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Don't let fraudsters slip through the holes in your data and identity resolution

Author: Lindy Porter


As a fraud professional, you’re constantly battling against fraudsters who want to exploit weaknesses in your organization's security and steal valuable information. One of the most critical areas of focus is data and identity resolution, as it serves as the foundation for fraud prevention efforts.

But, despite the best efforts of organizations to secure their data, fraudsters are constantly finding new ways to steal data and identities, making it difficult for companies to keep up. That's why it's important to have a solid data and identity resolution strategy in place to prevent fraudsters from slipping through the cracks.

Data and identity resolution refers to the process of matching and merging customer data across multiple systems to create a single, accurate view of each customer. This process is crucial for preventing fraud because it allows companies to quickly identify suspicious activity and take action before it's too late.

Unfortunately, many companies still struggle with data and identity resolution. They may have multiple systems with conflicting data, outdated information, or incomplete records. And in most cases, those systems are black box – creating gaps of their own. These gaps in data can make it difficult to identify fraudulent activity, allowing fraudsters to slip through the cracks.

What are black box fraud solutions?

Black box fraud solutions refer to algorithms or software that rely on complex data analysis techniques to identify fraudulent activities. While these solutions can be effective in detecting and preventing fraud, they also create gaps in data and identity resolution because they often rely on proprietary models that are hidden from users. This means you can’t see how these solutions arrive at their decisions and what data points they’re using to make those decisions. All you get is a score, or recommendation. This lack of transparency leads to blind spots in fraud detection and identity resolution, potentially allowing fraudsters to go undetected. Black box solutions also struggle to keep up with the evolving tactics of fraudsters, who are constantly developing new methods to avoid detection. It’s crucial to combine black box solutions with other approaches, such as machine learning, transparent algorithms, and behavioral biometrics to ensure comprehensive and effective identity resolution and fraud prevention.

Traditional fraud solutions are also third-party, meaning the data is sent outside the organization, analyzed, and sent back. This requires major encryption and security and causes significant delays in data sharing – causing data gaps. The time to catch a fraudster is while they’re committing the fraud, not hours or days later.   

How to identify and plug the holes in your identity resolution

As fraud cases become increasingly complex, staying one step ahead of those trying to exploit identity resolution systems is essential for fraud professionals. To achieve this, you need data-driven solutions which are comprehensive enough to identify gaps and weaknesses in existing identity resolution and fraud detection processes.

Data integrity is the cornerstone of any fraud prevention strategy. To maximize the effectiveness of your identity resolution efforts, it's critical to consider how your data is collected, stored, and shared with other systems. Fraudsters constantly look for weak spots in your data infrastructure that allow them to slip through undetected. To stay ahead of them, you need to ensure all relevant data points are included in your analysis and that accurate identity resolution is taking place in real-time.

The first step is making sure you have access to the full picture: from anonymous web traffic and account history to customer profiles and other internal records. This enables effective monitoring of potential risks or anomalies within multiple systems. You need a comprehensive view of all related activities across systems so you can identify patterns before they become an issue. Mobile devices can also prove invaluable when building an effective identity verification solution due to the range of personal information that can be gathered from them. This includes device properties such as location, IP address, hardware information, OS version, app versions installed etc., which can help build a more robust risk assessment profile for customers.

Once you have access to all relevant data sources, you also need a strong understanding of how they work together - when connected properly, data provides invaluable context and insights on potential fraud patterns or consumers who pose a higher threat. Having an automated system in place will ensure accuracy and reduce investigation times when anomalies arise.

When configuring your system, it’s essential that you protect against false positives by using reliable services like blacklisting databases to check user credentials against known fraudsters or threats before issuing access authorization. It's also important to configure customizable rules depending on the type of transaction or product being used so you have the most up-to-date insight into any potential risks. A comprehensive first-party data capture solution enhances this by feeding real-time data into fraud models for constant evolution.

Additionally, deploying layered authentication methods is key in making sure no one slips through the cracks—this should include using behavioral biometrics alongside traditional username/password logins or two-factor authentication solutions. By leveraging an additional layer of security you’ll reduce potential vulnerabilities within your systems while empowering customers with peace of mind when engaging with your products or services online.

6 steps to prevent fraudsters from exploiting gaps in your data and identity resolution

  1. Consolidate your data: Start by consolidating your customer data into a single, centralized system. This ensures all your data is up-to-date and accurate and makes it easier to identify suspicious activity.
  2. Implement a first-party data capture solution to eliminate organizational silos and avoid the limitations and delays caused by black box fraud solutions. An advanced solution will also address the first point, acting as a central source of customer identity.
  3. Verify customer identities: Use a multi-layered approach to identity verification to ensure each customer is who they claim to be. This can include passwords, 2FA or OTP, biometric authentication, and most importantly – an ID graph that leverages behavioral biometrics.
  4. Monitor for suspicious activity: Use fraud detection tools to identify suspicious activity, such as unusual transaction volumes or patterns, multiple account logins from different devices, or attempts to change account information.
  5. Layer behavioral biometrics into your ID graph and authentication methods to improve accuracy and reduce false positives.
  6. Respond quickly to suspicious activity: If you detect suspicious activity, act quickly. This might involve blocking a transaction, freezing an account, or launching an investigation.

By taking these steps, you can plug the holes in your data and identity resolution and prevent fraudsters from slipping through the cracks.

At the end of the day, effective identity resolution requires deep visibility into customer data and a strong understanding of how your data infrastructure works together. Comprehensive identity is essential to quickly diagnose any threats or weaknesses within your system and ensure they remain secure. With the right tools and processes in place, you can protect your business and your customers.

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