Financial fraud is a multifaceted challenge that requires both vigilance and data-driven intelligence. At FiVerity, our latest research reveals intriguing connections between confirmed fraud cases across multiple financial institutions. This post aims to help financial fraud analysts unpack the nuances of fraud types, detection methods, and the power of data-driven insights for effective prevention.
What is Synthetic Identity Fraud?
Synthetic Identity Fraud (SIF) involves criminals mixing real and fabricated information to create a new identity, often blending real Social Security Numbers with fake names or addresses.
One of the most concerning aspects of SIF is its potential to facilitate a wide range of fraud types, including account takeovers, scams, loan defaults, credit card chargebacks, and more. By meticulously blending authentic elements like Social Security Numbers with fictitious names, birthdates, or addresses, criminals can open fraudulent accounts, secure loans, make unauthorized purchases, or engage in identity theft scams, all while avoiding detection for extended periods. This adaptability and ability to operate across multiple forms of fraud make SIF a significant challenge for fraud detection and prevention efforts across various industries.
What is Identity Theft?
Contrastingly, Identity Theft is the illegal use of another person's personal information, impacting the victim immediately through unauthorized transactions or legal ramifications.
Key Differences
Understanding the key differences between SIF and Identity Theft is vital for developing targeted prevention methods.
Origin of Information
Detection Difficulty
What is Partial Fraud Matching?
In the realm of fraud detection, a partial match refers to instances where only a subset of a person's Personally Identifiable Information (PII) correlates with known fraudulent activities.
What Constitutes Full Fraud Matching?
Contrastingly, a full match occurs when Name-DOB-SSN aligns with details found in recognized fraud cases.
Key Points to Consider
The capability to pinpoint both partial and full matches within your portfolio—or even across a broader network—does more than just augment fraud detection rates. It also facilitates accurate labeling of suspicious accounts, thereby enhancing future detection and prevention mechanisms. This, coupled with secure information sharing, is revolutionizing the way financial institutions operate in the realm of fraud detection. By securely sharing pertinent data and insights, FIs can greatly improve their fraud detection capabilities and confidence, streamline their investigations process, and ultimately, speed up response times to potential threats, by having a more complete view of an account's history.
The Reuse of PII in Fraud
Our Venn diagrams reveal overlaps in PII elements and combinations, shedding light on more complex fraudulent activities.
The Elements That are Reused Most Frequently
Email addresses, names, and physical addresses are the most frequently reused elements, while more difficult to change elements such as SSN and phone numbers tend to be a shared element across multiple fraudulent accounts.
The Use of Attribute Changing to Avoid Detection
This tactic appears to be especially prevalent among sophisticated players who are well-aware of the surveillance they are under.
Simply put, attribute changing involves altering one or more elements of Personally Identifiable Information (PII) to sidestep fraud detection algorithms, either during an application process, or between multiple institutions, to maximize the use of the stolen identification elements they’ve obtained. This could mean changing email addresses, tweaking physical addresses, modifying names slightly, or using nicknames like Bob vs. Robert. This maneuver allows fraudsters to blend in with legitimate transactions and activities while carrying out their fraudulent operations at a large scale.
Why is Attribute Changing a Concern?
Potential Mitigation Strategies
Source: researchgate.net
Source: aws.amazon.com
Manual Identification: Spotting the Unusual in the Usual
Frequent changes in PII elements that deviate from typical user behavior often warrant closer scrutiny for fraud detection.
Manual Matching: The Art of Detail-Oriented Scrutiny
Automation Across the Network, for Free: Scale with FiVerity
Whether you're a DIY fraud analyst or looking to leverage automated systems, understanding the nuances of financial fraud and employing tailored strategies can enhance your fraud prevention efforts significantly. With FiVerity's platform, you can scale these tactics across a vast network, making your institution more secure and staying one step ahead of the fraudsters.