In a world where there are no absolute security guarantees, any organization could potentially become a victim of fraud. And non-profit companies, too – because data sometimes takes on even more weight than pure money. The more companies come up with protection methods, the more scammers come up with ways to outwit them in return. This is like running in a vicious circle, and the only thing that can break this circle is the ability to predict fraudulent attempts and prevent them. Machine learning is capable of this, and in this article, we will look at how fraud detection with ML works for different industries.
Fraud Detection with ML for Different Industries
Every year, a business loses up to five percent of profits due to fraud. It may seem that this is a very small amount, which is not worth worrying about, but in monetary terms, this figure can reach almost four billion dollars. That is, four billion is the net income of fraudsters, which very often is reinvested in other illegal operations. This situation clearly requires a response.
Businesses can no longer afford to leave machine learning out of their fraud detection arsenal, said Ashley Kramer, SVP Product Management at Alteryx.
In practice, it is indeed much more difficult to deal with the consequences of fraud than to prevent it. Let’s look at examples from different areas of the business.
Machine Learning for Fraud Detection in Banking
The banking sector is one of the most attractive business areas for fraud because there is both physical money and a lot of valuable customer data. Here’s how machine learning can work in this area.
Credit Card Fraud Detection
Most financial fraud cases involve credit card fraud. Here is the classification of types of credit card fraud and a description of how machine learning can prevent each.
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Card not present |
In the case of buying goods in a physical store, it is possible to detect fraud using face recognition cameras. That is, an AI-based device can distinguish an unlawful buyer even if the operation is carried out without a card. |
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Stolen or lost credit card |
This is the case when a machine learning system can quickly detect theft through behavior analysis. As soon as card transactions are not similar to the standard behavior of the owner, the system signals a possible abduction. |
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Account Takeover |
In this case, the machine learning system also analyzes patterns of behavior and most likely finds an anomaly – for example, when a hacker starts making too large purchases from a stolen account. |
Safe Investment Strategies
Investments always involve a certain risk, as well as the need to process a large amount of data in order to determine the right vector for investment. Machine learning systems were created for data analysis, and for them, this is a natural operation.
Moreover, using smart algorithms, financial institutions can receive much better insights based on the largest possible amounts of data. In addition, machine learning for developing strategies means the ability to get a solution free of emotions and personal beliefs, which means that the probability of its correctness increases as much as possible.
Low-Risk Loans
Machine learning systems work similarly to assess the level of credit risk. At that moment when the client applies to get a loan, the system extracts, evaluates, and systematizes all the known information about a particular person – from the gender and level of earnings to the history of searches on Google and social networks. On this basis, the algorithm concludes that the risk of issuing a loan is risky, as well as about a person’s involvement in illegal activities.
Money Laundering and Terrorism Financing Prevention
Both of these operations are carried out with the participation of banks, and very often banks are confident that they serve law-abiding customers. In fact, this statement can be far from the truth, and a careful analysis of the data can help establish the likelihood of a person being involved in illegal activities.
AI Fraud Detection in Insurance
The insurance industry is also attractive enough for fraud, as there is always the temptation to create an artificial insured event to get paid. Here’s how machine learning can work to prevent fraudulent attempts.
Fraudulent and Duplicate Claims Prevention
The essence of this type of fraud is clear from the name:
- either the fraudster is trying to get insurance benefits, for example, through identity theft,
- or the owner of the insurance policy intends to deceive the company and receive the due payment for the second time in a row.
In the first case, machine learning can work with behavior analysis, as well as recognize a face during a meeting in the office. In the second case, the system will work with existing data, plus analyze the likelihood that a particular client may attempt to cheat – for example, if such precedents are known in relation to another insurance company.
Detection of Initial Intentions
Cameras that can recognize emotions and intentions are already created for retail, but it is clear that they will be more useful for the financial and insurance sectors. The bottom line is that a camera equipped with sensors recognizes a person’s intentions based on his facial expressions, and the artificial intelligence algorithm processes this data and concludes whether the intentions of a particular client are honest, for example, at the time of signing an insurance contract.
Ecommerce Fraud Detection and Prevention
This is how machine learning works to prevent e-commerce fraud.
RTO Fraud
In this case, the attacker buys the goods and then returns the fake according to the return conditions. It is possible to prevent such attempts in the physical store through cameras with recognition of faces and intentions, as well as through behavioral analysis when buying online.
Identity Theft
This is a situation where an attacker breaks into a user’s account on an e-commerce platform and begins to make purchases on his behalf. In this case, the system monitors behavior and reveals anomalies. For example, if an attacker buys an Apple Watch, but before that, the user was not interested in this product on the Internet, this may be suspicious.
Proxy Detection
Fraudsters like to use a proxy server in order to create the illusion of ordering from different countries. Typically, this attempt also involves simultaneous credit card fraud or identity theft. In any case, machine learning systems catch such attempts and give an alarming signal.
Mobile Fraud
Mobile fraud attempts are growing exponentially, and there are more and more mobile fraud types invented. Some of them are:
- account takeover fraud
- call center fraud
- prepaid gifts-card fraud
- friendly fraud.
In all these cases, the machine learning system is designed to prevent fraudulent attempts by analyzing users’ behavior and catching anomalies in real-time.
Fraud Detection System for Healthcare
The healthcare sector is also attractive for scammers because here they can steal valuable medical data that are of great value on the black market, as well as get the opportunity to blackmail victims. The following infographic shows nine ways to use artificial intelligence and machine learning in the medical field, and then we will describe how these technologies can be used to combat fraud.
Medical Recipes Abuses
In this case, the machine learning system will be a kind of supervisory authority that will monitor the sequence, correctness, and the number of prescriptions given to patients, especially in cases where medicines can be dangerous to health and have value on the black market.
Medical Data Breaches
As we already said, medical data is especially valuable and vulnerable, especially for patients who have diseases that are condemned by society, such as AIDS or drug addiction. Machine learning and artificial intelligence applications can act as a protective wall between personal data and scammers.
Identity Scams Prevention
In this case, machine learning and artificial intelligence can also work for the purpose of personality recognition. A classic example is retina recognition, as in films about secret medical developments. In addition, this approach will be useful, for example, for cases when doctors work with narcotic drugs of strict accountability – face recognition helps to be sure that these drugs are in the hands of a responsible person.
Conclusion – Is It Suitable for My Industry?
As you can see, the possibilities of machine learning and artificial intelligence for analyzing data and detecting anomalies are practically limitless and universal. This makes these technologies suitable for all businesses that recognize that it is no longer possible to move forward without data. The most important thing to do is to correctly determine your business goals for which the ML and AI solution will be created, and get in touch with a reliable vendor.
