Fraud detection and analysis
Systems and methods comprise a platform including a processor coupled to a database. Risk engines are coupled to the platform and receive event data and risk data from data sources. The event data comprises data of actions taken in a target account during electronic access of the account, and the risk data comprises data of actions taken in a accounts different from the target account. The risk engines, using the event data and the risk data, dynamically generate an account model that corresponds to the target account, and use the account model to generate a risk score. The risk score represents a relative likelihood an action taken in the target account is fraud. A risk application coupled to the platform includes an analytical user interface that displays for the actions in the target account at least one of the risk score and event data of any event in the account.
1. A system comprising:
a platform comprising a processor coupled to at least one database;
a plurality of risk engines coupled to the platform, the plurality of risk engines receiving event data and risk data from a plurality of data sources that includes at least one financial application, wherein the event data comprises actual observable parameters corresponding to actions taken in a target account by an owner of the target account during electronic access of the target account during an online session on a web browser, wherein the risk data comprises actual observable parameters corresponding to actions taken in a plurality of accounts different from the target account by at least one user other than the owner of the target account, wherein the plurality of risk engines uses the event data and the risk data to dynamically generate an account model that corresponds to the target account and comprises probabilistic relationships between the event data and the risk data, wherein the plurality of risk engines dynamically update the account model using event data of a future event occurring during the online session or during a future online session in the target account, wherein the plurality of risk engines generate a prediction during the future event with use of the account model, wherein said generated prediction predicts whether the owner of the target account is perpetuating the future event during the online session or during a future online session; and
a risk application coupled to the platform and comprising an analytical user interface that displays for the actions in the target account at least one of the generated prediction and the event data of any event in the target account,
wherein the actual observable parameters comprise at least one of: an operating system, a browser type, an IP address, HTTP data, and page views relating to an online session.
2. The system of claim 1 , wherein the analytical user interface displays a plurality of columns representing at least one event conducted in the account and at least one risk row representing risk of the at least one event based on the generated prediction such that an intersection region defined by an intersection of the risk row with at least one of the plurality of columns corresponds to a risk score of the at least one event corresponding to the column.
3. The system of claim 2 , wherein the intersection region comprises color coding relating the risk score to at least one event.
4. A method comprising:
receiving at a plurality of risk engines event data and risk data from a plurality of data sources that includes at least one financial application, wherein the event data comprises actual observable parameters corresponding to actions taken in a target account by an owner of the target account during electronic access of the target account during an online session on a web browser, wherein the risk data comprises actual observable parameters corresponding to actions taken in a plurality of accounts different from the target account by at least one user other than the owner of the target account;
dynamically generating by the plurality of risk engines an account model that corresponds to the target account, wherein the generating comprises generating probabilistic relationships between the event data and the risk data;
dynamically updating by the plurality of risk engines the account model using event data of a future event occurring during the online session or during a future online session in the target account;
generating a prediction by the plurality of risk engines during the future event with the account model, wherein the generated prediction predicts whether the owner of the target account is perpetuating the future event during the online session or during a future online session; and
presenting an analytical user interface, by a risk application coupled to a platform comprising a processor coupled to at least one database, that displays for the actions in the target account at least one of the generated prediction and the event data of any event in the target account,
wherein the actual observable parameters comprise at least one of: an operating system, a browser type, an IP address, HTTP data, and page views relating to an online session.
5. The method of claim 4 , wherein the analytical user interface displays a plurality of columns representing at least one event conducted in the target account and at least one risk row representing risk of the at least one event based on the generated prediction such that an intersection region defined by an intersection of the risk row with at least one of the plurality of columns corresponds to a risk score of the at least one event corresponding to the column.
6. The method of claim 5 , wherein the intersection region comprises color coding relating the risk score to at least one event.