Fraud Detection Machine Learning What is a financial fraud? Fraud particularity lies in cheating people. There are many different ways to do it. But if we speak about the financial field, there are a few widespread manipulations like:
Carding Counterfeit banking card skimming. Hackers are installing special devices on ATMs that read card data;
Maleficent software Viruses are not sleeping. Hackers create viruses that can simplify the process of stealing money. But in most cases an antivirus can detect such software;
Phishing A type of Internet fraud that allows criminals to steal the confidential data of users. Sometimes it can be a fake copy of your website, for example, where you input all your personal data but then falls into bad hands;
Mobile fraud A maleficient virus can read all information you input in your mobile app and steal important financial data.
What types of financial fraud your app may fall under?
Generally, all types of a financial fraud can be divided into 4 main groups: ● falsification of financial accounting; ● misuse/appropriation of company property; ● abuse of an official position; ● fraud in e-systems. In some cases, if it is about abuse of power or something like this, the administrative measure can be applied. However, when business processes that are in the fraud risk group are processed in informational systems, administrative measures can be useless. That is where anti-fraud systems come in.
Anti-fraud system and how it works Such systems represent specialized fraud prevention software or software and hardware systems that monitor, detect and manage fraud levels. Primarily, systems are developed for banks, telecommunication service providers, payment systems and so on. Cyber attacks of today are focused more on online banking services, maleficent software for mobile devices and special-purpose fraud in automated payment systems known as internal and insider fraud. So, to fight all mentioned threats to information security, software called financial fraud detection system or anti-fraud system is applied. By the way, our developers already have experience with the development of fraud detection systems. Principle of functioning Now then, let's look at these systems and explore how they function and how efficient they are at fraud detection. Basics Various developers create algorithms that may vary from similar ones in other software since they can be implemented in a different way. But the basic principles anti-fraud systems are working on remain permanent. First of all, it is a search of abnormalities like atypical actions in procedures with large data
arrays that often recur. The majority of developed systems have a basic range of actions that should be adapted to each specific case. So it may be better to create your own system from scratch. Thus, the main signal of fraud lies in atypical actions and it helps understand how to control frauds. It is necessary to arrange a working procedure with the fraud-detection system: ● You need to form ordinary and habitual actions users perform; ● You should adjust automatic notifications; ● If there are any deviations from the habitual working process, notifications will be activated.
How anti-fraud system works
But depending on your scope of activity, the formation of algorithm patterns
for fraud detection may vary, so keep that in mind. Analyzing the data In each specific case, the array of analyzed data will be different as well. So an anti-fraud system should analyze data collected from financial systems like automated banking systems, transaction databases for payment systems in your software and so on. Machine learning in banking industry, as well as any other industry, can become really necessary. Criteria for selection should be fixed individually by you in your software or we can do it upon the completion of development process according to your requirements. Architecture A fraud detection system as a full-fledged product for a large company will be oriented to the client-server structure. Technical features will depend on the design of a specific software developer and in the IT infrastructure the software is integrated into. But on the whole system will contain such components like: ● ● ● ●
system core kernel; database; client modules; management servers.
Machine learning and fraud detection - how they can be combined Smart anti-fraud systems installed in industrial data centers or server locations inside their algorithms use math models of the typical working day, while the formation of private behavior models (developed for business processes of a specific customer) occurs on the basis of machine learning technology with Big Data. The advantage of machine learning lies in self-learning. Considering the data accumulation from self-learning systems makes it possible to reduce the probability of false positive and false negative frauds in the financial sector. And it has a positive influence on the efficiency of the system. False positive fraud means ordinary legitimate transactions that were detected as dangerous and were blocked. It may seem better than if a real danger was ignored, but in fact, it may substantially slow down the business processes of the user. And time is money, don't forget it. False negative fraud means a real fraud that was ignored and caused serious losses and damage to the payment system and the user in particular. So the system should use machine learning to constantly improve its functioning constantly.
False positive and false negative frauds
Besides that, keep in mind that risk of ignoring fraud is always real. Cybercriminals create more and more crafty ways to attack payment systems worldwide. So that is why machine learning technology is a must-have feature in your software to protect it from any financial manipulations.