How Machine Learning Helps Fraud Detection

In the modern century, with the acceleration of technological developments, people's daily routines are largely digitalized. The simplest example of this is online shopping and banking transactions. Even with a mobile phone, it is very easy to pay bills and shopping, location independent and without leaving our comfort areas. The accessibility of digital resources, technological developments, and the new world order push both users and institutions to digitalization. Especially, the easy application of payment flows with the digital tools creates new opportunities for fraudsters. Credit card transactions become the most common form of payment for both online and offline transactions, so the rate of credit card fraud is increasing day by day. In this context, fraud methods that we are frequently exposed to in daily life are also emerging in the digital environment and came into our lives as virtual fraud. Although there are many types of virtual frauds, many of them could be prevented with correct fraud detection and prevention methods.

What is Fraud Detection?

Quick actions to be taken to detect virtual fraud are very important in terms of preventing the losses of institutions and individuals and compensating for these losses. Fraud detection is becoming increasingly important in detecting and limiting revenue loss due to fraud. If this cannot be prevented, fraudsters cause service providers to incur financial damages by aiming to use the services offered without payment or to use the service in other ways. To prevent these damages, institutions get help from various fraud detection systems. Fraud detection is a set of processes and analyzes that allow institutions to identify and prevent unauthorized financial activities.  

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These frauds can include; 

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Companies can incorporate fraud detection into their websites, company policies, employee training, and advanced security systems.

Companies create specific sets of rules to prevent fraud and include them in their software algorithms.

Many fraudsters commit fraud by testing these sets of rules and exploiting loopholes.

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How can you benefit from fraud detection using machine learning?

Today, fraud analysts spend a lot of time checking and detecting fraud streams. At this point, the speed of detecting frauds is directly proportional to the time spent by a person. However, fraudsters can perform millions of fraud transactions within seconds with advanced technology devices. The progress of fraud detection in proportion to a human resource causes the processes to not be detected in a compensable time and therefore the lost money cannot be recovered as a result of money laundering. In this context, the use of fraud detection systems is of great importance to reduce losses from fraud. Traditionally, businesses used only rules-based systems to prevent fraudulent payments. Today, rules are still an important part of fraud detection and prevention tools, but using them alone in the past has also caused some problems. As the data that organizations need to examine increased, the analysis of these data with appropriate methods, proper classification of the data, and fraud measures in parallel with tracking user movements revealed the necessity of machine learning systems. Fraud detection using machine learning provides many benefits to businesses at this point.

Less False-Positive Case

The fact that the databases that companies keep on transactions are very large and continue to grow rapidly reveals the necessity of scalable machine learning systems. Deciding whether a transaction is real or suspicious at the point of detecting fraud creates a huge waste of time for analysts. False positives, which seem to be real but are not fraud, slow down the speed of detection of real fraud movements by causing a waste of time due to the workload. Fraud detection using machine learning models applied for this take fast actions at the point of detection and minimize the losses. Using a large number of rules tends to result in a high number of false positives. This means that many real customers are likely to be blocked by suspicion of fraud. For example, the orders which have high-value and from high-risk locations may be perceived as abnormal by the ruleset, the probability of fraud. This can be described as a fraud transaction, and when it turns out that there is no fraud at the end of the day, the time spent by fraud analysts to detect it can be considered a waste of time.

Less Employee Need

Machine learning involves the processes of computers learning how to perform tasks without being programmed, and making predictions and actions with the support of data provided to perform specific tasks. As the amount of data and experience increases, the results of machine learning become more accurate. At this point, it is very important to process the data obtained by institutions in fraud analysis by machine learning, to create the right decision-making algorithms with machine learning instead of the labour force. Fraud detection using machine learning helps to quickly detect and recover fraud actions and minimize the damages to institutions.

Faster Fraud Detection

False-positive actions are checked one by one by analysts and as a result, they are not considered as fraud, causing the late detection of possible real fraud actions and in parallel with this, the lost money cannot be recovered. Machine learning provides great support to analysts in detecting false positives. Machine learning, along with simple filtering mechanisms and algorithms, can create the right alert systems.

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Analysing Normal & Abnormal Activities

Machine learning-based abnormality detection algorithms accurately detect abnormal behaviours in different data models and apply filters to these abnormalities, allowing you to only receive alerts on issues that matter to you. One of the key benefits of machine learning is the capacity to process more data than an analyst could ever do, and then use the compiled data to identify patterns that are difficult for a human to define. Fraud detection using machine learning allows analysts to investigate previously undiscovered suspicious activity. The ability to retrain and setting models and automatically adjust parameters is a powerful tool for risk model management.

Machine Learning Insights, Actions, Suggestions for Fraud Detection

Using only rule-based fraud detection software means your rule library should continue to expand as fraud technologies evolve. This makes the system slower and causes a heavy maintenance necessity for fraud analysis teams, and requires an increasing number of manual reviews. But today, fraudsters use sophisticated methods to steal customer data and impersonate real customers, making it even more difficult for rules prepared for typical fraud to detect such behaviour. At this point, the importance of using machine learning technology in fraud detection software is once again revealed. In summary, machine learning can be described as a branch of science that enables learning based on data types. When it is used successfully, machine learning eliminates the heavy data analysis responsibility on fraud detection teams. The results assist the team in research, insight, and reporting. Machine learning cannot replace a team of fraud analysts but gives them a chance to reduce the time spent on manual reviews and data analysis. This means analysts can focus on the most significant and urgent cases and evaluate alerts more accurately, faster, and also reduce the number of real customers rejected.

To Prevent Your Company From Fraud

Machine learning makes the roles of fraud analysts more efficient because they can use their time to do more strategic work. Analysts improve and optimize machine learning fraud detection systems by reviewing and tagging customers and setting rules. The machines are extremely good at doing data analysis and numerical computing. At this point, machines can learn a lot from analysts, process billions of data, and fraud detection using machine learning plays a major role in preventing companies from serious losses.

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