Machine Learning In Fraud Detection: A Simple Guide

Future is here so we rely more and more on modern technologies and computer software to take care of such issues as storing confidential details, making financial transactions, and many others. Nowadays artificial intelligence not simply shows great potential but is already used in spheres not limited to information technology one. AI helps with detecting fraudulent transactions, blocks spam emails, makes personal recommendations of products or services, and even assists with accurate medical diagnoses.

Machine learning is a part of AI and it is widely used to prevent fraudulent activity. That’s why when you look for ways of protecting your software, securing a payment system, etc., considering the use of machine learning systems is a great way to save time, money and people resources.

In this article, we’re going to sort out what machine learning is and ways it can secure our data as well as significantly reduce the number of fraudulent transactions. So if you want to find out how to secure personal information and reduce risks which result in money loses, stay tuned!

Principles of anti-fraud system functionality

First of all, let’s take a look at anti-fraud systems to understand how they work, what models are present now and their basic features.

There is no single machine learning algorithm or method that surely works in each sphere. Success comes out of combining machine learning-based methods, trying different variations and testing them with a few datasets.

Basic features of anti-fraud systems

As mentioned, various algorithms are currently used for preventing online fraud, however, basic principles remain the same. Whether you’re going to use an old well-designed pattern or create a new one from scratch, the basic range of actions should be used for both. This way, in case of any atypical activity the system will recognize it and act according to the installed pattern.

Here are the main steps you need to take to configure a fraud detecting system:

• Form an algorithm of actions ordinary users perform;
• Configure automatic notifications system;
• If there are any signs of atypical activity, the system will send a notification.

How fraud-detection system works

Main elements of a fraud-detection system

The main elements of an anti-fraud system include:

• system core;
• database;
• client modules;
• servers.

Moreover, it depends on your company and customers’ requirements what other elements should be included during the development of an eligible system.

The way of combining machine learning and fraud detection

The main advantage of machine learning is that the system works on its own development – non-stop self-learning process. Data accumulation makes it possible to reduce the probability of false positive (ordinary transaction detected as dangerous and blocked) and false negative frauds (malevolent activity ignored by the system and resulted in financial loses) in the business sector. Certainly, the self-learning process has a positive influence on the efficiency of the system as it becomes more and more efficient.

Unfortunately, even the uses of advanced system cannot guarantee its flawless performance. Cybercriminals are on guard and constantly working on new, more efficient ways of attacking banking and payment systems. However, if you’re going to invest some money in protection software, self-learning system is a great asset for protecting your company from informational and financial manipulations.

Machine learning algorithms

In the field of machine learning, there are two main models:

Supervised model is the most common type of machine learning that attempts to get new details from identified records. At the start of using a supervised model, you add fraudulent and non-fraudulent records to see if the system can detect whether fraud is present or not.

Unsupervised machine learning model differs as its performance is connected with the identification of anomalous activity by comparing received data to the number of instructions and patterns set previously. However, a downside of the unsupervised model lies in assessing the accuracy of the approach until the given result is checked and verified by specialists.

How to prevent fraud with the help of machine learning

When creating your own anti-fraud system, you should know how to prevent financial fraud and here we mention the main ways to do it.

Getting acquainted with basic customer behavior
Know Your Customer (KYC) is a widely used process of verifying a client’s identity and predicting risks they can bring to your company. A detecting system based on this behavior model monitors customers’ actions with the help of databases and data science. Bank creates a specific pattern for each type of clients, and once the system based on Know Your Customer behavior is implemented, it divides all customers’ actions into ordinary and suspicious. Thus, for example, if a customer made an unusual transaction which is far from their habitual ones, the system would send a notification bringing your attention to what happened.

Data analysis
The second method of fraudulent activity detection depends on the process of analyzing all available data. This approach is of more quality and brings more accurate results because the learning system implemented works more efficiently.
Still, even such a well-developed fraud detection system requires a specialist to watch over it – to check for false transactions and make sure they are indeed fraud ones and fix the problem of a false alarm.

Social graphs analysis
Social media analysis helps with detecting a great deal of dangerous financial operations by visualizing all accounts as in a social network and checking for suspicious transactions. In case of a one, a specific algorithm detects where money is vanishing from preventing one of the most popular deceits in the banking sphere.

Basic processes automatization
Machine learning helps not only with detecting maleficent user activity but automatizes everyday actions such as report preparation, mail work, accounting processes. This way, the system not only helps with saving our time but also with growing efficiency and labor intensity.

However, machine learning is not currently used in all the financial institutions as it cannot be left without people that control its performance and fix issues if any. Hopefully, machine learning system will reach 100% accuracy in detecting illegal activity one day and will be able to work on its own.

User identification
Machine learning is not used just for filling in reports or checking the history of users’ transactions. Modern technologies implementation result in the development of a new security level, thus, due to nowadays algorithms more and more fintech companies use the technique for biometric authentication of their customers.

This way, prior to creating an account, a customer takes a photo of their face using the front camera. While the picture is being loaded to the platform, the software analyzes the customer’s eyes and the map of facial veins. Hackers can steal your credit card details, PIN number or the ID card but not your face or eyes, thus, the approach is beneficial for both the company and their clients.

What spheres anti-fraud system is used in

We’ve already discussed what anti-fraud systems are and how they work, so now is the time to define who uses these systems and what for.

Currently, fraud detecting systems are used in:

1. Banking sphere
Working with clients’ money means taking a lot of responsibility, that’s why banks need to detect fraudulent transactions in real time to prevent monetary loses. AI in fintech is important for identifying online frauds as it analyzes data used in banking systems, during transactions, etc.

2. Government institutions
AI greatly helps with preventing not only money but also data loss, predicting intrusions and determining tax fraud as well as with identification of abnormal behavior. All this helps our government to solve issues without getting sidetracked - faster and efficiently.

3. Medicine
Health care sphere works with both payments and personal data and spends millions per year to keep this information secure. By the way, machine learning is also used for making a correct diagnosis which greatly helps medical personnel.

4. Insurance
Anti-fraud system identifies suspicious activity, detects fraudulent schemes and patterns at an early stage of the claims process by using multiple techniques in real time. A developed scoring engine allows specialists to quickly confirm which claims are valid and which are not, and these actions are captured by the system and used for its further improvement.

So, what do we do with anti-fraud systems? Definitely, give them the green light! Such a system is a great asset to any company or institution that keeps confidential details, works with payments or on automatizing the workflow. Despite common misconception, the anti-fraud system with elements of machine learning can be applied not only in the IT sphere but is widely used in online banking, health care, by insurance companies, and even government institutions as these systems make our lives easier and perform tasks people spend a lot of time on. Sure, anti-fraud software especially without human supervision can’t run perfectly, however, taking into account the current speed of technological development, we are sure that in a few years AI and machine learning will be used everywhere for a variety of tasks. Let it be so!