One of the greatest challenges to fraud prevention and detection is understanding how a fraudster uses an identity and how quickly. This new report from ID:A Labs provides insight into fraud mobility by exploring the distinct patterns of fraud behavior associated with compromised identities.
This new study from Javelin explores the shift to digital account opening, and the challenges facing financial institutions as they seek to fulfill consumer expectations in this digital-centric world.
The latest study from ID:A Labs analyzes consumer behavior for three demographic groups, millennials, Generation X and baby boomers, across multiple industries, and explores the rates at which millenials are seeking and being denied credit.
This comprehensive study from ID:A Labs shows that the rate of synthetic identity fraud has increased more than 100 percent since 2010. This white paper the estimated size of the synthetic fraud issue, and possible solutions for reducing synthetic fraud.
Due to the limited data used to calculate traditional credit scores, they cannot provide a complete view of a consumer’s credit history. With the unique combination of traditional and alternative credit data, organizations can now have more complete, up-to-date access into a consumer’s risk assessment.
This paper identifies gaps in the use of traditional controls currently employed to prevent account takeover fraud, and offers an alternative to access-focused solutions.
According to a new study by ID Analytics’ ID:A Labs there are more than 10,000 identity fraud rings in the U.S. This study is the first to systematically find many thousands of identity fraud rings, by developing an algorithm capable of automating the process of examining the interconnections between identity fraudsters.
How can an institution gain detailed insight into identity risk to assess the risk of new customers? Identity attributes are an excellent complement (or alternative), to scores when an institution is looking for a third-party opinion about a prospective customer’s legitimacy or risk for fraud.
While the Social Security Administration’s 2011 decision to randomize Social Security Number (SSN) issuance made the identifier less susceptible to fraudsters, it also caused a series of unintended consequences for organizations that rely on the number’s sequencing as a key part of identity-verification and fraud defense.
Minors’ identities are appealing targets for fraudsters because their personal data is untainted, legitimate, and less likely to be monitored for misuse. To provide greater insight into the scope of child identity fraud, ID Analytics conducted a study of more than 172,523 children enrolled in ID Analytics’ Consumer Notification Service.
The Red Flag seek to protect lenders and consumers from the detrimental impact of identity fraud. Affected creditors are required to design and implement procedures for detecting and preventing identity fraud at the point of account origination as well as during management of existing accounts.
While the convenience, personalization and speed of online interactions have resulted in mainstream consumer adoption, these same interactions have created new and unique challenges for businesses. One of these challenges is the validation of identity assertions made in an online or faceless transaction.
Minors’ identities are particularly appealing targets for fraudsters because their personal data is untainted, legitimate, less likely to be monitored for misuse, and few tools are available to protect children against attack. To provide greater insight into the scope of child identity fraud, ID Analytics conducted a study of more than 172,523 children enrolled in ID Analytics’ Consumer Notification Service at some point during the 12-month period.
Direct marketing and many other consumer analytics frequently rely on name/dates-of-birth (DOB) combinations for personal identification. We will demonstrate that empirically, names/DOB combinations are woefully inadequate as a precise tool for personal identifiers; this is true for common, especially for cyclical and trendy names appended by a DOB.
Despite the absence of apparent associations between individuals seeking credit, we find that the credit application process reveals a socio-economical network of credit customers. In the complex heterogeneous data captured by ID Analytics, we observe networks of people connected by their names, dates of birth, SSNs, phones and addresses.