Synthetic Identify Fraud (SIF) is defined by the Federal Reserve as “the use of a combination of personally identifiable information (PII) to fabricate a person or entity in order to commit a dishonest act for personal or financial gain.”
Two words in the Fed’s definition of SIF – “combination” and “fabricate” – are the key to understanding why banks and credit unions need a combined machine learning (ML) and data science solution to combat SIF.
ML and data science have three advantages relative to how bad actors use SIF: intelligent automation, time savings, and compound benefits derived from leveraging IT and banking systems.
Intelligent Automation involves continuous learning and rapid iterations facilitated by machine learning algorithms which result in unlimited variation, escalated reproduction, and early detection of new fraud patterns.
A fraudster will hatch a SIF scheme, for example, that consists of a set of synthetic identities. These fraudulent identities are derived from compromised personal data acquired on the dark web and combined in hundreds – sometimes even thousands – of variations. The SIF identities evolve at a rapid pace and are designed to defeat conventional rules-based systems, lightweight machine learning countermeasures and a range of security solutions. The only way to consistently and effectively combat SIF fraud schemes is by bringing a “gun to a knife fight” by using a combination of ML and data science systems that complement anti-fraud solutions.
Time Savings is a great advantage for machine learning end users. Finding the fraud faster results from the speed of application deployment, synthesis of complex information, transformations into actionable insights, efficient sifting of immense volume of applications, “human in the loop” discoveries, and other capabilities.
The time savings advantage can only be realized by combining the analysis of identities, fraud schemes, and other patterns using data science; then innovating to identify reusable patterns that successfully apprehend fraud, operationalize fraud remediation, and scale prevention initiatives. This advantage significantly reduces the dark-cloud hanging over new accounts, loans, and other services that have been originated by bad actors.
Compound Benefits are derived from combining machine learning with data science, then leveraging IT and banking systems to create an overall solution that fraudsters simply can’t compete with. This starts with a support for data sources (such as customer and credit profiles), banking systems (such as KYC solutions), and APIs which support banking operations, anti-fraud and security systems and solutions. The ultimate belt-and-suspenders solution.
By using the FiVerity Cyber Fraud Defense platform, customers derive competitive advantage against cyber fraudsters through an intuitive interface, graphical data displays, identity analysis, statistical and ROI analysis, as well as other features. These powerful tools provide a fraud or risk analyst, security officer, business decision maker and other users with a competitive advantage in the battle against cyber fraud.
Watch for our fraud profile collaboration network, which – when combined with our SynthID® Detect prediction algorithms – creates the ideal solution to your cyber fraud challenges.