Abstract: In various embodiments, systems, methods, and techniques are disclosed for analyzing various entity data items including users, computing devices, and IP addresses, to detect malfeasance. The data and/or database items may be automatically analyzed to detect malfeasance, such as criminal activity to disguise the origins of illegal activities. Various money laundering indicators or rules may be applied to the entity data items to determine a likelihood that money laundering is occurring. Further, the system may determine one or more scores (and/or metascores) for each entity data item that may be indicative of a likelihood that it is involved in money laundering. Scores/metascores may be determined based on, for example, various money laundering scoring criteria and/or strategies. Account entities may be ranked based on their associated scores/metascores. Various embodiments may enable an analyst to discover various insights related to money laundering.

Abstract: In various embodiments, systems, methods, and techniques are disclosed for analyzing various entity data items including users, computing devices, and IP addresses, to detect malfeasance. The data and/or database items may be automatically analyzed to detect malfeasance, such as criminal activity to disguise the origins of illegal activities. Various money laundering indicators or rules may be applied to the entity data items to determine a likelihood that money laundering is occurring. Further, the system may determine one or more scores (and/or metascores) for each entity data item that may be indicative of a likelihood that it is involved in money laundering. Scores/metascores may be determined based on, for example, various money laundering scoring criteria and/or strategies. Account entities may be ranked based on their associated scores/metascores. Various embodiments may enable an analyst to discover various insights related to money laundering.

Abstract: In various embodiments, systems, methods, and techniques are disclosed for analyzing various entity data items including users, computing devices, and IP addresses, to detect malfeasance. The data and/or database items may be automatically analyzed to detect malfeasance, such as criminal activity to disguise the origins of illegal activities. Various money laundering indicators or rules may be applied to the entity data items to determine a likelihood that money laundering is occurring. Further, the system may determine one or more scores (and/or metascores) for each entity data item that may be indicative of a likelihood that it is involved in money laundering. Scores/metascores may be determined based on, for example, various money laundering scoring criteria and/or strategies. Account entities may be ranked based on their associated scores/metascores. Various embodiments may enable an analyst to discover various insights related to money laundering.