Synthetic identity Fraud is among the most difficult to identify and stop, and it is now on track to defraud financial and commercial institutions of about $5 billion by 2024. 5.3% of all digital transactions worldwide resulted in losses in 2022, an increase of 132% over the previous year.

Sontiq, a company under the ownership of TransUnion, conducted a comparative analysis of the magnitude and gravity of data breaches in 2022 to those of previous years. Per a statement released by TransUnion, the breaches above have significantly contributed to the proliferation of identity engineering. TransUnion further anticipates that the emergence of synthetic identities will reach an unparalleled scale by 2022. According to TransUnion, the United States witnessed a record high in outstanding balances associated with synthetic identities for various types of loans, including auto, credit card, retail credit card, and personal loans. The outstanding balances amounted to $1.3 billion in Q4 2022 and $4.6 billion for the entire year of 2022.

Any fraud severely damages customers’ faith in businesses and their willingness to use their services. The main issue is that 10% of people who use credit and debit cards have been victims of fraud in the past year.

Identifying fraudulent use of synthetic identities is a challenge with data.

To fabricate or build synthetic identities, attackers collect every piece of personally identifiable information (PII) they can get their hands on, beginning with social security numbers, birth dates, residences, and work histories. After then, they use them to apply for new accounts, which many currently available fraud detection models consider accurate.

Focusing on identities with common first and last names is a popular tactic that can make identifying those trying to commit cybercrime challenging because it makes the perpetrators less noticeable. The objective is to develop fabricated identities that can fit in with the general population. Attackers frequently rely on several iterations when attempting to create synthetic identities that are as inconspicuous and invisible as possible. To further trick detection systems, ages, places, residences, and several other demographic characteristics are mixed.

To determine synthetic identities, McKinsey used a process that involved multiple steps. The organization used a database of consumer marketing information in conjunction with nine other outside sources of information to collect 15,000 customer profiles. The research team then determined 150 characteristics that functioned as depth and consistency measures for a profile that could be applied to all 15,000 individuals. The research team found these characteristics. After that, an overall rating for each ID was determined based on its depth and consistency. The lower the score, the likelihood that the ID is fraudulent.

According to research conducted by LexisNexis Risk Solutions, fraud discovery techniques are missing 85–95 percent of potential synthetic identities. Most fraud detection models do not provide real-time insights or support for a vast foundation of telemetry data accumulated over several years of transactional activity. The model outputs are inaccurate since there are insufficient transaction records or real-time views.

CISOs have expressed to VentureBeat their desire for improved fraud prevention modeling applications and more straightforward solutions than those now available.

Five ways that AI is contributing to the fight against fraudulent synthetic identities

The problem that every fraud system and platform vendor faces in the fight against Synthetic Identity Fraud is to balance providing sufficient authentication to catch an attempt and alienating customers using their identities legitimately. The objective is to lower the number of false positives so that threat analysts at a company or brand are not overworked while at the same time employing Machine Learning (ML)-based algorithms that are capable of continually “learning” from each attempt at fraud. It is an ideal application for machine learning and generative AI, which can learn from the real-time data sets that a company collects on fraudulent activity.

This project aims to train supervised machine learning algorithms to discover anomalies not recognized by existing fraud detection methods and to enhance these approaches with unsupervised machine learning to find new patterns. The most cutting-edge AI solutions in this market mix supervised and unsupervised machine learning.

Aura, Experian, Ikata, Identity Guard, Kount, LifeLock, IdentityForce, and IdentityIQ are among the many leading fraud systems and platforms vendors that can detect and prevent synthetic identity fraud. Other vendors include IdentityIQ. It is noteworthy that among the various providers, Telesign’s risk assessment model stands out because ruptured and unstructured machine learning test assessment scores in milliseconds and verify whether a new account is accurate.

Listed below are five ways that AI is assisting in detecting and preventing escalating instances of identity fraud.

Integrating Machine Learning into the Primary Source Code

A machine learning (ML)-based platform that is continually learning and sharing the latest insights it finds in all transaction data is required to stop synthetic identity fraud across all stores and retail locations. The objective is to develop an ecosystem for preventing fraud that can continuously increase the amount of knowledge it has derived.

Splunk’s method of developing a fraud risk scoring model demonstrates the benefits of data pipelines that provide dashboarding and investigative capabilities in addition to making data indexing, transformation, machine learning model training, and application of machine learning models. According to Splunk, businesses that use proactive data analysis techniques suffer frauds up to 54% less costly and 50% shorter in duration than businesses that do not monitor and analyze data for fraud signals.

Using cloud services to cut down on the amount of time needed to uncover ongoing instances of synthetic fraud

Existing fraud prevention systems suffer from one of their primary weaknesses, which is a comparatively longer latency compared to modern cloud services. Along with Amazon Cognito, many banking, e-commerce, and financial services firms employ Amazon Fraud Detector. This tool helps adapt unique authentication workflows to spot synthetic fraud activity and efforts to deceive a business or a consumer.

According to statements made to VentureBeat by CIOs and CISOs, using an excessive number of tools that cannot work together efficiently hinders their capacity to recognize and respond to fraud warnings. When there are excessive tools, the time available to fraud analysts is spread too thinly among several dashboards and reports. A more integrated technology stack that can deliver machine learning-based efficacy at scale is required to increase fraud detection. To increase the accuracy of risk scoring and identify synthetic identity fraud before it results in a loss, it is necessary to have decades’ worth of transaction data paired with real-time telemetry data.

According to Jim Cunha, who holds the position of Senior Vice President at the Federal Reserve Bank of Boston and is also a leader in secure payments strategy, organizations can increase their likelihood of detecting synthetic identities by implementing a multi-layered approach to fraud prevention that combines both manual and technological data analysis. The methodology integrates both manual and technological techniques for data analysis. Moreover, organizations can learn about evolving fraud tactics by exchanging information within their internal network and with other entities operating in the payments industry.

Onboarding friction and false positives are both reduced thanks to ML-based risk assessments.

It is the responsibility of fraud analysts to determine how high decline rates should be established to avoid fraud while allowing legitimate new consumers to sign up. Fraud analysts employ machine learning-based scoring algorithms, which integrate supervised and unsupervised learning, rather than going through the traditional process of trial and error. Using AI-based fraud scores helps reduce false positives, a vital source of friction between businesses and their customers. This reduces the number of manual escalations and declines, which in turn enhances the experience for the consumer.

Methods that are successful for real-time, identity-based activity anomaly detection include predictive analytics, modeling, and algorithmic methods.

The ML models’ fraud assessments become more accurate as more data is added. Real-time risk rating helps protect individuals from falling victim to identity theft. Using supervised and unsupervised machine learning, it would be best to look for fraud detection technologies that produce trust scores. The most cutting-edge technologies for preventing fraud and verifying identification can construct convolutional neural networks dynamically and “learn” from machine learning data patterns in real time.

Machine learning helps maintain a healthy friction-to-user-experience balance.

The chief executive officer of Telesign, Joe Burton, was quoted in VentureBeat as saying, “Customers don’t mind friction if they understand that it’s there to keep them safe.”

According to Burton, ML is a valuable technique that can reduce the friction a user encounters while optimizing the user experience. Customers can receive peace of mind from friction by learning that a company or brand has an extensive understanding of cybersecurity and, most importantly, the importance of protecting customer data and privacy.

It is also essential for threat analysts, who monitor fraud protection platforms regularly, to spot newly emerging risks and take appropriate action against them to find the optimal balance between friction and experience. The difficult task that fraud analysts face is determining whether an alert or reported anomaly results from a fraudulent transaction started by an identity that does not exist or whether it results from a legitimate customer trying to acquire a product or service.

Implementing machine learning provides analysts with more efficient workflows and insights and better accuracy and real-time latency, which helps them prevent probable fraud before it happens.