A bayesian approach for suspicious financial activity reporting (PDF Download Available) u s anti money laundering laws

A bayesian approach for suspicious financial activity reporting (PDF Download Available) u s anti money laundering laws

This paper presents a bayesian network (BN)-based approach to analyse customers’ transactions in a financial institution and then to detect suspicious patterns in them. The approach is developed as part of an anti-money laundering system that requires identification of suspicious transactions so that they are reported to the concerned authorities in a timely manner. The proposed BN is designed on the basis of rules suggested by the state bank of pakistan in its 2008 regulations to declare a transaction as suspicious. Using transaction history, the proposed approach assigns a baseline money laundering score to each customer. The score is an indication of the customer’s transaction behaviour. During the live operational mode, if there is a significant difference in customer’s historical transactional pattern and the current behaviour, an alert is generated which requires the branch manager (or compliance head) to verify the reason for the difference.U s anti money laundering laws

the approach has been tested on real financial transactions set having more than 8.2 million records of more than hundred thousand customers.

This paper presents a bayesian network (BN)-based approach to analyse customers’ transactions in a financial institution and then to detect suspicious patterns in them. The approach is developed as part of an anti-money laundering system that requires identification of suspicious transactions so that they are reported to the concerned authorities in a timely manner. The proposed BN is designed on the basis of rules suggested by the state bank of pakistan in its 2008 regulations to declare a transaction as suspicious. Using transaction history, the proposed approach assigns a baseline money laundering score to each customer. The score is an indication of the customer’s transaction behaviour. During the live operational mode, if there is a significant difference in customer’s historical transactional pattern and the current behaviour, an alert is generated which requires the branch manager (or compliance head) to verify the reason for the difference.U s anti money laundering laws the approach has been tested on real financial transactions set having more than 8.2 million records of more than hundred thousand customers.

This paper presents a bayesian network (BN)-based approach to analyse customers’ transactions in a financial institution and then to detect suspicious patterns in them. The approach is developed as part of an anti-money laundering system that requires identification of suspicious transactions so that they are reported to the concerned authorities in a timely manner. The proposed BN is designed on the basis of rules suggested by the state bank of pakistan in its 2008 regulations to declare a transaction as suspicious. Using transaction history, the proposed approach assigns a baseline money laundering score to each customer. The score is an indication of the customer’s transaction behaviour. During the live operational mode, if there is a significant difference in customer’s historical transactional pattern and the current behaviour, an alert is generated which requires the branch manager (or compliance head) to verify the reason for the difference.U s anti money laundering laws the approach has been tested on real financial transactions set having more than 8.2 million records of more than hundred thousand customers.

This paper presents a bayesian network (BN)-based approach to analyse customers’ transactions in a financial institution and then to detect suspicious patterns in them. The approach is developed as part of an anti-money laundering system that requires identification of suspicious transactions so that they are reported to the concerned authorities in a timely manner. The proposed BN is designed on the basis of rules suggested by the state bank of pakistan in its 2008 regulations to declare a transaction as suspicious. Using transaction history, the proposed approach assigns a baseline money laundering score to each customer. The score is an indication of the customer’s transaction behaviour. During the live operational mode, if there is a significant difference in customer’s historical transactional pattern and the current behaviour, an alert is generated which requires the branch manager (or compliance head) to verify the reason for the difference.U s anti money laundering laws the approach has been tested on real financial transactions set having more than 8.2 million records of more than hundred thousand customers.

This paper presents a bayesian network (BN)-based approach to analyse customers’ transactions in a financial institution and then to detect suspicious patterns in them. The approach is developed as part of an anti-money laundering system that requires identification of suspicious transactions so that they are reported to the concerned authorities in a timely manner. The proposed BN is designed on the basis of rules suggested by the state bank of pakistan in its 2008 regulations to declare a transaction as suspicious. Using transaction history, the proposed approach assigns a baseline money laundering score to each customer. The score is an indication of the customer’s transaction behaviour. During the live operational mode, if there is a significant difference in customer’s historical transactional pattern and the current behaviour, an alert is generated which requires the branch manager (or compliance head) to verify the reason for the difference.U s anti money laundering laws the approach has been tested on real financial transactions set having more than 8.2 million records of more than hundred thousand customers.

This paper presents a bayesian network (BN)-based approach to analyse customers’ transactions in a financial institution and then to detect suspicious patterns in them. The approach is developed as part of an anti-money laundering system that requires identification of suspicious transactions so that they are reported to the concerned authorities in a timely manner. The proposed BN is designed on the basis of rules suggested by the state bank of pakistan in its 2008 regulations to declare a transaction as suspicious. Using transaction history, the proposed approach assigns a baseline money laundering score to each customer. The score is an indication of the customer’s transaction behaviour. During the live operational mode, if there is a significant difference in customer’s historical transactional pattern and the current behaviour, an alert is generated which requires the branch manager (or compliance head) to verify the reason for the difference.U s anti money laundering laws the approach has been tested on real financial transactions set having more than 8.2 million records of more than hundred thousand customers.