Businesses worldwide lose up to 10% of their annual revenue or $3.7 trillion on average to fraud. On the other hand, frauds are difficult to detect and organizations managed to find out who conducted the fraud in 17% of financial audits only. In most cases, frauds are conducted by employees, managers, and customers but there are also cases when the one to conduct a fraud is a business owner.
That is why companies have started exploring new ways to protect their assets and turned to data science and machine learning as the most powerful tech weapons of our age. Today, we are talking about how these technologies help with fraud detection, the benefits of machine learning, and how to actually use it to prevent fraud.
How Does Machine Learning Help With Fraud Detection?
In order to detect fraud, you should train the machine learning engine first. That includes using historical data and creating rules artificial intelligence will use to detect potential flags. For instance, you can train it to detect and block fraudulent transactions or suspicious logins. However, you should also create non-fraud rules to ensure higher precision and accuracy.
Note that there is a difference between machine learning and AI. AI is a wider concept while machine learning is its subcategory and deep learning is a subset of machine learning. Machine learning, just like its name suggests, makes it possible for machines to learn from data.
3 Benefits of Machine Learning for Fraud Detection
Unlike humans, machines can process large datasets and identify uncommon behaviors and patterns in milliseconds. AI and machine learning can truly put any process on speed and help with accelerating profound discoveries.
Less manual work and fewer costs
For the above-mentioned reasons, there is no need for human agents to review data manually anymore. Machines will do all the hard work, plus, they can run 24/7 without the need to take a break.
Businesses now don’t have to increase risk management costs when scaling since machine learning systems can replace multiple employees and handle literally any volume of data, even during the busiest periods.
The longer the algorithm runs, the more accurate it gets. Machine learning engines can process large data assets, find similar patterns, and get easily trained, which is not the case with humans who would need months to identify suspicious behaviors or find similarities in different kinds of fraudulent behaviors. What’s more, according to studies, machine learning algorithms have a 96% success rate in detecting and preventing fraud.
Which Industries Are Using Data Science and Machine Learning for Fraud Detection?
It is predicted that a myriad of eCommerce websites and online stores will lose up to $50 billion to fraud by 2024. That’s why some popular eCommerce brands have started using machine learning to protect valuable data, find out which products fraudsters target the most, which card payments to block, and to understand why the system flags some transactions as fraudulent.
Online Gaming and Gambling
Betting and gambling platforms as well as iGaming companies typically offer attractive rewards and signup bonuses to new users. Wanting to get as many bonuses as possible, some users create multiple accounts in order to claim multiple bonuses.
Users are trying to set up multiple accounts, cheat players, use poker bots, or fake the number of affiliates users they bringing. All of this can be easily detected by machine learning systems that analyze data and suspicious behavior. That’s why numerous online gaming companies use data science and machine learning to make sure their users are real.
Metaverse companies and tech giants are embracing AI and machine learning, too. Knowing that many people are looking for ways to make money in Metaverse, it is also super important to prevent fraud in a virtual world where you can’t really tell who is who.
Financial institutions like banks, insurance providers, and fintech companies need to make sure that they are not dealing with scammers but they also have to remain competitive in the market. Data science and machine learning can help with identifying fraudulent profiles, avoiding regulatory fines, and, finally, getting valuable insights about their user base and typical user profile and what they can do to improve their service.
How to Use Machine Learning to Detect and Prevent Fraud
To get the most accurate results from the very beginning, gather as much data as you can. If you are already using a fraud prevention tool but it doesn’t support adding custom fields, you’ll have to do all this manually.
For instance, if you are running an eCommerce business, you need to collect data such as stock keeping unit, transaction value, and type of credit card. Then, you’ll need customer-related data such as the type of device they are using and IP data.
You can set single (if-this-then-that) or multi-parameter rules and tighten the triggering conditions whenever it is needed. Rules can be super descriptive so that you can clearly understand how certain actions, such as logins can end up being fraudulent.
You can and you should review rules from time to time and adjust thresholds manually. For example, you can filter rules by type and accuracy and enable or disable machine-learning suggestions.
Train and Test the Algorithm
To ensure that the algorithm reaches maximum accuracy, you should train and test it every 180 days or even sooner.
Alternatively, you can let the machine learning system retrain itself based on the data accumulated while you can access and review these rules at any time. This can be super important since you should be able to single out the rules that have helped with fraud detection and prevention in past cases.
You can calculate the algorithm’s accuracy within a certain date range and then maybe set new rules or tweak the current ones and monitor results.
No matter if you are a business owner or a fraud manager, you should gain complete control over your risk strategy, and data science and machine learning can definitely help with all this. With time, you’ll prevent and reduce fraud attempts to almost none.
Author: Nina Petrov is a content marketing specialist, passionate about graphic design, content marketing, and the new generation of green and social businesses. She starts the day scrolling her digest on new digital trends while sipping a cup of coffee with milk and sugar. Her white little bunny tends to reply to your emails when she is on vacation.
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