Machine Learning For Fraud Prevention - Why It's The Best Tool Yet

With the development and sophistication of modern technologies, life has become much more comfortable. While it was considered impossible in the past to conduct complicated operations simultaneously, a computer made this task way easier. 

At the same time, the methods of gaining illegal access to computers using Spyware, Ransomware, and other illicit applications have also become rampant. Hackers use various tools to impact the operation of networks and steal data from people. 

Fraud schemes are prevalent, as well. In some cases, they are so well-designed that it is impossible to distinguish between the genuine and fake. AI is used frequently to tackle those threats, and in this article, we will review why machine learning is the best tool for fraud prevention.

Bank accounts are in the high-risk group

Large cash flows, billions of transactions, and payment transactions for millions of customers create favorable conditions for hackers to compromise people’s bank accounts. The actions of fraudsters cause not only direct material damage but also undermine the credibility of a financial institution, causing a severe blow to its reputation. 

Today the number of targeted attacks has significantly increased, in which a specific victim is selected, and the attack itself is carefully prepared and carried out by various groups of attackers specializing in a specific type of activity: developing and selling malicious code, breaking communication channels, which leads to the emergence of new fraud schemes.

By the method of influencing banking systems, fraud is divided into external and internal, in which bank employees are involved. Fraud can also be divided into channels for its implementation: bank branches - unlawful execution of account expenditure operations, fraud with crediting of compensations, payments, refunds, temporary borrowing of funds, illegal operations with dormant accounts, reversal; bank and payment cards - skimming (card compromise in payment terminals and ATM), CNP fraud (Card Not Present, card data compromise when making purchases on the Internet); phishing - misrepresentation of a client for spending transactions; remote banking services - compromising a channel, changing client information, unauthorized transfers, changing the details of a recipient in a payment order, etc.

Each of the fraudulent schemes has its actions to prepare, sell, and withdraw and cash-out money due to the features of the service channel, method of compromise, composition stolen data, etc.

Anti-fraud systems the best tool against prevention

The most effective way to protect against external and internal fraudsters is to use anti-fraud systems that can control payment and session transactions of bank customers, evaluate the actions of bank employees, quickly identify new fraud schemes in various service channels, and also prevent the withdrawal of funds from customer accounts. 

This also applies to other industries as well, especially those that have a very high percentage of fraud. For example, let’s take the crypto industry. It is believed that 80% of all ICOs that occurred between 2018-2019 were fraudulent. This created a premise that every crypto project is a scam. We are very well aware that is very far from the truth. Nowadays, fraud detection AI is used to confirm scam accusations rather than find them. For example, during the Bitcoin Evolution scam drama, the company employed several AI specialists to have the algorithms take a look at company activities. In the end, the AI managed to absolve the company of its accusations, which proved to be more trustworthy than the word of professionals.

The main feature of the anti-fraud system is the ability to aggregate large amounts of data from various sources, which allows you to see operations in the context of client and employee actions in different channels. The main objectives of the anti-fraud system are:

• Analysis and processing of flows of financial and non-financial transactions committed in various systems;
• Applying business rules and algorithms to detect suspicious activity
• Identification of behavior patterns uncharacteristic for a client or employee
• Identifying a sequence of events with signs of fraud
• Providing convenient tools for investigating and analyzing data

Expert systems are also widely used to detect fraud transactions, containing many statistical rules and logical expressions aimed at identifying suspicious transactions, but this approach has several disadvantages.

Why machine learning proves to be an effective way against fraud?

The use of machine learning methods together with statistical rules helps to reduce the risks associated with the limitations of expert systems, in particular, to reduce the number of cases where legitimate transactions are erroneously identified as fraudulent and to increase the number of successfully detected truly fraudulent transactions. Machine learning algorithms can detect dependencies that are not obvious to humans, quickly analyzing huge amounts of data.

To detect fraud, learning algorithms are used both with a teacher (supervised learning) and without a teacher (unsupervised learning). In the first case, we are talking mainly about classification algorithms, when there is a training sample with previously known answers, and in the second, there are no such answers. Transnational sequences can be considered as text, and then methods of the analysis of text data and processing of a natural language (NLP) appear.

For the classification algorithms to work, it is necessary to have a data set, for example, for some limited period, with confirmed fraudulent and legitimate transactions. However, when marking up transactions, difficulties inevitably arise: it is often necessary to do this manually according to information taken from acts of fraud investigations for the period chosen for building models. A sample of fraudulent transactions can also be obtained using machine parsing of investigation documents, but due to their poor structured structure of good quality, such a sample is difficult to achieve.

When learning with a teacher, a class imbalance is inevitable: the number of legitimate transactions is hundreds of thousands of times the number of fraudulent ones. In this case, the following methods are used: data balancing; filtering; enrichment of the sample by “re-marking” additional transactions, with a high probability identified by the expert as fraudulent. Besides, semi-supervised learning methods are used, which use both transactions for which it is known whether they are fraud or not, and transactions for which this answer is not.

Conclusion

When solving fraud detection problems, a thorough preliminary analysis of the data and the choice of the correct methodology for constructing and validating the effectiveness of the models are of great importance, since otherwise, it will likely be necessary to retrain the models. There is no one standard solution that would be equally well suited for any tasks of detecting fraud - in each case, an individual approach is required that takes into account all the features of the problem and the requirements for the anti-fraud system.

While machines are not flawless mechanisms and also make mistakes, yet they are the best tools to confront the fraud which disrupts the normal operation of banks, systems, various networks. Tech experts work a lot to improve their operation further and make them more alert against threats.