How to Use Machine Learning in Fraud Detection

Fraud detection and prevention process involves identifying the signs and markers of a financial cyber security threat to flag and prevents it from materializing. The fraud detection process, when its heavily dependent on the manual process, can become tedious and highly inaccurate. That’s why more teams are embracing machine learning in fraud detection.

Machine learning fraud detection systems are more efficient in analyzing larger datasets without tiring and can quickly and more accurately detect signs of fraudulent activities before it happens. If you want to learn how you can implement machine learning in fraud detection, you should continue reading. We’ll talk about how machine learning in fraud prevention works and present the advantages and disadvantages for better decision-making.

What Is Machine Learning In Fraud Detection?

Machine learning in fraud detection involves using data from previous occurrences to guide machine learning algorithms regarding authentic fraudulent activities and regular legal activities so that it can recognize and flag future occurrences. There are two classes of machine learning, and they’re called supervised learning and unsupervised learning.

Supervised learning requires you to label and section the data the system needs to learn and input it into the machine learning system so it can begin detecting fraud anonymously. On the other hand, unsupervised learning doesn’t require to label or sectioning the data. Instead, you provide the data and leave the algorithm to do the grouping without human intervention.

Unsupervised machine learning comes in especially handy when there are unrecognizable threats. Although, it requires more computing power to work seamlessly.

Comparing in-house machine learning fraud detection and outsourcing

There isn’t a generic method for whether you should put an in-house machine learning team together or outsource your machine learning fraud detection system.

1. Cost: Setting up an on-site team to create a custom machine-learning model would mean hiring machine-learning experts. Meanwhile, outsourcing only involves you paying someone else to do the heavy lifting.
2. Preparing data is time-consuming: You may need to prepare the data that will teach the machine learning model about what fraudulent and non-fraudulent activity is. It’s a drawn-out process you may not have time for. Hence, outsourcing would be more efficient.
3. Range of data sources: With on-site machine learning models and fraud detection, you only have access to information that’s within your reach, but that may not be enough to form accurate rules and thresholds. In contrast, outsourced teams have access to more data from previous customers and can implement what they’ve learned in your case to provide more precise results.

How Does Machine Learning Fraud Detection Work?

Whether you decide to outsource your fraud detection machine learning operations or you have an in-house team to handle it, machine learning for fraud detection follows a range of steps that ensure no fraudulent attempt gets past you:

1. Data input for source material: First, the machine learning algorithm needs information to form its beliefs. Feed it information such as credit card information and the origin of the financial activity. Also, try not to overload it by only supplying the essential information.
2. Generate system rules: Set your machine learning system to implement complex or simple rules for flagging and blocking suspicious activities on a financial account. This is also where you select the threshold and rejection, and acceptance rate.
3. Review and activate algorithm rules: Once you set the rules that the machine learning algorithm will operate on, you’ll need to review the rules and then activate it when you’re sure that everything meets your quality checks. To adjust the threshold, you may do it manually or automatically adjust it with machine learning.
4. Train the algorithm: Initially, the algorithm is still trying to learn what constitutes a fraudulent transaction and what doesn’t. That’s why when you’re just starting, your team must provide feedback on the machine’s early results by accepting, declining, or reviewing the results. With time, you wouldn’t need to do this as frequently as before.
5. Sandbox historical testing: This step involves reviewing and testing past successful fraud attempts within a sandbox environment. Here, you’re allowed to toggle the rules off or on and test the accuracy of your system.

Types Of Fraudulent Activities And How Machine Learning Can Prevent Them

Before discussing the solution, let’s examine some scenarios surrounding fraud detection and machine learning:

Tax fraud

Some individuals and businesses try to beat the system by not paying taxes by concealing assets or underreporting income and expenses. Machine learning utilizes diverse markers to tell whether the person is lying or not.

Insurance claims fraud

Machine learning in fraud detection can curb deceitful insurance claims by accessing notes and insurance forms filled out by victims, doctors, and nurses. It finds discrepancies or irregularities in the way the forms are filled.

Identity theft

Scheming e-commerce scams and loan applications are instances of how identity theft happens. Machine learning can determine abnormal behavior on an account through behavioral detection techniques.

Credit card fraud

Machine learning systems have the ability to spot credit card fraud by noting the purchase of luxury goods and sudden recurring purchases on a card that’s near it expiry date. Another instance is when the algorithm detects multiple spending or payment methods being added to the card within a short period. To curb such activities and prevent further activities, the machine learning system will flag down such transactions.

Money laundering

Machine learning in fraud detection for money laundering cases aims to note three types of transactions: money laundering, money transfers flagged by the bank due to unusual alerts, and normal legal transactions. The machine learning algorithm takes note of the sender and receiver information and their spending habit and raises the alarm in the future if there’s a similar, recurring transaction.

Market manipulation

Market manipulation refers to when someone maliciously affects the supply and demand dynamics to gain profits from price changes. Machine learning has the feature to prevent market manipulation by tracking stock buyer and broker information to spot possible indications of market manipulation.

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

Organizations that implement machine learning in their fraud prevention operation will experience benefits that include the following:

Algorithms can work 24/7; humans can’t

Fraudsters and the financial crimes they commit do not go on a break just because Joe, the fraud detector, has closed from the fraud prevention job for the day. Contrary to Joe, machine learning systems don’t go on breaks, retire for the night, or end their shift.

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Machine learning is more cost-effective

Generally, machine learning systems can keep you from hiring more staff for RiskOps. Specifically, there are businesses whose operations increase with seasons or periods, and it may not be feasible to hire additional fraud analysts and fraud detection specialists solely for those periods.

Hiring people according to the season would mean more resources spent on onboarding and ensuring they blend into the company quickly. Whereas machine learning systems function round the clock and throughout the year regardless of traffic. Also, when the organization wishes to scale in times of increased fraud attempts, they only need to scale the system.

More accurate predictions with larger datasets

Before you can predict how something works or behaves, you’ll need to see it in action often enough. The same situation pertains to fraud detection. Unfortunately, the downside to solely revolving around human effort is that humans have limited capacity to consume or assess information.

But machine learning systems are programmed to process larger datasets that are more than human capacity is capable of. More importantly, because machine learning systems have a larger capacity, they can accurately predict fraud within a shorter period.

Faster manual review times

Without machine learning systems, we’ll be stuck manually and painstakingly reviewing data piece by piece. The result would be an increase in workload, effort, and time that could have been used more efficiently and effectively with technology. With machine learning, the algorithm can go through the same data sets far faster than a human can.

The key would be to combine both. So when a fraud detector manually reviews data in conjunction with ML technology, they wouldn’t use too much effort. So it becomes easier, quicker, and less demanding.

What are the drawbacks of using machine learning for fraud detection?

Technology’s primary and theoretical purpose is to make the life of its users easier and more efficient. That’s also the story of machine learning in fraud detection. But as with all technology, machine learning also has its downsides that may affect your choice.

You already know how advantageous machine learning is to your fraud detection efforts. Knowing its disadvantages can help you combine human intervention and technological advancement in a seamless process that works for you.

Some disadvantages of using machine learning in fraud detection include the following:

Not being 100% reliable

No matter how many datasets the machine learning algorithm learns from, it cannot 100% detect fraud. That’s because human psychology, though predictable, is dynamic in nature and can wind and twist in ways the computer can’t understand or predict.

That’s why machine learning can’t completely beat the human touch in understanding human psychology and how other human beings operate. For that grey area, you’ll need human fraud detectors to detect fraud attempts where the machine can’t.

False positives can affect the rest of the result

The way machine learning systems operate is that they’re able to learn from the data you provide and detect which behavior is positive fraudulent behavior or false fraudulent behavior. But as we pointed out earlier, it can’t predict this 100%.

Sometimes, the machine learning algorithm can detect a legitimate action and mark it as “positive” fraudulent behavior. If this goes undetected by human eyes, it can mess with the rest of the result, and it’ll begin marking other legitimate activities as fraudulent behavior.

Less control over mistakes

While machine learning has a greater chance of accuracy, it can sometimes make errors. This gives you less control over the outcome of your results, especially if you allow it to go unchecked.

Conclusion

The more we advance in technological solutions, the more sophisticated fraudulent transactions and attempts will become. While many people are still skeptical about the ethical viability of machine learning and artificial intelligence, machine learning algorithms remain the top option for detecting fraudulent transactions such as identity theft, credit card fraud, tax fraud, and money laundering.

The industry might be better off focusing on creating a balance between man and machine that suits the needs of each situation. Machine learning has its defects, and only the human touch, along with setting rules, thresholds, and reviewing parameters, can software and systems metamorphose along with fraudulent attempts.

Frequently Asked Questions (FAQs)

What is machine learning?

Machine learning can be defined as the process of developing and using a set of computer algorithms and statistical models to learn, adapt, analyze, and draw conclusions from a data pool for a specific goal.

What are the types of machine learning?

The types of machine learning are supervised learning and unsupervised learning. Unsupervised learning is the more demanding method based on the computational requirement.

Should you use on-site or outsourced machine learning fraud detection models?

It depends on your preference regarding cost, availability to perform data preparation, and the diversity of the data you have at hand. In most cases, it might be better to outsource than to gather an in-house team.

If you are thinking of starting a new machine learning project, contact us now!