Abstract: A method of generating a credit/lending balance sheet may include receiving, from a financial institution, an identifier associated with a request for funds. The method may include identifying one or more financial accounts associated with a debtor who initiated the request for funds based on the identifier. The method may include accessing account data from the one or more financial accounts associated with the debtor. The method may include aggregating the account data from the one or more financial accounts. The method may include generating a balance sheet from the aggregated account data, the balance sheet providing data associated with income and expenses of the debtor.

Abstract: A payment and authentication network may include a communications interface, one or more processors, and a memory. The memory may have instructions stored thereon that, when executed by the one or more processors cause the one or more processors to receive, using the communications interface, transaction information associated with a transaction from a merchant system and generate a fraud risk score based on the transaction information. The instructions may cause the one or more processors to determine that the fraud risk score is indicative that the transaction is likely fraudulent and transmit an alert to a user device that informs a user of the user device that the transaction is likely fraudulent prior to generating an approval decision for the transaction.

Abstract: A method including collecting transactional information from a mobile application on the mobile device. The mobile device can be used by a user to initiate an activity at a risk moment. The method also can include aggregating a set of risk signals. The method additionally can include obtaining a first set of risk rules for a model specific to the activity requested by the user. Each risk rule of the first set of risk rules can define weights when the risk rule is triggered based on one or more risk signals of the set of risk signals. The method further can include executing a risk engine using the first set of risk rules for the model and using the set of risk signals to generate a risk score. The risk score can be based on the weights of triggered risk rules of the first set of risk rules. The method additionally can include generating a disposition based on a comparison of the risk score to one or more predefined thresholds scores for the model.

Abstract: Account data (e.g., balance information) for accounts at a plurality of financial institutions (or government agencies) is stored (and updated) in a central database system and accessed using a personal identifier, such as a social security number. Risk data may be generated for accounts based on the account data. The account data and risk data are accessed in response to either an account search request (e.g., from a government entity and relating to a benefits program or a subpoena) or an account verification request (e.g. from a mortgage company and relating to a mortgage application).

Abstract: Account data (e.g., balance information) for accounts at a plurality of financial institutions (or government agencies) is stored (and updated) in a central database system and accessed using a personal identifier, such as a social security number. Risk data may be generated for accounts based on the account data. The account data and risk data are accessed in response to either an account search request (e.g., from a government entity and relating to a benefits program or a subpoena) or an account verification request (e.g. from a mortgage company and relating to a mortgage application).

Abstract: A method including collecting transactional information from a mobile application on the mobile device. The mobile device can be used by a user to initiate an activity at a risk moment. The method also can include aggregating a set of risk signals. The method additionally can include obtaining a first set of risk rules for a model specific to the activity requested by the user. Each risk rule of the first set of risk rules can define weights when the risk rule is triggered based on one or more risk signals of the set of risk signals. The method further can include executing a risk engine using the first set of risk rules for the model and using the set of risk signals to generate a risk score. The risk score can be based on the weights of triggered risk rules of the first set of risk rules. The method additionally can include generating a disposition based on a comparison of the risk score to one or more predefined thresholds scores for the model.

Abstract: In order to prevent false positives at a duplicate transaction detection system, channel deposit data from financial institutions (representing the manner of presentment of checks at those institutions) are provided to a channel mapping/monitoring system. The channel mapping monitoring system calculates a prediction interval representing a normal expected range of deposits in any given channel. If deposits at a financial institution fall outside the normal expected range, the channel mapping/monitoring system provides an alert to the financial institution.

Abstract: Account information is analyzed to identify accounts that have received deposits drawn on accounts associated with money service businesses. When such deposits are detected, other transaction data is analyzed to decide whether or not to categorize the account as suspected of being used for an illicit purpose such as money laundering. The analyzed information may include the number or percentage of deposits received drawn on accounts associated with money services businesses, the pattern of deposits and withdrawals into and from the account, and other information.

Abstract: Transaction data is analyzed according to one or more transaction rules to identify accounts that are suspected of being used for activity such as money laundering. The methods may be performed on an inquiry basis for ad hoc investigation of transaction activity relating to one or more particular account owners or other entities, or may be performed on an ongoing basis for monitoring transaction activity of one or more account owners or other entities for suspicious activity.

Abstract: Check transaction data is analyzed to identify checks drawn on U.S. subsidiaries of international financial institutions. The accounts into which the identified checks are determined, and the owners of the accounts identified. The number, amounts, and frequency of such deposits may be indicative of certain types of fraud. Deposits of checks drawn on U.S. subsidiaries of international financial institutions may be detected across a plurality of accounts owned by the same owner at different depository institutions.

Abstract: Account data (e.g., balance information) for accounts at a plurality of financial institutions (or government agencies) is stored (and updated) in a central database system and accessed using a personal identifier, such as a social security number. Risk data may be generated for accounts based on the account data. The account data and risk data are accessed in response to either an account search request (e.g., from a government entity and relating to a benefits program or a subpoena) or an account verification request (e.g. from a mortgage company and relating to a mortgage application).

Abstract: Account data (e.g., balance information) for accounts at a plurality of financial institutions (or government agencies) is stored (and updated) in a central database system and accessed using a personal identifier, such as a social security number. Risk data may be generated for accounts based on the account data. The account data and risk data are accessed in response to either an account search request (e.g., from a government entity and relating to a benefits program or a subpoena) or an account verification request (e.g. from a mortgage company and relating to a mortgage application).

Abstract: In order to prevent false positives at a duplicate transaction detection system, channel deposit data from financial institutions (representing the manner of presentment of checks at those institutions) are provided to a channel mapping/monitoring system. The channel mapping monitoring system calculates a prediction interval representing a normal expected range of deposits in any given channel. If deposits at a financial institution fall outside the normal expected range, the channel mapping/monitoring system provides an alert to the financial institution.