How to reduce false positives and false negatives at onboarding

Does your user identity verification at onboarding result in high false positives and/or false negatives?

Reducing false positives and false negatives is imperative to minimizing lost business opportunities and avoiding revenue losses.

In this post, we take a look into what are false positives and false negatives, as well as the ways to reduce them. Read on!

What are false positives and false negatives?

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In fraud prevention parlance, false positives refer to approval of those users, who should ideally have been rejected at the authentication stage. These are usually bad actors who use fake, false or tampered documents to impersonate good users.

False positives can expose a company and its consumers to heightened risk of fraud, other criminal activities and cause damage – both financial and reputational.

False negatives, on the other hand, refer to those genuine users who were deemed suspicious and, therefore, rejected. This failure generally occurs due to inaccurate facial and document capture as well as the inability to identify exceptions in the documents from certain states and periods of issuance.

False negatives can lead to loss of business opportunities as a result of incorrectly closing the entry doors on genuine users, partners or suppliers.

Both false positives and false negatives can adversely impact a business and can result in significant losses for a company. To avoid them, you may consider the following guidelines for your anti-fraud operations, choice of solutions, and the vendors to partner with.

Four essential considerations

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To reduce false positives and false negatives in your onboarding process, we recommend the following four key parameters:

Access to data

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Data drives today’s digital economy. Lack of data can impede user identity verification efforts and increase the risk of fraud, as you can only analyze the data that you capture. To be able to support efficient user identification at onboarding, you need access to robust databases.

There are two ways you can access more data to support your analysis. First, request necessary information from users; and second, have access to good databases – such as a databank of confirmed fraudulent identities.

Furthermore, for enhanced document analysis, we recommend an artificial intelligence-driven tool that can quickly identify inconsistencies, if any, by comparing a document with thousands of other documents from the same state and year of issuance on several parameters.

Leverage the latest technologies

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The reduction of false positives and false negatives at onboarding is also closely linked to the adoption of automated processes that leverage artificial intelligence. Manual reviews to verify every user at onboarding can be time-consuming, error-prone and costly.

Manual reviews, however, can be need-based, whereby human experts review only the critical or unusual cases that require extra checks to determine the authenticity of a user or document.

Adopt efficient rules

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False positives and false negatives are dependent on how stringent or lenient your verification rules are.

For example, with strict parameters, you run the risk of increasing false negatives; whereas, if the parameters are extremely flexible, you may see a spike in false positives.

Balancing the verification rules perfectly is a constant challenge. A platform with configurable filters and the flexibility to modify rules can play an important role in obtaining reliable data, which aligns with the specific needs of your business.

Training and development

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The reduction of false positives and false negatives requires a combination of state-of-the-art technology and human expertise based on continuous training and development.

Training is an essential measure that can help experts determine the authenticity of a user or document in special cases to supplement the use of technology. This will further improve the ability to anticipate possible failures and ensure maximum efficiency for user identity verification solutions.

How we can help

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Caf has the best-in-class suite of identity verification solutions that combine face matching, facial biometrics and the most efficient document verification on the market to accurately detect and prevent fraud attempts.

With access to relevant and reliable databases for document verification and a proprietary database of more than 40,000 fraudulent identities, we enable businesses to quickly identify fraudsters and minimize false positives.

With Caf, you can increase your approval rates without running the risk of accepting fraudsters in your onboarding process.

To learn how Caf can help you accurately verify user identities at onboarding and minimize false positives and false negatives, request a free demo now.