Abstract: Methods and systems of the present disclosure include identifying and addressing potential healthcare-based fraud, according to one embodiment. The methods and systems identify potential healthcare-based fraud associated with potentially suspicious healthcare providers, patients, and/or claim submissions, in one embodiment. According to one embodiment, the methods and systems acquire data associated with a healthcare provider, patient, and/or claim submission; apply the data to one or more predictive models to generate one or more risk scores to identify potential healthcare-based fraud, and perform one or more risk reduction actions based on the one or more risk scores, according to one embodiment.

Abstract: Methods and systems of the present disclosure include identifying and addressing potential fictitious business entity-based fraud, according to one embodiment. The methods and systems identify fictitious business entities associated with fraudulent tax return filings, in one embodiment. According to one embodiment, the methods and systems acquire data associated with an employer identification number (EIN), apply the data to one or more predictive models to generate one or more risk scores to identify potentially suspicious EIN data, and perform one or more risk reduction actions based on the one or more risk scores, according to one embodiment.

Abstract: Account takeover and stolen identity refund fraud are types of Internet-centric crime (i.e., cybercrime) that include the unauthorized access or use of a user account or identity information to file a tax return in order to obtain a tax refund and/or tax credit from, for example, a state or federal revenue service. Because fraudsters access legitimate user accounts or use legitimate identity information to create user accounts, it can be difficult to detect fraudulent activity in user accounts. Methods and systems of the present disclosure facilitate the identification and prevention of potential fraudulent activity in a financial system, according to one embodiment. The methods and systems automate fraud claim receipt, predictive model training, risk score threshold improvement/optimization, and/or investigation of potentially affected user accounts, according to one embodiment.

Abstract: Account takeover is one of a number of types of Internet-centric crime (i.e., cybercrime) that includes the unauthorized access/use of a user's account with the user's identity or credentials (e.g., username and/or password). Because fraudsters acquire user credentials through phishing, spyware, or malware scams, it can be difficult to detect unauthorized access of a user's account. Methods and systems of the present disclosure identify and address potential account takeover activity, according to one embodiment. The methods and systems acquire system access data, apply the system access data to one or more predictive models to generate one or more risk scores, and perform one or more risk reduction actions based on the one or more risk scores, according to one embodiment. The financial system is a tax return preparation system according to one embodiment.

Abstract: A method and system gathers user tax data for a user, from one or more sources of tax information, to prepare the user's tax return within a tax return preparation system, in one embodiment. The method and system populate a database with relationships between existing user metadata and one or more sources of tax information, in one embodiment. The method and system analyze new user metadata for the user to identify which of the one or more sources of tax information are relevant to the user, in one embodiment. The method and system retrieve new user tax data from the identified ones of the one or more sources of tax information that are relevant to the new user metadata of the user, in one embodiment. The method and system populate the user's tax return with the new user data, within the tax return preparation system, in one embodiment.

Abstract: A method and system evaluates analytics modules to improve a personalization of tax questions delivered to a user in a tax return preparation system, according to one embodiment. The method and system retrieves historical tax return data and selects one or more interchangeable analytics modules for evaluation with the historical tax return data, according to one embodiment. The method and system applies the historical tax return data to the one or more analytics modules that are selected for evaluation, according to one embodiment. The method and system receives analytics outputs from the one or more analytics modules, in response to applying the historical tax return data, according to one embodiment. The method and system determines an effectiveness of each of the one or more analytics modules by correlating the analytics outputs with at least part of the historical tax return data, according to one embodiment.

Abstract: A method and system selects one or more interchangeable analytics modules for use in a tax return preparation system to provide a customized electronic tax return preparation interview to a user, according to one embodiment. The method and system receive user data associated with a user, according to one embodiment. The method and system apply one of a number of selection techniques to determine which of one or more analytics modules to use within the tax return preparation system, according to one embodiment. The method and system apply the one or more analytics modules to the user data to determine the relevance of tax return preparation interview questions to the user, according to one embodiment. The method and system deliver tax return preparation interview questions to the user, based on the determined relevance of the number of tax return preparation interview questions to the user, according to one embodiment.

Abstract: A method and system for providing a tax return preparation system with interchangeable analytics modules includes providing one or more interchangeable analytics modules. Each of the interchangeable analytics modules includes one or more analytics algorithms used to select user experience elements to be included in a tax return preparation interview process presented to a user through one or more tax return preparation systems. The one or more interchangeable analytics modules are distinct and independent analytical components provided to the tax return preparation system that can be interchanged, overwritten, and interfaced with individually, and without otherwise changing and/or modifying the tax return preparation system. Consequently, a tax return preparation system can provide a tax return preparation interview process capable of dynamically evolving to meet the specific needs of a given user.