Anti-money laundering characterization parameter analysis system and method

Abstract

An anti-money laundering characterization parameter analysis system includes a processing unit and a storage unit electrically connected to the processing unit. The processing unit calculates an anomaly detection accuracy rate corresponding to each parameter based on the ratio between the number of anomalous cases corresponding to that parameter and the number of alert cases corresponding to that parameter. The processing unit also calculates an anomaly retention rate corresponding to each parameter based on the ratio between the number of anomalous cases corresponding to each parameter and a reference anomaly. The processing unit performs a screening procedure on the parameter data to select N most applicable parameter data points and generates and outputs a parameter screening result containing these N most applicable parameter data points.

Claims

1. A money laundering characterization parameter analysis system, comprising: a processing unit; and a storage unit electrically connected to the processing unit, storing multiple sets of parameter data, multiple alert case counts corresponding to the parameter data, and multiple abnormal case counts corresponding to the parameter data, wherein, For each parameter and the corresponding number of alert cases, the number of alert cases indicates the number of alert transaction cases in multiple transaction case data, and each alert transaction case is one of the transaction case data that the computer determines has an abnormal risk based on the corresponding parameter. For each parameter and the corresponding number of abnormal cases, the number of abnormal cases indicates the number of abnormal transaction case data in the transaction case data, and each abnormal transaction case is one of the alert transaction case data corresponding to the parameter and is manually determined to be abnormal. The processing unit selects the largest abnormal case from the abnormal case numbers as a reference abnormality number. The processing unit calculates an abnormality judgment accuracy rate corresponding to the parameter data based on the proportional relationship between the abnormal case number corresponding to each parameter and the number of alert cases corresponding to the parameter. The processing unit calculates an abnormality case retention rate corresponding to the parameter data based on the proportional relationship between the abnormal case number corresponding to each parameter and the reference abnormality number. The processing unit performs a filtering procedure on the parameter data to select N most applicable parameter data from the parameter data, and generates and outputs a parameter filtering result containing the N most applicable parameter data, where N is an integer greater than or equal to 1. The processing unit performs the filtering procedure in the following ways: using one of the two types of data, namely the anomaly judgment accuracy rate and the anomaly case retention rate, as multiple first clustering indicators, and using the other type of data as multiple first filtering indicators; dividing the parameter data into multiple groups according to the first clustering indicators, and selecting one parameter data from the parameter data in each group as a preliminary candidate parameter data according to the first filtering indicators corresponding to the parameter data in each group; and determining the N most applicable parameter data from the preliminary candidate parameter data.

2. The anti-money laundering characterization parameter analysis system as described in claim 1, wherein, The processing unit determines the N most applicable parameter data from the preliminary candidate parameter data in the following manner: It uses multiple first screening indicators corresponding to the preliminary candidate parameter data as multiple second clustering indicators, and uses multiple first clustering indicators corresponding to the preliminary candidate parameter data as multiple second screening indicators; it divides the preliminary candidate parameter data into multiple groups according to the second clustering indicators, and selects one preliminary candidate parameter data from the preliminary candidate parameter data in each group as an advanced candidate parameter data according to the second screening indicators corresponding to the preliminary candidate parameter data in each group; and it determines the N most applicable parameter data from the one or more advanced candidate parameter data.

3. The anti-money laundering characterization parameter analysis system as described in claim 2, wherein, The processing unit performs the screening procedure by further comprising the following steps before determining the N most applicable parameter data from the one or more advanced candidate parameter data: treating the first screening indicators as multiple third grouping indicators, and treating the first grouping indicators as multiple third screening indicators; dividing the parameter data into multiple groups according to the third grouping indicators; for each group of parameter data, selecting one parameter data from the group of parameter data as another preliminary candidate parameter data according to the third screening indicators corresponding to the parameter data in that group; treating multiple third screening indicators corresponding to the preliminary candidate parameter data as multiple fourth grouping indicators, and treating multiple third grouping indicators corresponding to the preliminary candidate parameter data as multiple fourth screening indicators; Based on the fourth grouping index, the preliminary candidate parameter data are divided into multiple groups. For each group of preliminary candidate parameter data, based on the fourth screening index corresponding to the preliminary candidate parameter data of the group, one preliminary candidate parameter data is selected from the preliminary candidate parameter data of the group as another advanced candidate parameter data.

4. The anti-money laundering characterization parameter analysis system as described in any one of claims 2 and 3, wherein, The processing unit determines the N most applicable parameter data from the one or more advanced candidate parameter data in the following way: it determines whether the retention rate of the abnormal case corresponding to each advanced parameter data is greater than or equal to a retention rate threshold value, and if the determination result is yes, the advanced parameter data is regarded as one of the N most applicable parameter data.

5. The anti-money laundering characterization parameter analysis system as described in claim 1, wherein, The storage unit also stores transaction case data, each transaction case data including a transaction amount and an attribute tag, and each parameter data including multiple amount threshold values ​​corresponding to a case attribute feature; the processing unit performs a check process on each transaction case data according to each parameter data, wherein the processing unit performs the check process by using the attribute tag of the transaction case data as the attribute threshold value corresponding to the case attribute feature in the parameter data that matches the attribute tag as a matching amount threshold value, comparing the transaction amount of the transaction case data with the matching amount threshold value, and when it is determined that the transaction amount is greater than or equal to the matching amount threshold value, the transaction case data is treated as a warning transaction case data corresponding to the parameter data; the processing unit counts the number of warning transaction case data corresponding to each parameter data to obtain the number of warning cases corresponding to the parameter data, and stores the number of warning cases in the storage unit; For each parameter data, the processing unit treats each warning transaction case with an abnormal annotation in the warning transaction case data corresponding to the parameter data as an abnormal transaction case data corresponding to the parameter data, counts the number of abnormal transaction case data corresponding to each parameter data to obtain the abnormal case quantity corresponding to the parameter data, and stores the abnormal case quantity in the storage unit.

6. A method for analyzing anti-money laundering characterization parameters, implemented by an anti-money laundering characterization parameter analysis system, wherein the system stores multiple sets of parameter data, multiple alert case volumes corresponding to the parameter data, and multiple abnormal case volumes corresponding to the parameter data, wherein... For each parameter and the corresponding number of alert cases, the number of alert cases indicates the number of alert transaction cases in multiple transaction case data, and each alert transaction case is one of those transaction case data that the computer determines has an abnormal risk based on the corresponding parameter. For each parameter and the corresponding number of abnormal cases, the number of abnormal cases indicates the number of abnormal transaction case data in the transaction case data, and each abnormal transaction case is one of those transaction case data that matches the parameter and is manually determined to be abnormal; the anti-money laundering characterization parameter analysis method includes: (A) The anti-money laundering characterization parameter analysis system selects the largest one from the abnormal case quantities as a reference abnormal quantity; (B) The anti-money laundering characterization parameter analysis system calculates an abnormality judgment accuracy corresponding to the parameter data based on the proportional relationship between the abnormal case quantity corresponding to each parameter and the alert case quantity corresponding to the parameter; (C) The anti-money laundering characterization parameter analysis system calculates an abnormal case retention rate corresponding to the parameter data based on the proportional relationship between the number of abnormal cases corresponding to each parameter data and the reference abnormality amount; (D) The anti-money laundering characterization parameter analysis system performs a screening procedure on the parameter data to select N most applicable parameter data from the parameter data, and generates and outputs a parameter screening result containing the N most applicable parameter data, where N is an integer greater than or equal to 1. The anti-money laundering characterization parameter analysis system performs the screening procedure in the following ways: (D1) Using one of the two data, the abnormality judgment accuracy rate and the abnormal case retention rate, as multiple first cluster indicators, and using the other data as multiple first screening indicators; (D2) Dividing the parameter data into multiple groups according to the first cluster indicators, and selecting one parameter data from the parameter data in each group as a preliminary candidate parameter data according to the first screening indicators corresponding to the parameter data in each group; and (D3) The most suitable N parameter data are determined from these preliminary candidate parameter data.

7. The method for analyzing anti-money laundering characterization parameters as described in claim 6, wherein, In step (D3), the method by which the anti-money laundering characterization parameter analysis system determines the N most applicable parameter data from the preliminary candidate parameter data includes: (D31) using multiple first screening indicators corresponding to the preliminary candidate parameter data as multiple second grouping indicators, and using multiple first grouping indicators corresponding to the preliminary candidate parameter data as multiple second screening indicators; (D32) dividing the preliminary candidate parameter data into multiple groups according to the second grouping indicators, and selecting one preliminary candidate parameter data from the preliminary candidate parameter data of each group as an advanced candidate parameter data according to the second screening indicators corresponding to the preliminary candidate parameter data of each group; and (D33) determining the N most applicable parameter data from the one or more advanced candidate parameter data.

8. The method for analyzing anti-money laundering characterization parameters as described in claim 7, wherein, In step (D3), the method by which the anti-money laundering characterization parameter analysis system performs the screening procedure further includes, before determining the N most applicable parameter data from the one or more advanced candidate parameter data: (D34) treating the first screening indicators as multiple third grouping indicators, and treating the first grouping indicators as multiple third screening indicators; (D35) dividing the parameter data into multiple groups according to the third grouping indicators, and for the parameter data in each group, selecting one parameter data from the parameter data in that group as another preliminary candidate parameter data according to the third screening indicators corresponding to the parameter data in that group; (D36) The third screening indicators corresponding to the preliminary candidate parameter data are respectively used as a plurality of fourth grouping indicators, and the third grouping indicators corresponding to the preliminary candidate parameter data are respectively used as a plurality of fourth screening indicators; and (D37) The preliminary candidate parameter data are divided into a plurality of groups according to the fourth grouping indicators, and for the preliminary candidate parameter data of each group, the anti-money laundering characterization parameter analysis system selects one preliminary candidate parameter data from the preliminary candidate parameter data of the group as another advanced candidate parameter data according to the fourth screening indicators corresponding to the preliminary candidate parameter data of the group.

9. The method for analyzing anti-money laundering characterization parameters as described in claim 7 or 8, wherein, In step (D33), the method by which the anti-money laundering characterization parameter analysis system determines the N most applicable parameter data from one or more advanced candidate parameter data includes: determining whether the retention rate of the abnormal case corresponding to each advanced parameter data is greater than or equal to a retention rate threshold value, and when the determination result is yes, taking the advanced parameter data as one of the N most applicable parameter data.

10. The anti-money laundering characterization parameter analysis method as described in claim 6, wherein the anti-money laundering characterization parameter analysis system further stores transaction case data, each transaction case data including a transaction amount and an attribute marker, each parameter data including multiple amount threshold values ​​corresponding to a case attribute feature; the anti-money laundering characterization parameter analysis method further includes, prior to step (A): (E) the anti-money laundering characterization parameter analysis system performing a verification process on each transaction case data based on each parameter data, wherein, The method by which the anti-money laundering characterization parameter analysis system performs the verification process includes: (E1) using the attribute marker of the transaction case data as the amount threshold value corresponding to the case attribute feature that matches the attribute marker in the parameter data as a matching amount threshold value; (E2) comparing the transaction amount of the transaction case data with the matching amount threshold value; (E3) when it is determined that the transaction amount is greater than or equal to the matching amount threshold value, treating the transaction case data as a warning transaction case data corresponding to the parameter data; (F) the anti-money laundering characterization parameter analysis system counts the number of warning transaction case data corresponding to each parameter data to obtain the number of warning cases corresponding to the parameter data, and stores the number of warning cases; and (G) for each parameter data, the anti-money laundering characterization parameter analysis system treats each warning transaction case data with an abnormal annotation in the warning transaction case data corresponding to the parameter data as an abnormal transaction case data corresponding to the parameter data, counts the number of abnormal transaction case data corresponding to each parameter data to obtain the number of abnormal cases corresponding to the parameter data, and stores the number of abnormal cases.

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