How do fraud prevention systems detect financial scams? Many businesses other than banking systems need to be protected from fraud. Today there are machine learning tools capable of countering social engineering, but should you use them? Read about how to select a fraud prevention tool to combat financial sector scams.
The economic feasibility of making fraud prevention systems a part of your information security policy, is beyond doubt. Anti-fraud tools in the financial sector can significantly reduce the damage caused by illegal transactions. However, while the effectiveness of such solutions in the fight against malware is quite high, the identification of illegitimate actions based on social engineering is fraught with certain difficulties.
Let us figure out how to choose and implement a fraud protection system, as well as find out if there are means to fight phone fraudsters, fake bank security officers, and other similar criminals. Financial institutions need to identify and prevent illegal transactions. Fraud prevention systems have been invented specifically for these purposes. Based on multiple parameters, they make a decision to automatically block illegal financial transactions in real-time. There are cross-channel fraud prevention systems, as well as systems targeting only one specific channel.
It should be noted that banking fraud prevention goes far beyond financial transactions. Banks have many different products, and almost all of them can be affected by some type of fraud. This can be credit fraud, money laundering, abuse of payment channels, and other kinds of misdoings.
Not only does a fraud prevention tool detect scam payments already committed, but it also prevents such payments at the stage of preparation. Session antifraud tools allow you to identify risks at the stage of authorization in the system using a stolen username and password.
The development of online services creates a breeding ground for abuse and fraud, increasing the growth of economic fraud. Only the joint efforts of security experts, law enforcement agencies, and the banking community, as well as the education of users, will be able to dramatically improve the security of financial systems and prevent people who seek to earn money illegally
The transactional analysis becomes less effective as the scammers act in such a way that in terms of financial transactions, things look pretty common (or there are so many off-type operations that it is difficult to process them on a transactional level). That being said, the more signs of fraud are available to the system, both transactional and non-transactional, the more effectively scams can be countered.
Attacks powered by social engineering have received the most remarkable development according to cybersecurity statistics. This area is dominated by phone fraudsters who represent themselves as bank employees or other officials. Some of these scams, especially those related to the withdrawal of cash by the victim and depositing it into an account in another bank, are especially difficult to detect by security systems. Slightly less popular methods of cybercriminal attacks leverage phishing and the use of banking Trojans that may come bundled with adware or other malware.
One of the essential factors contributing to the development of financial fraud is the low level of technical literacy among users. Educational efforts are critical to develop cybersecurity skills needed for business, It is not the sole responsibility of governments; manufacturers of information security systems should contribute too.
A couple of years have already passed since fraud prevention organizations in the banking sector missed the trend of socially engineered crime. At that time, it seemed that attacks would develop, first of all, technologically, but it turned out to be easier and cheaper for the attackers to focus their efforts on primitive mass calling and such like schemes.
Anti-fraud systems use a variety of tools to detect fraudulent activities. These include but are not limited to device identification, remote access detection, identification of linked accounts, and bot attacks. Anti-fraud suites control various channels that an attack can go through such as remote banking, card payments, and internal transactions. They make use of behavioral analysis methods to detect social engineering attacks effectively.
One of the important aspects of fraud prevention is detecting remote access to the user’s device. Often, the purpose of a fraudster connecting to a banking system is not to directly execute a transaction. Their aim is to find out the second identification factor and perform a card-to-card transfer. Detecting such attacks requires protecting all possible channels with fraud prevention.
Is this a third-party solution, an in-house development, or an open-source product?
Home-made systems focused on protecting individual channels (as a rule, they are based on open-source projects) might be worth trying. Still, there are practically no reasons for switching to them from commercial, cross-channel solutions.
Meanwhile, the reverse process is in full swing. Business fraud detection solutions, as a rule, can receive information about fraudsters from other products of the same vendor, which improves the quality of detection. In addition, specialized developers enjoy the option of global profiling as they compare data originating from different users of their systems.
Fraud prevention can be supplied to the client both as a licensed (on-premises) solution and through a service model. In the latter case, the vendor provides the subscriber with cross-channel protection using the vendor’s technologies. The cost of such a service depends on the amount of data to be processed, as well as on the accuracy and quality of detection.
In some cases, the customer may accept the risk that certain attacks will not be detected in exchange for lower costs. Cybercriminals are making efforts to neutralize fraud prevention systems. One of their attack options is DDoS targeting the transmission channels of data collected by the system. The vendor can share responsibility with the client since financial institutions usually have established protection against this type of malicious activity. Anti-fraud system performance is very important for the key business processes of the client. Primary attention should be paid to the issue of protecting it from hacks and malicious or accidental changes in the rules it is governed by.
The fraud prevention system can be licensed based on the number of accounts to be protected (session model) or by the number of payments to be controlled (transactional model.) The list of channels that the system protects can serve as a secondary license metric. In general, the price of a fraud prevention solution depends on the load on the vendor’s resources that the clients generate. Therefore, even when using the session model, the protected accounts can be ranked depending on their activity.
The critical shortage of fraud prevention professionals spawns a reasonable question of whether to work with clients using a service model. One of the bottlenecks of the service model is the issue of connecting to the system. The problems of finding the necessary data and transferring it to the fraud prevention environment will be the same both for an on-premises solution and for a cloud system. Mistrust in matters of transferring data to external storage, which is typical for the banking sector, also works against the service model.
As regards the tools that the fraud prevention system uses to detect fraudulent transactions, in addition to verifying payments, specialized products actively use the analysis of user behavior. For example, when following the instructions provided by another person, the victim would change the style of filling out forms and navigating through menu items. Tracking this behavior, you can mark the session as high-risk and conduct an additional check of its legitimacy.
Biometric technologies as a means of additional user authentication raise certain concerns. These things are device-specific and will not work if the client tries to log in from a computer without a camera or fingerprint reader. In addition, biometric authentication in modern gadgets is still relatively unreliable and can be circumvented by intruders.
There are already available fraud prevention systems that use speech analytics, as well as tools that, based on facial expressions and eye movements, are capable of detecting critical deviations from typical user behavior. They still cannot play a key role, but the data they provide can be used in conjunction with other indicators. One of the most popular functions of fraud prevention is behavioral analysis, as well as analysis of reputation and comparison of user system fingerprints.
One of the key methods of anti-fraud operation is role-based access control (RBAC), whereby the decision to block a transaction considers a particular set of user roles and privileges. Many systems also use machine learning based on tagged incident data. Ongoing training enables artificial intelligence to identify potentially illegitimate transactions and assign higher fraud risk scores to those events. In addition, AI can be used to detect abnormal behavioral factors.
Meanwhile, the verdict made by the machine learning-based system is generally less transparent than the decision made with the help of other, simpler algorithms. Nevertheless, artificial intelligence has proven itself well in reducing the number of false positives, which allows a financial institution to reduce the cost of protecting against fraud. The use of machine learning requires significant preparatory work from the vendor and the client to prepare the data on which the system will be trained.
Predicting the development of the fraud prevention market, it should first be noted that the attack vector may shift to new payment options and tools that will appear in the coming years. These can be various electronic currencies. The task of information security specialists is not to overlook the trend of scams and thefts and create tools to protect new channels.
Social engineering attacks will get more sophisticated. To prevent them, fraud prevention systems will move along the path of developing the functionality of behavior analysis. In the future, the number of additional signals might increase, ultimately improving the quality of fraud prevention. One of the primary sources of such data will be signals collected by telecom operators.
We can safely predict the integration of information flows associated with the movement of funds, which will lead to an increase in the quality of detection of illegal activity. The obvious solution to the problem is to unite the efforts of various financial institutions and exchange information between them.
Anti-fraud systems are not only essential for ensuring the security of financial transactions, but they can also supply information to other business processes of the bank. Modern fraud prevention systems are tools capable of both controlling remote banking and direct card-to-card payments and effectively combating internal threats of a financial institution. The biggest challenge for developers of fraud prevention system solutions now is the identification of attacks based on social engineering scenarios. Behavioral analytics to boost business cybersecurity, as well as machine learning, used for that purpose, are gaining ground.
Author Bio: David Balaban is a computer security researcher with over 18 years of experience in malware analysis and antivirus software evaluation. David runs MacSecurity.net and Privacy-PC.com projects that present expert opinions on contemporary information security matters, including social engineering, malware, penetration testing, threat intelligence, online privacy, and white hat hacking. David has a strong malware troubleshooting background, with a recent focus on ransomware countermeasures.