Synthetic Identity Security

5 AML measures that help counter synthetic identity fraud



The world of online fraud is continually growing and becoming more sophisticated. One key type of fraud that’s becoming increasingly prevalent is synthetic identity fraud. Its complex nature combines real and fake identity elements. Rather than simply stealing an identity or creating a completely fabricated identity, synthetic identity fraud uses elements of both genuine and fake information to obtain credit or other benefits and commit fraud.

Due to its complex nature, synthetic identity fraud is challenging to detect and prevent. Many of the traditional identity verification methods are ineffective when attempting to detect a synthetic identity.

Collaboration between financial institutions, government agencies, credit bureaus, and consumers is essential to fighting synthetic identity fraud.

Anti-money laundering’s (AML) role in countering synthetic identity fraud

synthetic identity fraud Data analyst looking at screen

First, what is anti-money laundering (AML)? It’s important to understand, as financial institutions and businesses implement AML compliance to discover illegal activities that are used to launder money.

Stopping money laundering, terrorism financing, and other illegal financial activities is the core objective of AML methods.

When implementing AML, financial institutions and businesses closely monitor their customers’ transactions and flag all suspicious activity. When this suspicious activity is recognized, it’s important that the institution or business notifies the relevant authorities, so the customer or entity is thoroughly investigated. The use of AML helps ensure the credibility of the financial framework customers trust for banking and purchasing. This safeguards against fraudulent activities and upholds lawful and ethical benchmarks in financial dealings.

Anti-money laundering compliance is necessary in preventing fraud by requiring financial institutions to verify customer identities in numerous ways. AML ensures that suspicious activities are reported, encourages industry collaboration and trains employees to detect potential fraud. To maintain the integrity of our financial system it is necessary to continue to support fraud detection measures.

How does AML prevent synthetic identity fraud?

  • Implementing strict customer identity verification, using data analytics and machine learning to detect unusual patterns and anomalies in transaction behavior.
  • Utilizing watchlist screenings to identify potential fraudsters, facilitating information sharing among institutions to uncover cross-institutional fraud networks, and implementing enhanced due diligence for high-risk customers and transactions.

Using these AML methods strengthens the financial system collectively and empowers institutions and businesses to detect and reduce online fraud and money laundering.

Five Key AML Measures to Help Stop Synthetic Identity Fraud

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1. Robust Customer Identity Verification

Financial institutions reduce the risk of synthetic identity fraud by implementing a multifaceted verification process.

  • Verifying several identifying documents and information like government-issued IDs, addresses, social security numbers and biometric data
  • Institutions cross-reference and authenticate information through biometric authentication and multi-factor verification, making it challenging for fraudsters to create or manipulate synthetic identities.
  • This process improves the accuracy and reliability of customer information, strengthening the defense against synthetic identity fraud attempts.

2. Data Analytics and Machine Learning

Data analytics is critical in deterring synthetic identity fraud by analyzing vast amounts of data and then using that data to detect complex patterns and anomalies. Traditional monitoring methods do not have the capacity for a high detection level. Once these patterns are detected, the analytics then continue to identify suspicious behaviors, correlations, and deviations that point to synthetic identities and the likelihood of fraud.

Machine learning pulls from historical data and allows the model to understand how fraud tactics adapt. The model then adjusts to the new system of fraud. Then these models establish a customer behavior baseline, allowing it to detect and flag unusual activity. Things like inconsistencies in identity elements and unusual transaction sequences are possible indications of a synthetic identity in use.

Businesses and institutions are able to proactively identify and prevent synthetic identity fraud using data analytics and machine learning.

3. Watchlist Screening and Monitoring

Watchlist screening and monitoring is another procedure in AML that can be utilized to prevent synthetic fraud. Customer information is taken and referenced against global watchlists. These watchlists include individuals and organizations or entities associated with financial crimes, terrorism, and other illegal activities. An automated system continuously monitors customer activities and transactions.

Real-time monitoring allows instant reaction to suspicious activity, avoiding significant financial loss and damage.

4. Enhanced Due Diligence (EDD)

EDD is a crucial policy in stopping or deterring synthetic identity fraud. There are several ways EDD works to counter the fraudsters:

  • EDD subjects high-risk customers or transactions to a more comprehensive investigation. When a high-risk is flagged, financial institutions apply EDD protocols that involve gathering additional information about the customer, including financial history and business relationships.
  • EDD provides in-depth research and analysis allowing the institutions to validate the authenticity of the customer’s identity.
  • The EDD protocols identify potential signs of fraud and synthetic identity use.
  • EDD protocols highlight high-risk customers and transactions and apply maximum scrutiny to those cases.

5. Collaborative Information Sharing

Institutions and organizations must use data exchanges to deter and stop synthetic identity fraud.

This cooperation prevents synthetic identity fraudsters from using and abusing multiple institutions to cover their tracks.

The collaboration between financial institutions, law enforcement agencies, and other stakeholders who gather and use information about fraud patterns is critical. Creating a pooled dataset helps institutions identify cross-institutional fraud networks.

These five anti-money laundering examples represent key methods to fight against online fraudsters.

Technology-based anti-money laundering procedures in synthetic identity fraud

Facial Recognition software synthetic identity theft

The various technologies available to institutions can assist and prevent synthetic identity fraud.

  • Blockchain Technology: The nature of blockchain technology allows for enhanced identity verification by providing unalterable and enduring records of customers’ identities. The tamper-proof quality of blockchain prevents manipulation of identity information.
  • Biometric Authentication: Biometric authentication uses fingerprint scans, facial recognition, and voice recognition to verify customer identities. These biometric markers are difficult to replicate, which reduces the risk of synthetic identity creation.
  • Data Enrichment and Validation: The collaboration and pooling of data resources assist in the corroboration of customer information. Integrating external data sources ensures identity accuracy.
  • Predictive Analytics: By analyzing historical data, advanced analytics identifies patterns that indicate future fraudulent activity. Institutions can then take steps to prevent synthetic identity fraud before it happens.
  • Pattern Recognition: AML policies and procedures allow advanced algorithms to identify hidden connections and patterns within unrelated accounts and transactions. These patterns are often flags of synthetic identity accounts and networks.
  • Behavioral Analytics: Baseline profiles created by behavioral analytics through customer behavior and transaction history. When a customer deviates from their baseline profile, it flags a possible scam or fraud alert.
  • Real-time Monitoring and Alerts: Setting up real-time monitoring stops fraud before the damage occurs.

In conclusion

The battle against synthetic identity fraud requires a multifaceted approach emphasizing collaboration and cutting-edge technology. Fraudsters are continually adapting to detection methods and creating new types of fraud. To counteract this, a united effort between financial institutions, government agencies, credit bureaus and consumers is required.

Five critical anti-money laundering measures that provide a comprehensive framework to counter synthetic identity fraud are:

  • Robust customer identity verification
  • Data analytics and machine learning
  • Watchlist screening and monitoring
  • Enhanced due diligence
  • Collaborative information sharing

To strengthen the effectiveness of fraud prevention efforts it is necessary to integrate technology-based measures and procedures. Blockchain is reliable for unchangeable identity records, which allows institutions to trust the identities of their customers. Biometric authentication provides dependable verification that cannot be manipulated. Predictive analytics, pattern recognition and behavioral analytics all contribute to identifying the use of synthetic identity accounts and unusual behaviors. These technologies help combat the evolving methods of fraudsters.

While implementing stringent AML is necessary, balancing security with a seamless customer experience is also crucial. User-friendly technologies and clear communication about AML policies, procedures and regulations provide a streamlined process to the customer. This process does not unnecessarily burden legitimate customers while also providing stringent verification for the financial institution.

For more information on safeguarding your business against synthetic identity fraud, contact our team today.

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