule induction. At the same time, the current rule system is difficult to maintain its robustness, and its performance will decrease with the expansion of the rule system. It canno...

AI Technical Summary

Problems solved by technology

However, the emergence of fraudulent means and the inconsistency of transaction behavior have brought difficulties to rule induction

At the same time, the current rule system is difficult to maintain its robustness, and its performance will decrease with the expansion of the rule system. It cannot guarantee high precision and high recall at the same time, thereby reducing user experience.

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