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We’ve all heard the often-too-repeated narrative of the expanding role that artificial intelligence and machine learning plays in our lives. The domain of fraud detection is no exception to this, with companies looking for new solutions to detect and remove instances of fraud completely.

In this article, we will be diving into how traditional fraud detection works, how machine learning works effectively as a tool for fraud detection, some of its shortcomings, and how fraud detection machine learning engines are set up.

How can machine learning be used in fraud detection?

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If you’re wondering how machine learning can be so effective as a means of detecting fraud—all you need to understand is what machine learning is at its core.

Artificial intelligence algorithms paired with a dataset that trains itself over and over.

This can very well be extended to the fraud detection use-case.

With machine learning, you’re making use of AI algorithms and training them with your data to ensure that fraud detection happens easily and efficiently. You can then leverage rules that you set up to ensure that different instances of fraud—such as suspicious logins and transactions, identity theft—are immediately picked up by your machine learning system.

An example of one such software that helps with detecting fraudulent activity is SEON. SEON is a fraud detection software that uses machine learning to help prevent fraud.

But bear in mind—just as it may seem like machine learning engines are quicker and save more time—they are far from perfect. For the best results, ensure that you flag and report what counts as fraud and non-fraud in your dataset for better accuracy. You might need to run your machine learning engine for longer periods of time as well so that it is well-equipped to detect different patterns of fraud.

Know the difference between Machine Learning and AI

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To the layman, machine learning and artificial intelligence may seem like interchangeable terms.

But it’s important to clarify that they’re not exactly the same. Artificial intelligence is a concept much broader in scope and encompasses all efforts in computing to simulate the way that humans think.

On the other hand, machine learning is a subset of artificial intelligence where machines use historical data to train themselves for specific tasks without being reprogrammed.

As an interesting sidenote, it’s worth noting that even machine learning has its own subset/field called deep learning. Deep learning uses computing algorithms to closely simulate how the human brain works in a precise manner.

The advantages that machine learning brings to fraud detection

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Let’s understand why companies are taking to artificial intelligence, and more particularly machine learning, for fraud detection purposes:

Quick, and more efficient: Machine learning systems are generally fast and efficient at detecting different cases of fraudulent activity. In any case, they are much faster than human fraud detection agents and other manual processes.

Reduction in time needed for manual review: When you employ a machine learning engine to detect fraud from your data set, the time required for reviewing each and every data point becomes drastically reduced.

Bigger the data set, better the accuracy: For human agents, larger datasets often imply that more error might creep in due to the sheer volume of data under consideration. On the other hand, machine learning models get better and quicker with training on larger volumes of data.

Scalable and cost-effective fraud detection: With a single machine learning engine, you significantly reduce the costs of dealing with scalable fraud detection requirements. This can be crucial for companies that want to grow but keep costs associated with management of risk relatively the same.

24/7 fraud detection: This is a significant advantage that machine learning fraud detection systems bring to the table. ML engines do not require a lot of manual effort on the part of fraud detection teams and can detect fraudulent activity regardless of when they happen.

How fraud detection through machine learning still has scope for improvement

Despite the obvious advantages that machine learning has over manual methods of fraud detection, they still have a few pitfalls. Let’s discuss them here:

• Lesser degree of control: With machine learning engines like a black box model, errors may creep in without it ever being noticed by human agents.
• False Positives: Machine learning engines are dependent upon accurately set up data to a great extent. In the case that you report genuine activity as fraudulent, the algorithms in the machine learning model take this as a cue for reporting false positives. Again, it all depends on how you set up your machine learning model for fraud detection.
• Lack of psychological inference: Machine learning methods are ultimately algorithm-based and dependent upon the data that you feed them. They can in, no way, infer any psychology-based observation from any aberrant activity. For this, manual review by human agents is always preferred.

Steps involved in using machine learning for fraud detection

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These are some of the different steps that are involved whilst using machine learning for detecting fraudulent activity:

• Feeding the machine learning engine data: Any ML fraud detection system needs to collect data to detect patterns for both genuine and fraudulent activity. You can feed your engine data points pertaining to
• • SKU details
  • Transaction amount de
  • Type of card used
• This is information pertaining to the actual transaction—it might also be useful to find other data about how customers connect to your domain:
• • IP address
  • Device type/OS used
  • VPN/Proxy used
• Again a well-calibrated machine learning fraud detection system requires good data. Always make sure that the information you feed your engine is cleaned and does not contain falsely tagged data points.
• Generating rules for your machine learning model: This is the second step of setting your fraud detection machine learning system. This involves setting up rules for fraud detection. Rules can be of two types—
• • Rules with just a single parameter: These are basic or simple rules that draw upon just a single data point.
  • Rules with multiple parameters: These rules are more complex in nature and draw upon multiple data points at once.
• Doing review and activating the rules generated: Before you go ahead with activating the rules for your fraud detection model, you need to thoroughly review all the rules associated with different data points so that you’re calibrating the system in the right manner.
  In addition, you can also set up different activation trigger points for your rules depending upon any thresholds you want.
• Algorithm training: In this step of setting up your model, you’re training the system that you’ve created in the first three steps and creating a feedback loop. You can mark actions and score them based on how the algorithm has performed in relation to whether they’re able to detect fraudulent activity or not.
• Using historical data for testing the rules: It’s always a good idea to run the rules that you have set up for your machine learning engine on data from the past. This helps in estimating the accuracy of your machine learning model and also will enable you to see if the rules perform well in fraud detection for all your past data.

Machine learning is set to play an even bigger fraud detection role in the future

With artificial intelligence and machine learning capabilities only getting better by the day, it’s becoming clearer that they offer a robust solution to the problem of detecting fraudulent activity.

Machine learning systems, as we have seen in this article, provide significant advantages in the domain of fraud detection despite some of their shortcomings. Companies are turning to ML-based fraud detection engines since they’re fast, efficient, scalable, and cost-effective too.

Source: Plato Data Intelligence: Platodata.ai