Synthetic Frauds

Synthetic fraud is a type of identity theft in which criminals create fake identities using a mix of real and fake information. They may use stolen Social Security numbers, dates of birth, and addresses to create new credit accounts, open bank accounts, and even apply for loans.

Synthetic Fraud fuelled by “Frankenstein Identities” is the hardest to detect and quickly accounting for the highest percentage of fraud losses, primarily involving credit cards and unsecured lending portfolios.

Synthetic Fraud is quite different from the traditional identity theft, and cyber scammers create “Frankenstein Identities”. Instead of stealing a real identity, scammers create a fake identity using a real and/or unused social security number, combine the social security number with a fictitious name, driver’s license and physical address to form the “Frankenstein Identity”.

Synthetic fraud is increasing rapidly, and it is now the fastest-growing financial crime in the United States. According to a report by LexisNexis Risk Solutions, synthetic fraud losses are expected to reach $30 billion by 2025.

This type of fraud is increasingly common and largely targets older adults, who lost $588 million to tech support scams in 2022, according to the Federal Bureau of Investigation. Criminals persuade victims they have a serious computer issue such as a virus, then masquerade as computer technicians from well-known companies as a cover for theft. Often, they persuade victims to wire funds to fraudulent accounts.

As per the report, Americans, 60 and older lost $3.1 billion to cyber fraud in 2022, an 84% increase from 2021, according to the FBI. Losses have jumped ninefold in just five years, from $342 million in 2017, FBI data shows. Because fraud statistics are based only on reported incidents, its true scope may be far greater.

Emerging technologies like Artificial Intelligence and Machine Learning, combined with big data analytics and high-performance computing applications can process larger volumes of data from various sources and data sets (Structured, Unstructured and Semi-structured).

There are technologies like, AI/ML one can achieve higher scalability by processing large scale data types containing media data, image data and object data to spot trends and patterns across disparate data sets. Financial Institutions can predict and capture “Synthetic Fraud” scammers early in the process by applying accelerated deep learning and statistical machine learning technologies.

S Mohini Ratna, Editor, VARINDIA