Machine Learning Based Fraud Detection Systems in Finance: All You Need to Know

Fraud detection algorithms are an integral part of all modern financial systems.

They are an invaluable tool that protects financial institutions from chargebacks, investigation fees, government fines, and reputational damage. A good prevention and detection system can help your business in a variety of ways. It can filter out the vast majority of fraudulent transactions, freeing up the resources of your security team.  

But not all fraud detection systems are created equal. Credit card fraud detection using machine learning is an exciting new development in the sphere of identifying payment anomalies. It allows financial institutions to block fraudulent transactions with never before seen accuracy. It helps them reduce the number of false positives for genuine transactions. And it does so while reducing overall IT costs.

So what exactly is machine learning-based payment fraud prevention? Is it right for your financial institution? How expensive is it? And is there a future for rule-based systems?

Read on to find out.

Fraud Statistics

As we’ve already discussed in our article on payment fraud, illegitimate payments are becoming an ever-increasing concern for financial institutions. The total amount of losses incurred by businesses due to payment fraud have more than tripled in just the last decade. In 2011, $10 billion was lost due to illicit payments. In 2020, this figure climbed to more than $32 billion, with 6.83 cents out of every $100 being lost to fraud.

And, according to industry analysts, the costs of payment fraud will only continue to increase. It is projected that fraud losses will rise a further 25% and exceed $40 billion by 2027. And that is not even mentioning the ever-present chargeback fees, investigation fees, and fines from governments and credit card issuers.

One of the most popular and effective ways banks and payment processors deal with payment fraud is through fraud detection algorithms. In 2020, detection algorithms were implemented by more than 60% of all banks.

The two principal routes a business can go about transaction monitoring is by implementing either a rule-based or a machine learning-based system. Let’s look at what these systems are and which option is best for your business.

Machine learning vs. rule-based systems in fraud detection

Rule-based systems and fraud detection machine learning algorithms are two completely different approaches to combating illicit payments.

Rule-based systems are more traditional. They are configured by internal security teams to help automate procedures and checks that a human expert would typically handle. This is by far the most common system out there today.

Machine learning (ML) systems are a much more modern and efficient way of dealing with safety procedure automation. A lot of major industry players have had outstanding success with ML algorithms, but they are still much less common than rule-based systems.

Rule-based systems

Rule-based systems use pre-programmed behavior scripts to identify suspicious transactions. This is similar to the way classic antivirus software functions.

Once a behavior linked to a fraudulent payment becomes known by the bank’s security team, they implement a rule to combat it. The criminals will then try to outsmart the rule.

Banks and payment processors using rule-based systems can only react to an exploit once their security team detects it. Naturally, by that point, at least some fraudulent payments have been committed and at least some losses have been incurred.

On top of that, rule-based systems become more and more expensive to maintain as they grow larger and more complex. Their proper functioning requires the hiring of a large team of IT specialists.

Machine learning-based fraud prevention systems

The love child of time-tested fraud detection and modern machine learning technologies, ML systems offer a much more proactive and flexible approach to identifying fraudulent payments. 

Machine learning systems enable financial institutions to detect irregularities and identify subtle changes in large data sets in a much more precise and granular way than was previously possible.

Rather than becoming more expensive to maintain with time, machine learning systems tend to become more effective and efficient without requiring any additional specialist labor.

What are the benefits of using machine learning in fraud detection?

In order to understand the key benefits of ML fraud detection, let’s look at the most common frustrations the security departments of the world’s leading financial institutions have when dealing with rule-based approaches and how they are solved by machine learning.

Effortless scaling

As criminals work their way around old rules, security teams must continue to introduce new, more sophisticated rule sets. As a result of this, rule-based systems keep getting larger, heavier, and slower as time goes on.

Additionally, as there is only so much granularity you can introduce into a rule, human-run reviews become needed more often, putting added stress on your security team.

High-quality financial fraud detection machine learning systems, on the other hand, tend to age like fine wine.

As the system is exposed to more data and new instances of fraud, it only becomes better at separating fraudulent payments from genuine transactions. This means that it can potentially require fewer manual reviews per million transactions as time goes on.

Reducing the number of false positives

As rule-based systems become more and more complex, another important problem surfaces. Using heaps of rules layered upon rules results in more and more genuine transactions being blocked by the system.

While these rules may save you some money on chargebacks, they will introduce added payment friction to your clients. As a result of this, some of them may turn to one of your competitors for their payment needs.

Being much more granular and precise, machine learning algorithms do not suffer from this problem.

Flexible outcomes

As rules are based on yes/no answers, they necessitate fixed outcomes. If you’ve spent any length of time in the financial services industry, you know that it is anything but static.

Let’s say your fraud team analyzes all fraud cases for the last year and imposes a rule that scrutinizes all orders above $300.

Years pass. You acquire new business clients. Many of them serve an upmarket audience. Now, orders of over $750 are the norm. Unless you want to alienate your new clients and their wealthy customer base with increased payment friction, you will need to amend the rule or make a set of exceptions. 

At the same time, you receive an influx of customers from a less developed region who like your services due to their low transaction costs. Now low-value transactions are more common. As a result, fraudsters use smaller payments of $20 to $50 to blend in with them. The $300 rule won’t do you any service here, either.

Payment fraud detection machine learning systems, on the other hand, are much more flexible in nature. They can understand data sets in a way no human ever could.

Check out the SDK.finance’s demo video to explore how SDK.finance provides a comprehensive view and control over client transactions, along with advanced AML and fraud prevention features, empowering institutions to stay ahead in the fight against financial crime:

Final words

Machine learning-based fraud prevention is an exciting new development in the prevention of illicit payments.

By replacing outdated rule-based systems with modern machine learning solutions, banks and payment processors can reduce the losses they incur due to fraud, lower their security system-related expenses, and reduce payment friction for their clients.

As for the companies that are hesitant to switch, the costs associated with maintaining their legacy payment fraud systems will eventually outweigh the investment necessary to introduce the more modern system. It is predicted that all major financial industry players will eventually transition to machine learning-based payment fraud prevention systems.