A new customer signs up for a credit card using what appears to be a clean profile. The application passes basic checks. Payments are made on time for a few months, building trust. Then suddenly, the account maxes out and disappears. No clear individual to trace, no obvious red flags early on. This is the growing problem of Synthetic Identity Fraud.
Synthetic Identity Fraud occurs when a new identity is created, comprised of actual and false information. This identity is then used over time to establish accounts, build credit, and finally commit financial fraud. This type of fraud is becoming a costly problem to manageTech’s. This is where Machine Learning is entering the fray as a key component of fraud prevention.
The article explains how machine learning helps in preventing synthetic identity fraud.
Synthetic Identity Fraud : Step-by-Step Breakdown
A step-by-step guide on how synthetic identity fraud is carried out is discussed below.
Step 1: Gathering Data
The fraudsters start off their operation by gathering data. The data is comprised of both real and false information. For instance, it may comprise a real ID and a fake name and birth date.
Example: The fraudster may have a real ID number and false information such as a new name and email, thus creating a brand-new identity.
Step 2: Creating the Synthetic Identity
Using this blended data, a new identity is formed. At this stage, it often looks thin or incomplete, which may raise minor flags but not enough to stop the process entirely.
Example: The fraudster applies for a financial product, such as a low-limit credit account, using the newly created identity.
Step 3: Establishing a Financial Footprint
After the approval of the account, the fraudster begins building credibility. Small, consistent transactions are made and repaid on time to establish trust.
Example: The account is used to make small transactions, with regular payment clearing over a period of months.
Step 4: Expanding Access Across Platforms
After building credibility, the same identity is used to open multiple accounts. The identity now has a transaction history, so, approvals have become easier.
Example: The fraudster gets higher credit limits, personal loans, or even a digital wallet based on the same identity.
Step 5: Scaling the Fraud
After building a strong profile, the fraudster executes the main attack. This may include maxing out the credit limits or accumulating huge loans without the intention of paying them back.
Example: The fraudster uses multiple accounts associated with the synthetic identity concurrently to spend as much as possible within a short time.
Step 6: Disappearing Without a Trace
In the absence of the real individual, the process of recovery becomes challenging. There exists no real victim of the crime, thus the delay.
Example: The contact details are no longer accessible, and the identity disappears after the crime.
Key Machine Learning Algorithms Used in Fraud Prevention
A good fraud prevention strategy involves the use of different Machine Learning models to ensure that there is layered security.
1. Neural Networks for Complex Data Analysis
Neural networks can be applied in situations where there is complex and large data to analyze. The data may not have patterns that can easily be identified.
Example: A fintech organization is using neural networks to analyze transaction sequences and identify anomalies that may be associated with Identity Fraud.
2. Clustering Algorithm for Anomaly Detection
Clustering refers to the grouping of different types of data. It is essential as it helps identify anomalies that may not adhere to the patterns already identified.
Example: Accounts that behave differently from typical user groups such as making frequent transactions can be flagged for further investigation.
3. Support Vector Machines (SVM) for Boundary Detection
SVM assists in the distinction between normal and fraudulent behavior. It creates boundaries between the two. It performs well when the patterns of fraud are unique but intricate.
Example: An algorithm makes use of SVM as a way of classifying legitimate and suspicious login activities based on various parameters.
4. Anomaly Detection Models for Real-Time Monitoring
Anomaly detection models are specifically created to identify unusual patterns. They are essential in the prevention of fraud.
Example: An unusual surge of transactions from a newly created account prompts the system, even without prior history of fraud.
5. Gradient Boosting for Advanced Pattern Detection
Gradient boosting is based on learning from previous mistakes to improve predictions. Gradient boosting can be useful in catching evolving fraud patterns.
Example: A bank uses gradient boosting to identify synthetic identities that initially appear normal but have minor differences in behavior over time.
The Role of Transaction Data in Fraud Prevention
Leveraging transaction data with Machine Learning provides a scalable defense.
1. Powering Machine Learning Models
Machine Learning models rely on transaction data as the basis of learning patterns and detecting fraudulent transactions. These models examine the flow of transactions and identify abnormalities.
Example: Machine Learning models identify a set of transactions, where each transaction in isolation is legitimate but, as a set, points to fraud.
2. Strengthening Risk Scoring in Real Time
Transaction data allows risk scoring, where each transaction is individually scored based on multiple factors like the amount, location, frequency, and history of transactions.
Example: A transaction from a new device originating from a different country might have a higher risk score.
3. Improving Decision-Making for Approvals and Blocks
With the help of knowledge provided by transaction data, business decisions can be taken on the approval, denial, or flagging of a transaction.
Example: A transaction that is slightly out of normal behavior may not be blocked but may be subject to a one-time authentication process.
4. Connecting Transactions Across Accounts
Fraudsters often work with multiple accounts, and the data points may be hidden. With the help of transaction data, hidden connections can be found.
Example: Multiple accounts may be sending money to a destination account within a short period of time.
Conclusion
Synthetic Identity Fraud is a persistent problem for financial services, fintech, and digital services. The prevention of Synthetic Identity Frauds involves the right data, technology, and long-term strategy. Machine Learning is the solution to bring all these aspects together and ensure the building of a strong fraud prevention strategy.
Paramita Patra is a content writer and strategist with over five years of experience in crafting articles, social media, and thought leadership content. Before content, she spent five years across BFSI and marketing agencies, giving her a blend of industry knowledge and audience-centric storytelling.
When she’s not researching market trends , you’ll find her travelling or reading a good book with strong coffee. She believes the best insights often come from stepping out, whether that’s 10,000 kilometers away or between the pages of a novel.
