Have you ever been a victim of card fraud? If not, then it is a piece of good news as well to remain vigilant in the future. As we have shifted towards making more transactions online, the risk of fraud has also increased.
According to a report by Nilson, the losses from card fraud will account for $12.5 billion in the US by 2025.
Many companies use advanced technologies to secure data and tackle card fraud.
Does Your Company Need Machine Learning Fraud Detection?
Investing heavily in AI and machine learning helps improve the quality of decision-making regarding fraud detection and money laundering.
Machine learning is the most promising way to enhance a company’s production by eliminating fraudulent actions. However, it is crucial to highlight that machine learning is only one technique for detecting fraud, and it may not be the perfect option for every firm.
Considerations include your company’s size and complexity, the resources available for deploying and maintaining a machine learning system, and the overall performance of the solution in comparison to traditional techniques of fraud detection.
Benefits of Using Machine Learning to Combat Fraud
Since machines can process large amounts of data more effectively than humans, here are some benefits it provides.
- Speedy Detection
The algorithm can quickly find suspicious patterns and behaviours, which might take humans months to detect and solve. Although ML requires a lot of data to train, there is no possibility that it will be slower to detect fraud than a person.
- Informed Predictions
Inaccuracy in fraud detection, whether false negatives or false positives, must be as rare as feasible. By drastically lowering the chance of human mistake or prejudice tainting the data required for fraud detection, ML helps ensure that fraud is discovered precisely when it occurs before the illegal transaction can be processed. Human-Centric Intelligent Systems, for example, find a 95% accuracy rate for ANNs employed in credit card fraud detection.
- Cost-Effective Solution
Rather than hiring additional RiskOps workers, you simply need one machine-learning system to process all the data you throw at it, regardless of volume. It is significant for firms that see seasonal peaks and valleys in traffic, checkouts, or signups. A machine learning system is an excellent ally for scaling your business without significantly increasing risk management costs.
Limitations of Using Machine Learning for Fraud Detection
Machine learning can help detect irregular patterns in transactions. However, it is not perfect and does have some limitations.
- Lack of Human Understanding
No matter how much technology advances, it cannot replace humans when it comes to analyzing and interpreting data to determine the likelihood of questionable activity. A human’s psychological analysis and understanding are critical in accurately filtering and interpreting data to derive the meaning of a risk score.
- Difficulty in Interpretation
Machine learning algorithms can be tough to read and comprehend, particularly for those unfamiliar with the technical aspects of how they operate. Sometimes, it becomes difficult for people to understand why the model flagged a transaction as fraudulent.
- Costly
Maintaining an algorithm model is quite expensive. Companies that don’t have in-house expertise face roadblocks in operating it.
Which Machine Learning Algorithm Should You Use for Fraud Detection?
If you are brainstorming to adopt machine learning to your business processes, let’s discuss which machine learning algorithm to use for fraud detection.
- Supervised Learning
Here, the input should be disclosed as good or bad. This technique is based on predictive data analysis. The algorithm is trained to clarify data and make predictions based on this information. However, there is a drawback in the algorithm.
It cannot detect fraud that is not present in the historical data set from which it learned. Supervised learning is widely used in credit card fraud detection since fraudulent patterns can be learned from an analysis of previous transactions.
- Unsupervised Learning
There is no requirement for large amounts of data or human intervention. The algorithms work with unlabeled and unstructured data, helping discover hidden patterns. Unsupervised learning is mostly used when you need to detect patterns in consumer behaviour and recommend products similar to the ones already in the cart.
- Reinforcement Learning
This technique teaches itself from its own experience without a training data set. The algorithm constantly learns from the environment, making various decisions and receiving positive and negative feedback on its actions. A notable disadvantage of this technique is that it involves a lot of computation. Reinforcement learning is effective when fraud patterns are not understood.
Use Cases of Machine Learning in Fraud Detection
Let’s take a brief look at some examples of real-world applications.
- Anti-Money Laundering
A practical application of machine learning algorithms is using them in anti-money laundering programs in transaction monitoring. Financial organizations generally use rule-based scenario tools for transaction monitoring. However, these tools often crumple to capture the latest trends in anti-money laundering cases. Banks can apply machine learning across the entire anti-money laundering value chain by combining it with other advanced algorithms to identify suspicious patterns and behaviours.
ML models can also be used to detect wash trading. A real-life example is Web3 antivirus. It’s an open-source ML-powered plugin that protects users from signing dangerous transactions. For instance, a user wants to buy a non-fungible token. The plugin’s ML module scans the NFT, analyzes its previous sales, and detects its involvement in wash trading. As a result, the user can get actionable insights before buying the token.
- Rental Platforms
Just like any online business, rental platforms are often the targets of malicious actors aiming at stealing credit card data. Therefore, applying machine learning for fraud detection is a good practice.
Airbnb trains ML models using positive and negative examples from previous bookings to forecast the likelihood that the current booking is fraudulent.
- Online Gaming
Online gaming companies do all possible to ensure that all gamers are genuine. They also like to provide high-value incentives to new clients. A typical example is account sharing, which leads to unpredictable spending that looks like fraud. By using the ML models you can better detect in-game fraud.
Amazon offers a machine-learning solution that helps developer run and train their own ML models to detect fraud. It detects potential fraudulent activity and flags it for further investigation.
Conclusion
The use of machine learning models to combat fraud has proven to be highly effective. However, there is a growing need for custom software development services that are more difficult to abuse, as well as machine learning fraud detection.
The future of financial fraud detection using machine learning appears bright, with advances in AI algorithms and data analytics leading to more accurate and efficient fraud detection systems that adapt to developing fraud trends and safeguard organizations from financial losses.