Fraud detection and prevention will still be a relevant issue in 2021. The more financial transactions take place online, the higher the risks of fraud. In addition, 2021 promises to be the year of instant payments, which means banks, online stores, and their customers need stronger protection than ever. In this article, we are going to discuss banking and eCommerce fraud prevention best practices you should start utilizing as soon as possible.
What Is Fraud Detection in Banking?
In order to properly define payment fraud detection, it is necessary to start by defining banking fraud. Banking fraud covers situations where customers, bank employees, or other persons commit illegal actions aimed at taking over money or data. What is more, in order for such actions to be classified as fraudulent, it is necessary that they are committed secretly, and most often, under the guise of bona fide transactions.
Thus, payment fraud detection is the post-factual disclosure of a fraudulent act in relation to a banking or other financial organization. However, it is also possible to prevent fraud by detecting it at an even earlier stage.
As for fraud prevention best practices, this is an analytical and technological process that allows you to intercept illegal operations at the stage of intention or at the stage of committing, but in any case, before the onset and manifestation of harmful consequences. Obviously, this approach is smarter, safer, and more efficient, especially considering that machine learning and artificial intelligence are used to detect and prevent fraud.
What Are the Risks and Frauds Which Happen Online?
There are a lot of approaches to financial fraud classification. To put it simply, financial fraud can be divided into three major sections:
- Bank fraud. Bank fraud covers a huge number of cases – for example, credit card fraud, ATM fraud, identity theft, phishing and phone fraud, money laundering and terrorism financing, and so on. What is more, no type of financial fraud is possible without the participation of a bank, which means losses and risks for both the financial institution and the victim. Therefore, credit card fraud detection using machine learning, as well as countering other types of fraud using this technology, makes the most sense.
- Corporate fraud. As for corporate fraud, these are the fraudulent activities performed by the financial institutions’ employees. What’s more, in order for such actions to be classified as fraud, the employee must be aware that his action is illegal and expect harmful consequences for the bank. That is, for example, inadvertent use of a phishing link that resulted in data loss cannot be considered corporate fraud.
- Insurance fraud. As the name implies, this situation covers the cases when an insurance company’s customers are trying to deceive it by claiming their payments twice, causes deliberate damage to health or property, or resorts to other actions aimed at unlawful receipt of insurance payment. In this case, the use of artificial intelligence and machine learning can prevent these situations through data analysis as well as intent recognition.
Also, we can’t miss eCommerce fraud since it is on a special rise right now, and banks are indirect participants in e-commerce fraud as well. Thus, online stores should also follow eCommerce fraud prevention best practices to protect their money, data, and reputation. SPD Group is a fraud detection solutions development expert that can offer you competent help when it comes to the need of protecting your online presence.
How Is Payment Fraud Detected – 7 Techniques
According to the Realtime Fraud Detection In The Banking Sector Using Data Mining Techniques Research, “Banks are experiencing challenges in protecting the online/internet banking channel. The challenge is in keeping customer’s accounts secure while avoiding complexity in the login process. However, the myriad of passwords, hardware token devices, and other out-of-bound communication tools introduced by some banks has greatly discouraged some customers. Well, it is obvious that security focuses less on customer convenience, but there can still be an improvement.”
Thus, there are some payment fraud detection and prevention practices that are efficient and safe to be used by the bank, and at the same time, they make customers’ experience even better and allow for avoiding a lot of inconveniences. Biometric identification is the simplest example of fraud prevention best practices. However, there are more sophisticated techniques aimed at credit card fraud detection using machine learning and other types of fraud prevention.
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1. Cash Transaction Monitoring |
As a part of this strategy, the bank asks for the sender and recipient identification. Also, there should be a document (a bill or invoice) confirming that this cash payment is a payment for the provided service or actual goods, and the amount itself should not exceed the limits set by the bank. In certain cases, the movement of funds is also checked after they are credited to the recipient’s account. |
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2. Check Tampering Prevention. |
Check tampering is one of the most common corporate fraud schemes. More often than not, this type of fraud is committed by employees who are authorized to write checks. In this case, they create fake checks that supposedly cover the company’s expenses, when in fact, they pay for goods or services for the fraudulent employee. In this case, the analysis of financial data and accounting with the help of AI makes sense, since when the company is overspending, this type of fraud will be quickly detected. |
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3. Billing control |
Billing control means the establishment of certain limits that cannot be exceeded during the execution of the transaction. AI helps to make sure that the limits are really not exceeded. |
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4. Decision trees |
A decision tree is a machine learning algorithm that can predict the outcome of an event using the basics of probability theory and the available knowledge about factors that can affect the outcome. It is one of the main fraud prevention tools in eCommerce and banking. |
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5. Data mining |
In a nutshell, data mining is the process of finding actionable insights in data arrays. As for using these methods in fraud detection, it allows for finding anomalies that can be a sign that the fraudulent transaction is happening. |
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6. Neural network |
A neural network is a system that is capable of detecting relationships between factors. For example, if the system knows that a bank customer has bought a ticket to another country and is now making a transaction while abroad, this transaction will be assessed as legitimate. |
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7. Hybrid methods |
Hybrid methods mean the simultaneous use of machine learning and human intelligence to achieve the perfect balance. According to best practices, ML and AI should always be under the control of a specialist, and their decisions should also be re-checked. |
How Do You Handle a Fraud Transaction?
Handling fraudulent transactions takes place in three steps.
- The bank is notified of the fraud. The process starts from a fraud alert – a signal that something goes wrong.
- The consumer verifies the fraud. If there are still some doubts about the legality/illegality of the transaction, the bank contacts the customer to find out his real intentions.
- The bank investigates the fraud. Next, if the fraud is already committed, the bank in collaboration with competent authorities (cyber police), investigates the case of fraud, and the fraudulent scenario is taken into account to train the machine learning model.
How to Protect Yourself From Internet Fraud?
In fact, it is not that difficult to protect yourself from Internet scams. Neglect of security rules, and especially careless use of social media, makes users the victims of fraud and hacking most often. Therefore, whether you are a corporate or personal user, follow these simple rules.
- Protect your passwords. Never use the same passwords for your accounts, generate them randomly, and store them in a reliable password manager.
- Educate your employees. Reckless behavior and ignorance of security rules can make your company and your customers victims of fraud. Start by training your employees to recognize and combat online fraud.
- Use machine learning for fraud detection and prevention. There are a lot of solutions for corporate use across different industries. What’s more, private users may also protect themselves with the help of a standard antivirus program powered by AI.
Conclusion
At the moment, there is no absolute defense against financial fraud. However, following best practices and using banking and eCommerce fraud prevention tools can greatly reduce your risks. Be sure to foresee the need to tackle fraud in 2021 and place the highest bet on machine learning and artificial intelligence. At the moment, this is the most advanced preventive technology that is becoming available even to small businesses.
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