Beyond the Welcome Email: How Neobanks Can Use Advanced Email Scoring to Detect Synthetic Identities During Onboarding

Introduction

The rise of neobanks has revolutionized the financial landscape, offering customers a seamless and digital-first banking experience. However, this convenience also opens the door to sophisticated fraud schemes, with synthetic identity fraud emerging as a significant threat. Synthetic identities are fabricated personas created by combining real and fake information, making them incredibly difficult to detect through traditional verification methods. For neobanks, where the onboarding process is entirely digital, the risk of unknowingly accepting these fraudulent accounts is magnified.

A recent study by McKinsey estimates that synthetic identity fraud is the fastest-growing type of financial crime in the United States, costing businesses billions of dollars annually.

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The Achilles' Heel of Digital Onboarding: Synthetic Identities

Neobanks pride themselves on rapid, frictionless onboarding. While this is a major draw for legitimate customers, it's also a vulnerability that fraudsters are quick to exploit. Synthetic identities are not stolen; they are meticulously crafted, often over long periods, to build a seemingly legitimate credit history. These "Frankenstein" identities can pass through initial KYC (Know Your Customer) checks that primarily focus on the validity of individual data points rather than the holistic picture. Fraudsters can then use these accounts for a variety of nefarious activities, from money laundering to loan stacking, leaving neobanks with substantial losses.

The core of the problem lies in the difficulty of distinguishing a synthetic identity from a real person who may have a limited credit history, such as a young adult or a recent immigrant. Both may lack extensive financial records, making it challenging for neobanks to make accurate risk assessments based on traditional credit bureau data alone. This is where more advanced, data-centric approaches become essential for protecting the integrity of the financial system.

Going Beyond Basic Email Validation

For years, financial institutions have relied on basic email verification to confirm that an email address exists and is capable of receiving mail. While this is a necessary first step, it is no longer sufficient to combat the threat of synthetic identities. Fraudsters can easily create valid email addresses from a multitude of free providers, and these emails will pass a simple validation check without raising any red flags. This is a critical gap in the onboarding process that neobanks must address.

A simple check for a valid MX record is not enough to determine the true risk associated with an email address. A fraudster can create a new email account in minutes, and it will appear perfectly legitimate to a system that only looks at its basic technical validity. Neobanks need a more sophisticated approach that can analyze the context and history of an email address to uncover hidden risk signals. This is where advanced email scoring comes into play.

Unmasking Fraud with Advanced Email Scoring

Advanced email scoring, such as Greip's Data Scoring & Validation service, provides a much deeper level of analysis. Instead of just verifying an email's existence, it assesses its overall quality and reputation. This is achieved by examining a wide range of data points that, when combined, can paint a clear picture of the email's legitimacy and potential for fraud.

This multi-faceted approach allows neobanks to move beyond a simple "valid" or "invalid" determination and instead receive a comprehensive risk score. This score is based on a variety of factors that are often overlooked by traditional verification methods. By leveraging this more nuanced assessment, neobanks can make more informed decisions during the onboarding process and significantly reduce their exposure to synthetic identity fraud.

Here are some of the key checks that an advanced email scoring system performs:

• Domain Reputation: Is the email address from a well-known and reputable domain, or is it from a disposable email provider known for facilitating fraud?
• Email Age: A brand-new email address is often a red flag, as it may have been created specifically for fraudulent purposes.
• Gibberish Check: Does the email address consist of a random string of characters, or does it appear to be a genuine name or phrase?
• Role-Based Email Detection: Emails such as "admin@" or "support@" are not typically used for personal banking and could indicate a fraudulent application.

A Step-by-Step Guide to Implementing Email Scoring

Integrating an advanced email scoring solution into your neobank's onboarding process can be a straightforward and highly effective way to combat synthetic identity fraud. By leveraging a real-time API, you can seamlessly add this powerful verification step without introducing unnecessary friction for legitimate customers. This proactive approach allows you to identify and block high-risk applications before they can cause harm.

The implementation process can be broken down into a few key stages, each designed to ensure a smooth and successful deployment. From initial setup to defining your risk thresholds, a well-planned integration will provide maximum protection with minimal disruption to your user experience.

Here is a step-by-step guide to implementing email scoring:

1. API Integration: The first step is to integrate an email scoring API, like the one offered by Greip, into your onboarding workflow.
2. Real-Time Scoring: As a new user signs up, their email address is sent to the API for real-time analysis.
3. Risk Assessment: The API returns a detailed risk score based on a comprehensive analysis of the email address.
4. Automated Decisioning: Based on the risk score, you can create automated rules to either accept, reject, or flag the application for manual review.
5. Continuous Monitoring: The system can be configured to continuously monitor for new fraud trends and adjust the scoring algorithm accordingly.

Real-World Scenarios: Putting Email Scoring to the Test

To truly understand the power of advanced email scoring, it's helpful to consider a few real-world scenarios. In each of these cases, traditional verification methods would likely fail to detect the fraud, while an email scoring system would raise immediate red flags. These examples highlight the importance of looking beyond the surface-level validity of an email address and digging deeper into its contextual signals.

Consider the case of a fraudster attempting to open multiple accounts using synthetic identities. They might use a series of newly created email addresses from a free provider. While each of these emails would pass a basic validation check, an advanced scoring system would identify them as high-risk due to their recent creation date and lack of history.

Here are a few scenarios where email scoring can make a critical difference:

• The Disposable Email: A user signs up with a disposable email address from a known provider. The email scoring system immediately flags this as a high-risk indicator.
• The Gibberish Handle: An application is submitted with an email like "[email protected]." The system's gibberish check identifies this as a likely machine-generated email and assigns a high-risk score.
• The Brand-New Domain: A fraudster registers a new domain and creates an email address to appear more legitimate. An advanced system would detect the domain's recent registration and flag it for review.
• The Role-Based Email: A user attempts to open a personal account with an email like "[email protected]." This is immediately identified as a role-based email and a potential indicator of fraud.

Overcoming Common Implementation Hurdles

While the benefits of advanced email scoring are clear, neobanks may face a few common challenges during implementation. These can range from concerns about adding friction to the onboarding process to the technical complexities of API integration. However, with careful planning and the right partner, these hurdles can be easily overcome. A phased approach to implementation can also help to minimize disruption and ensure a smooth transition.

One of the most common concerns is the potential for false positives, where a legitimate customer is incorrectly flagged as high-risk. This is a valid concern, but a well-tuned email scoring system can be configured to minimize false positives while still providing a high level of protection. By working closely with your provider, you can fine-tune the risk thresholds to match your specific risk appetite. You can test your email addresses using a free tool like Greip's Email Scoring tool.

Advanced Tactics for Proactive Fraud Prevention

Once you have a solid foundation of email scoring in place, you can begin to explore more advanced tactics for proactive fraud prevention. This can involve combining email risk signals with other data points, such as IP address intelligence and device fingerprinting, to create a truly comprehensive risk profile for each user. This layered approach allows you to identify even the most sophisticated fraud attempts and stay one step ahead of the criminals.

By correlating email risk data with information from other sources, you can uncover hidden connections between seemingly unrelated accounts. For example, if multiple applications are submitted from the same device but with different email addresses, this could be a strong indicator of a fraud ring. An IP Lookup API can also be used to identify high-risk locations and proxies that are commonly used by fraudsters.

Here are some advanced techniques for leveraging email scoring:

• Multi-Layered Risk Assessment: Combine email scoring with IP intelligence, device fingerprinting, and behavioral biometrics for a holistic view of user risk.
• Link Analysis: Use data from multiple applications to identify fraud rings and other coordinated attacks.
• Machine Learning Models: Develop custom machine learning models that are trained on your own historical data to detect emerging fraud patterns.

The Future of Onboarding Security for Neobanks

As the financial landscape continues to evolve, so too will the methods used by fraudsters. Synthetic identity fraud is a constantly moving target, and neobanks must be prepared to adapt their defenses accordingly. The future of onboarding security lies in a combination of advanced data analysis, machine learning, and real-time monitoring. By embracing these technologies, neobanks can create a secure and frictionless experience for their customers while staying ahead of the ever-present threat of fraud.

The next generation of fraud prevention will be defined by its ability to analyze vast amounts of data in real time and identify subtle patterns that are invisible to the human eye. This will require a move away from static rule-based systems and towards more dynamic, adaptive technologies that can learn and evolve in response to new threats. For neobanks, the ability to detect and prevent fake accounts will be a key differentiator in a crowded and competitive market.

Conclusion

The threat of synthetic identity fraud is real and growing, and neobanks are a prime target. Traditional verification methods are no longer enough to protect against these sophisticated attacks. By implementing advanced email scoring, neobanks can gain a much deeper understanding of the risk associated with each new applicant and make more informed decisions during the onboarding process. This proactive approach to fraud prevention is essential for protecting the integrity of the financial system and building a sustainable, long-term business. By leveraging the power of data and technology, neobanks can create a secure and seamless experience that will attract and retain loyal customers.