5 Questions to Ask When Choosing a Fraud Detection Software

According to Fortune Business Insights’ Market Research Report, the global fraud detection and prevention market could reach $129.17 billion in 2029, in light of its compound annual growth rate of 22.8%.

This staggering industry growth lets us know two things in particular:

1. Businesses are willing to spend increasing amounts to reduce their fraud losses.
2. In the oncoming years, we will very likely see an explosion in the number of fraud prevention tools available.

All of this makes the decision-making process even more important when you’re looking for one or more fraud prevention systems. So how do you pick a fraud prevention tool that is efficient, future-proof and the right fit for your business?

Just begin by asking the following 5 questions before choosing it.

So how do you pick a fraud prevention tool that is efficient, future-proof and the right fit for your business? Just begin by asking the following 5 questions before choosing it.

5 Factors you Should Consider Before Choosing a Fraud Tool

1. What Kind of Fraud Prevention System is it?

Not all fraud detection platforms are created equal. Roughly speaking, you have 4 choices here: niche API solutions, legacy platforms, dedicated in-house staff and systems, and cloud-based software. Choosing the right one means weighing up the advantages and disadvantages of each options, so let’s now check out the table below.

• Niche API Solutions: These focus on aggregating data from various sources, and sharing it amongst companies to get a good picture of who fraudsters might be. Usually require one license per provider, which means costs can quickly add up. A middleware platform needs to be built, which also increases the budget.
• Traditional, Legacy Platforms: Fraud prevention is nothing new, and some services have been in operation for many years. They also operate by trading data between enterprises to predict fraudster abuse. However, what they gain in maturity is often lost in agility and affordability. The offers are often pricey, and the technology dated, making integration into new platforms less intuitive than with newer solutions.
• In-House solutions: Staffing your own fraud prevention department is absolutely possible. It is usually preferred for data-privacy reasons, and the team will benefit from great insider knowledge about the company. Unfortunately, the main issue is with scaling. Salaries and costs aren’t easy to budget when you never know how regular fraud attacks will be.
• Cloud-Based Solutions: Cloud-based solutions from third-party providers have a number of advantages. Scaling possibilities are an obvious one, as you can pay depending on usage. This, of course, has a strong influence on costs and reducing overheads. Similarly, updates and bug fixes are all taken care of by the provider. No need to monitor upgrades or to develop extra features in-house.

2. Does it Accept Custom Data and Parameters?

The more accurate data you feed into the system, the more precise the results. Commonly, this data is acquired through fields, which users fill in with personal information such as name, date of birth or email address.

However, digital wallet operators (online casinos or marketplaces) have very different needs from online retailers. To adapt to industry-specific needs, the provider should offer the ability to process custom fields and parameters. Ideally, merchant-specific requests are handled with ease, and at no extra costs.

One classic example is that fraudsters typically buy more size 9 shoes online as they are easier to resell. Could your solution incorporate this knowledge into its rule-decision model?

3. How Fast Are The Decisions?

It is crucial to have a fraud prevention system that detects a high rate of potentially criminal behavior, as well as one that is quick to make the decision to flag you accordingly.

Ask yourself, therefore: Once integrated into your platform, how quickly can you make a decision to allow the right processes? With this in mind, your fraud prevention tool should offer the following:

• real-time blocking
• asynchronous requests (whereby one data point is immediately checked, while other data points are queued up for analysis)
• a fundamentally short response time to help accommodate these considerations

It is also highly recommended that you choose a tool that has a strong ability to carry out well-informed, highly-efficient velocity checks. Velocity checks take stock of suspicious behavior that occurs within a certain time frame – such as the frequent use of frozen cards within, say, a matter of minutes.

4. Is the Risk-Scoring Adjustable?

Fraud detection systems usually deliver results via scoring or classification. The former gives you a better picture of how much trust you should put into each account user. With classifications, you only get a yes or no, which can be particularly troublesome if the solution isn’t completely whitebox.

Regardless of which system you choose, it becomes misleading if your tool lacks the ability to fine-tune its scoring or classification model. This is one of the many reasons why it’s worth asking the software provider in advance if manual adjustments are available, and how they are performed.

You can also read up on SEON’s fraud scoring capabilities if you’d like to learn more about the value of providing points to account users and the effectiveness that such customizability brings to fraud prevention.

5. Is it Whitebox or Blackbox?

Whether you choose a whitebox machine learning system or blackbox machine learning system boils down to how much transparency you wish to receive as the user of your chosen fraud prevention software. 

This is because whitebox machine learning explains its decision-making process to users, whereas blackbox machine learning calculates its decisions regardless of human input.

Depending on how much clarity you need in your fraud prevention systems’ machine learning interventions, you may benefit from investing in the former. Your team will be able to make sense of the data, rather than having to trust a strictly machine-given score.

However, it really depends on how much time and other resources you wish to invest in accommodating that team’s interventions, and of course, the question of how much you trust human common sense and intuition over the processing powers and accuracy of machine learning technology.

You can learn more about whitebox and blackbox machine learning systems by reading up on SEON’s software offerings, as the company provides both of the exciting technologies and therefore the multitude of benefits offered by each.

Final Thoughts

With a growing number of fraud-prevention tools and fraud attacks to defend, it can be easy for merchants to be confused. It is bad enough that many of them already have to fight relentless fraud attacks – but now, on top of that, they are faced with further challenges when choosing the right solution.

Hopefully, this article will serve as a good primer. And don’t forget to download our eBook for even more in-depth information such as:

• The state of fraud prevention
• Current challenges of fraud prevention tools
• 6 extra questions to ask when choosing your tool

The document is designed to answer questions for fraud managers and business owners of all sizes, and in all verticals, whether you want to reduce fraud for an ecommerce, financial institution, online gambling platform or travel industry company.

You might also be interested in reading about:

• SEON: Ecommerce Fraud Detection & Prevention
• SEON: Payment Fraud Detection & Prevention
• SEON: Compare the Top 9 Fraud Management Systems
• SEON: Best Fraud Detection Software

Learn more about:

Data Enrichment | Browser Fingerprinting | Device Fingerprinting | Fraud Detection API

Sources

Fortune Business Insights: Fraud Detection and Prevention Market Size | Global Report, 2029