Model multiphase adaptive correction method for transaction fraud risk artificial intelligence modeling

Abstract

The model multiphase adaptive correction method for transaction fraud risk artificial intelligence modeling comprises the following steps: step A, obtaining an original transaction fraud evaluation score; step B, correcting the values and the selection of the original characteristic values; c, correcting an intermediate output variable of the risk evaluation model; and D, correcting the original transaction fraud evaluation score. According to the method, the characteristics of the behavior finger model and the risk finger model are correspondingly adjusted according to the variables used by the model through multi-phase self-adaptive correction, so that the negative effects of the drifts on the model performance are partially eliminated, parameter correction can be performed on the trained anti-fraud model, the performance and adaptability of the model are improved, and the robustness of the model is improved. And normal decay of the model to time is slowed down. Normal decay of the artificial intelligence model is slowed down, so that a favorable technical support is provided for the artificial intelligence model to achieve a better data recognition and judgment effect. On the basis, the method has a good application prospect.

Description

technical field [0001] The invention relates to the field of anti-fraud technology applied in the financial sector, in particular to a model multiphase adaptive correction method for artificial intelligence modeling of transaction fraud risk. Background technique [0002] At present, financial fraud risk control has entered the era of big data and artificial intelligence. As an important technical link in the process of financial transaction risk control, artificial intelligence models have achieved remarkable results in international and domestic anti-trade fraud struggles. Artificial intelligence modeling will use the historical characteristics of transaction risk in various transaction parameter sets (transaction time, transaction location, transaction amount, etc.), the behavior habits of both parties to the transaction, and historical characteristics of other types of data. Since both normal trading activities and trading fraud will change with the evolution of economic...

AI Technical Summary

Problems solved by technology

Since both normal trading activities and trading fraud will change with the evolution of economic culture and the progress of trading technology, the historical characteristics of transaction risk, the behavior habits of both parties to the transaction and the historical characteristics of other types of data will also change with time, resulting in the model being App performance decays over time

Using artificial intelligence methods to correct the parameters of the trained model will enhance the performance and adaptability of the model, thereby slowing down the decay of the model in application, but such correction techniques have not been applied in known technologies