How Machine Learning Reduces the Scope for Identity Fraud in Payments

Constantly banks implement new technologies to provide more efficient services to their customers. However, despite these technological advancements, cases of fraudulent payments continue to rise. According to data from the Federal Trade Commission (FTC), more than 2.8 million consumer fraud reports in 2021, a 70% increase from 2020. 

Based on this data, fraudsters become bolder and targeting bank security systems through hacks and social engineering. One of the most common fraud tactics includes identity theft, which compromises customers' bank accounts and jeopardizes the security of digitalized payments.

Technologies such as machine learning became  quite critical for banks in overcoming the challenges of identity fraud in the systems. The deployment of machine learning models helps banks modernize their core systems and reduce the occurrence of financial crimes.  

But how exactly will banks use machine learning to reduce the scope of identity fraud in payments? Here's all you need to know. 


What is Machine Learning?

Machine learning makes predictions with limited human intervention. It stands as subset of artificial intelligence technology that uses algorithms and data to build systems. So, with the market size expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8%, machine learning remains high demand,

In banking ecosystems, machine learning generates actionable insights from bank databases. This technology collects data from transaction histories, documentation, and chat logs with bank employees to understand customer behaviors.


What is Identity Fraud? 

Identity fraud or theft occurs when someone steals your personal credentials, such as identification number, social security number, bank account number, or credit card information, and uses them to commit fraud. Criminals use financial identity theft, child identity theft, and synthetic identity theft to defraud financial institutions. 

Victims of identity theft will notice inconsistencies like unexplained bank withdrawals and charges, errors in the credit report, and receive letters from strange credit card providers.


How Does Machine Learning Reduce Identity Fraud? 

Fraud Detection

Statistics by PWC's Global Economic Crime and Fraud Survey 2022 show that 46% of organizations have been victims of corruption and other economic crimes over the past two years. Continually, Fraudsters design new tactics to bypass the banking systems. However, with machine learning, banks will eliminate system vulnerabilities in real-time.

Thanks to the enormous data sets that banks possess,  they quickly identify anomalies. For instance, they detect fraudulent customer transactions by using hidden relationships between their data.

Identifying Patterns

Machine learning helps banks gain a deeper understanding of customer behaviors. For instance, if the bank integrated machine learning-based budgeting tools in its mobile apps, it will use the customer transaction history algorithm to identify spending habits and personalize offers. 

Banks can use this same data to identify fraudulent activities and changes in customer spending habits. Machine learning will spot minor inconsistencies in customer identities to determine if they are stolen or real, and suspicious patterns could indicate identity theft.

Building a Suspicious Activity Monitoring System

Banks like Capital One use machine learning for suspicious activity monitoring. Implementing these models provides a wide range of data to fight money laundering. Therefore, banks use these algorithms to monitor suspicious profiles and flag abnormal activities. 

Performing Authentication Tests

Through machine learning, banks scan crucial identity documents in real time. This technology helps with check verification services to support processing payments faster. Therefore, the financial institution verifies and knows quickly when a fraudster tries to deposit a check or make withdrawals using a fake identity. 


Other Types of Fraud Detection and Prevention Measures 

  • Adopt Newer, Faster Payment Systems: Most financial institutions adopt the latest and faster payment systems that use machine learning and artificial intelligence to verify payments. These systems complete background checks and payment verification in real-time to prevent fraud.
  • Use Check Verification Services: Processing electronic checks in the ACH network requires check verification services. This service lets banks quickly verify the check status before accepting it.
  • Incorporate Biometric Authentication Technologies: ISVs provide banks with software that relies on biometric authentication to authorize payments, sign documents, or log in to an online payment terminal. When your bank implements digital payment applications, biometric technology makes hacking harder.
  • Rely on Advanced Fraud Detection Features: Some criminals can circumvent the primary credit card fraud prevention tactics and provide the correct card verification value (CVV) and address. To avoid this, your bank needs advanced features like a BIN filter, velocity filter, on-hold functionality, negative database, and built-in analytics processes. 


How Can Banks Get Started with Fraud Detection and Prevention?

  • Work with Cyber Security Professionals: By partnering with cyber security experts, top advanced corporate practitioners will identify the security loopholes in your bank and implement fraud prevention measures in your entire payments system.
  • Develop a Fraud Prevention Strategy: Once you have identified the areas that need more focus, you need to develop a security strategy for the bank to prevent further identity theft cases. 
  • Train Employees: You need your staff to be well-prepared to handle security issues and prevent fraudulent activities. Brief them on the best practices, such as websites to avoid and safe password management to prevent cyber-attacks on the bank. 
  • Limit Information Access: To protect sensitive customer data, limit access to specific individuals and teams. 
  • Choose the Best Partners: Banks must partner with reputable and reliable vendors with the latest technologies to protect sensitive financial information. This tactic ensures that third-party individuals won't interfere with your financial data. 

Become a Partner


The iCG Way of Fraud Detection and Prevention

Banks encounter different security challenges because of rising cases of identity theft. However, they can overcome this by adopting secure payment technologies from iCG to help recheck their payment systems for anomalies. A payment processor like iCG helps in fraud detection and prevention by providing these features:

  • Data Tokenization: Tokenization is important in financial operations because it converts sensitive customer information to random letters and numbers. This way, the bank will prevent the misuse of customer payment details.
  • Check Verification: iCG-Verify provides the check status and eliminates the potential for fraudulent activities. That protects the bank from accepting bad checks. 
  • PCI Compliance: iCG uses the latest payment technology to reduce your PCI scope. The built-in features mitigate risks and boost compliance within one platform. 


How Can iCG Help You Get Started?

Security is a critical component in your bank's payment system. You can reduce the scope for identity theft by using the latest technologies that incorporate machine learning. Tools from iCG have in-built security features to help with fraud detection and prevention. 

As iCG, we are joining the Nacha Preferred Partner Program as a representative for Risk and Compliance. We provide anti-fraud measures to ensure sensitive customer information is secure. If you want to get technologies like tokenization and reduce identity fraud, partner with iCG, a reliable PCI-compliant payment processor

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