False-Positive in Fraud Detection & Benefits of Reducing FPR in Fraud

There are several criteria in fraud detection flows for a transaction to be qualified as fraud. Transactions except normal flows are considered abnormal activities. Abnormal activities are detected by the fraud detection tools used by companies, and these flows can be varied according to the tool used. Considering the diversity of users and the variety of criteria evaluated in transaction flows, qualifying any transaction as a fraud if that is against the flows that are defined as normal can lead to false-positive in fraud detection. This situation causes loss of potential real customers.

Transactions that are described as suspicious but have not yet been verified are considered false-positive in fraud detection. False positives can cause huge financial and reputational losses to companies in both the short and long term.

What is customer insulting in fraud detection?

False positive in fraud detection most commonly occur when a transaction is legitimate but detected as suspicious. A legal transaction may be considered false by the algorithm because it occurs differently from the normal flows. False rejections can lead to qualifying a legitimate customer as risky and losing that customer in the long term. Not only will you lose revenue, but you will also leave a negative impression on customers. This damages your brand reputation.

It is wrong to think of false positives only as lost revenue from a customer's transaction. If a legitimate customer experiences a negative digital journey it can lead to the permanent loss of this customer.

Considering the bad reputation that your brand has created on these customers, the customers can express their bad experience to potential customers around them. In this case, your loss would be much more than just a customer.

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Why Reducing False-Positive Rate More Important Than Reducing Fraud Itself?
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Reducing the false-positive rate (FPR) in fraud detection comes with many benefits for businesses and FinTechs.

1. Reduces manual reviews
2. Provides that your fraud analysts working efficiently
3. Provides your customers a good digital journey
4. Protects your reputation
5. Allows you to work result-oriented
6. Creates a relationship of trust with your customers
7. You won't lose your legitimate customers

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1. Reduces manual reviews

You need to consider many criteria when trying to prevent fraud. When evaluating your performance on these criteria, you should also consider false positives. Detecting false positives in fraud detection as quickly as possible helps you turn big losses into profits in fraud detection. If you don't detect false positives in real-time, your potential gains could turn into long-term losses.

Progressing in fraud detection with manual reviews creates a large part of your fraud teams' workload. Encountering false-positive cases in manual review processes can cause a serious workload for fraud teams. Also, false positives that cannot be detected in real-time, can cause serious losses for businesses. Reducing manual investigations with machine learning technologies not only reduces the workload of fraud teams but also helps teams to use their energy and time for more critical events, to spare time for detecting real frauds, and to prevent losses.

Other disadvantages of manual detection are that experts are unable to recognize emerging fraud patterns spread in the database and cannot detect fraudulent behavior as soon as it is attempted.

The key to fraud detection and minimizing the costs of fraud-related losses is to use technologies that can detect fraud in real-time, supported by machine learning, instead of traditional detection methods requiring manual reviews.

2. Provides that your fraud analysts working efficiently

False-positive in fraud detection is a costly one. Companies use rule sets in fraud detection processes. Rule sets are software that can perform predetermined actions based on certain criteria. For example, if a company knows that mismatches between certain bill addresses and IPs are bad, defines rule sets according to this. But these rule sets have a limit, it is not entirely possible to reduce millions of different scenarios to specific rule sets. In addition, highly experienced analysts are needed to write rule sets most properly, and this is not possible for every company. Although you work with experienced analysts, it is a serious workload and it can be time-consuming to take action. It is almost impossible to compensate for the losses that will be experienced.

It is also very difficult to manage the defined rules on a large scale. Old rules need to be regularly monitored, restored, and changed. Disruption of this maintenance process leads to loss of efficiency from the rules. This triggers an increase in false-positive rates (FPR).

When a false positive warning signal is given, a review process begins and a manual review is required so operating costs increase accordingly. These manual reviews are the biggest barrier for fraud analysts to focus on critical cases. Dealing with false positives in fraud detection increases your losses based on fraud many times over, by causing you not only to miss real frauds but also to lose real customers.

3. Provides your customers a good digital journey

As a service or product provider, it should be important for you to optimize the experience of your customers on your digital channels and their transaction flow in terms of user experience. Every step your customers have digitally in the process of benefiting from your services is defined as a digital journey. This journey includes many steps.

The stage where you are closest to your customers is the acquisition stage. At this stage, your customers have transactions through their accounts and create an acquisition request for your product/service. The transactions that customers have on their digital journeys change on a user base. The method used by each user during the buying stage and the time spent are different. Therefore, it is very difficult to standardize the fraud detection flows at this point. Millions of different customers can mean millions of different transaction flows. The fraud detection flows you currently use can detect a legal customer as illegal and reflect on you as a false-positive transaction in fraud detection at the end of the day.

We talked about the harms of false positive in fraud detection for your company and fraud teams. In addition, the preventions you take for suspicious transactions may cause you to experience a negative digital journey to a legal customer. The limitations applied for suspicious transactions, which you use in the fraud detection flows, may result in the permanent loss of a real customer. As a result of the bad impression, they have about your company, these customers transfer this experience to their connections, so which damages your company's reputation.  

A false-positive case should not be considered as just a loss of transaction.

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4. Protects your reputation  

Rejected transactions can harm your company's reputation. After a bad purchase experience, most of your customers will give up on the purchase and others will blame your company for a possible second bad experience. Unfortunately, the rejected customer's bad experience is not just limited to losing a customer, especially if they decide to share this experience with their friends or via social media. An unhappy customer can significantly affect future sales because people often transfer their bad experiences to their friends.

This can lead to serious reputational losses for your company. Even an interaction that can be created on social media can cause this bad experience to reach even people who did not know you before. False positive in fraud detection can cause you to mistakenly mark your customers as fraudulent. This results in both a bad reputation and loss of customers in the long term.

5. Allows you to work result-oriented

While false positives can have devastating effects for companies, they are often overlooked in the vast of fraud prevention processes. Many companies use a variety of technologies to automate their fraud detection processes. These technologies aim to detect and restrict transactions that are generally found to be risky as soon as possible. However, the rule flows defined at the point of why transactions are considered risky do not always restrict a truly illegal transaction. Most of the time, it can also describe a real customer as a suspect. One of the main reasons for this problem is that false positive in fraud detection are extremely difficult to scale.

When a system marks a transaction as fraudulent, fraud analysts or systems often reject the transaction without further investigation. Whereas, unless you are using a system that detects false positives, these processes require extensive manual reviews. Because false positives are so difficult to detect, companies need to implement appropriate smart software as part of their fraud prevention strategy. The most effective solutions for detecting false positives are fraud detection tools with artificial intelligence-supported infrastructures.  

Another option is to have every transaction seen as suspicious be analyzed one by one by experienced fraud experts. However, this approach is quite time-consuming and requires a heavy workload. It can cause a lot of material and moral losses to your company. With the right fraud prevention technology, businesses can spend less time to manual review, accept more orders, and increase revenue.

6. Creates a relationship of trust with your customers

In fraud prevention, a false positive represents a potentially loyal customer rejected by an inefficient fraud detection software. You should balance your company's safety and trust relationship with your customers as you develop preventions to the fraud risk. You should design systems to create a customer experience that balances trust and usefulness and develop methods to facilitate low-risk customers and transactions. The amount of lost sales is a major concern. But it is also very risky to lose potential revenue from lost customers due to false positives. At this point, you should show that your trust in your loyal customers, and you should not damage this trust relationship. To succeed in this balance you should use proper technologies to struggle with false positives.

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7. You won't lose your legitimate customers

You should integrating the positive approaches and trust relationship that your company is trying to protect toward customers as much as possible in your digital processes. It will always provide your users with a more positive experience.    

Your product quality is not the only thing that connects your customers to your company. Their positive experience, and trust in the product acquisition step is a significant factor in their loyalty.  It is not easy to maintain this connection, but it is just as easy to lose it. In order not to lose your legitimate customers and to make their experience with your company always positive, you should integrate the opportunities of modern technology into your fraud processes. Utilizing these technologies not only protects you against fraud but also improves the experience of your legitimate customers and contributes to your brand image.

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