Fraud is a major problem in all institutions. The more institutions grow, the more opportunities fraudsters have. Therefore, there is a need for institutions to improve fraud detection. This article will discuss the various ways institutions can improve fraud detection, including customer vetting, proper account maintenance, and machine learning. Let’s dive in.
When a new customer approaches your financial institution intending to open an account, you need to vet them thoroughly. Thorough vetting will help you identify fraudsters early on.
Get your customers’ information (both digital and physical) to assess their fraud risk. It is crucial that you get the right information from your customer during the onboarding process. You can either get it actively (by asking for it) or passively (through research).
Once you get the necessary information, you should verify it. Enformion offers a platform where you can easily verify prospective customers’ information. Combining digital and physical identity intelligence goes a long way in improving fraud detection.
Fraud can occur at login. Therefore, there is a need to incorporate techniques to prove identity at logins and during transactions. It would be best if you were sure that your vetted customer is the one operating the account. The authentication process you select should be smooth-running, extensible, and risk-based. Additionally, it should match the threat level.
Consider incorporating a knowledge-based authentication, where you ask the customer a unique question to verify their identity. You can also incorporate push notifications, voice identification, among others. Note that having a multi-factor authentication process improves fraud detection.
Additionally, it is important to verify your customer’s identities on all the devices they use. Most customers use various devices to access their accounts, and fraudsters tend to use this to their advantage by getting the customer’s data and trying to access the account digitally.
Your authentication process should have the ability to differentiate a fraudster from a genuine customer regardless of the channel they use to try to access their account.
Machine learning improves the efficiency and accuracy of fraud detection. Note that most fraudsters are smart and can easily find a way around any rules you put in place, but with machine learning, the algorithm self-learns and flags any activity or transaction that seems abnormal.
There are two main types of machine learning; unsupervised machine learning and supervised machine learning. Unsupervised machine learning refers to instances where the algorithm is not given a target. It flags any potential risk. With supervised machine learning, the algorithm only flags targeted risks.
Machine learning has several benefits. For starters, it is efficient. It significantly reduces the number of false positives and, in turn, saves the institution a lot of time and money. Machine learning also uncovers risks that the institution may not have thought of looking for.
Your employees should be knowledgeable about the various fraud detection techniques that the institution has in place. This way, they can alert the necessary officials when they encounter potential risks. Remember that employees are an important asset when it comes to fraud detection.
As discussed above, no verification or authentication technique can seamlessly detect fraud on its own. Therefore, it would be best to incorporate a multi-layered defense strategy.
While improving fraud detection in your institution, you must pay attention to your customer experience. Your verification and authentication services should be fast and efficient. Additionally, they should not have a bad impact on your customers’ experience. Having a good customer relationship is important.
If you want to improve fraud detection in your institution and still give your customers a good experience, contact us. Our experts will give you fitting solutions to help detect fraud in your institution.