How Can Machine Learning Help in Fraud Detection?
Today business is faced with a growing sophisticated enemy who attacks, responds, and changes tactics very quickly. With machine learning (ML), companies can detect, prevent and keep staying ahead of fraud in the long-term. Got interested in how it works? Then keep reading the article.
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- Fraud statistics.
- The variations of fraud.
- ML-based and rule-based systems. The comparison.
- Implement an effective ML fraud detection system.
- Apply for the fraud detection solution.
Let’s start with getting to know a couple of recent statistics around fraud to have a general view of the situation.
According to Statista, a 2020 worldwide survey of fraud examiners revealed significant increases in different types of fraud risks. The growth is from 24% in May 2020 to 31% in August 2020. Additionally, it is expected to reach about 47% over the next 12 months.
According to Global Payment Fraud Statistics, Trends & Forecasts by Merchant Savyy, Global losses from payment fraud have tripled from $9.84 billion in 2011 to $32.39 in 2020. Moreover, payment fraud is expected to continue increasing and projected to cost $40.62 billion in 2027. That is up to 25% higher than in 2020.
According to the Federal trade commission, American consumers reported losing more than $3.3 billion to fraud in 2020, up from $1.8 billion in 2019. Internet and mobile services; prizes, sweepstakes, and lotteries rounded out the top five fraud categories.
The variations of fraud
Fraud is constantly evolving and adapting to the changing conditions of social and economic life. The fraud types are acquiring new forms. They include phishing, identity theft, deliberate information leaks, payment fraud (credit card and bank loan), forgery of ID documents, fake accounts, etc.
The most popular fraud through the channels of its implementation are the next ones:
- financial organization’s branches – illegal execution of expense transactions on the account, fraud with crediting compensations, payments, refunds, temporary borrowing of funds, illegal transactions with “sleeping” accounts, cancellation
- payment cards – skimming (compromise of a card in payment terminals and ATM), CNP-fraud (Card Not Present, compromise of card data when making purchases on the Internet); identity theft (the illegal use of someone else’s personal data for profit).
- phishing – misleading a client to carry out debit transactions;
- remote financial services – compromising a channel, changing customer information, unauthorized transfers, changing the recipient’s details in a payment order, etc. Recently, there has been a surge in interest in crimes of this kind. It is associated with an increase in the number of remote services that do not require the user’s personal presence, such as paying for purchases in online stores using bank cards or payment systems.
Deliberate information leaks
One more fraud that businesses often face is deliberate information leaks. They carried out by insiders or external attackers. Insiders have access to important information within the organization, so a planned leak of this kind can be made by the employee for personal gain or for other interests. External attackers take advantage of the absence or poor quality of the company’s security system. In this case, they can use the following methods:
Hacking programs. By hacking a program, a criminal can inject malicious codes and facilitate information leakage.
Backdoors. An attacker gets remote access to a system and can use the data at will, in particular, to copy, forward, etc.
Trojan horses. Having penetrated the computer under the guise of a regular application, they collect various information, including the company’s or customers’ data.
In addition, deliberate information leakage can occur due to the simple theft of a device or media from a person. Data can be stored both on a laptop or smartphone and on removable drives.
Know the difference: ML-based and rule-based systems
In fact, most organizations currently still leverage the rule-based systems as their primary tool against fraud. However, the rules reveal known patterns, and they are completely ineffective when it comes to new and yet unknown schemes. Rule-based systems do not adapt to new conditions and are unable to resist the actions of fraudsters who are becoming more sophisticated within time. Only ML-based systems can handle this.
Implement an effective ML fraud detection system
Prevention of fraudulent activities is one of the most promising and developed areas of using artificial intelligence in financial organizations and industry-specific businesses with an online presence. To implement an effective ML system, here there are a number of factors you should take into account. Let’s look at the major ones.
When it comes to creating ML systems, the key to success is data. The more data, the better the model. And the fraud detection model is no exception. While raw data is important, best-in-class models use adaptive technologies that continually learn from any additional inputs so you can adjust your decisions based on current conditions. Since information grows and complexity changes, security professionals need scalable ML platforms.
Benefits of diversity
One should remember there is no single ML algorithm or method that will work 100% in all cases. We recommend trying out many separate algorithms and their combinations, as well as test them on different datasets. Data scientists require a wide variety of supervised and unsupervised learning methods, as well as various feature engineering technologies.
Supervised (controlled) models are based on marking data as fraudulent or non-fraudulent so that the computer can identify legal or illegal models. Unsupervised models use a form of self-learning by grouping data points together to fill in gaps when there is little or no tagged data.
Finally, the creative aspect of using ML to detect fraud should not be overlooked. We are talking about new and unusual ways of applying ML. For example, combining methods can be much more effective than using separate methods within the same system.
Behavioral analytics uses ML to anticipate the behavior of each account holder (user), such as transaction patterns. It uses advanced profiling, fraud prediction characteristics, and adaptive capabilities to differentiate itself from generic models. Based on this, the machine is able to detect anomalies in behavior. In the case of uncharacteristic spending, the algorithms may assume that this is illegal (fraudulent) behavior.
Since the models and the underlying data change, the quality of the input data degrades. As a result, the performance of the system as a whole also decreases. This problem is especially inherent not only in ML systems but also in rule-based systems.
That is, continuous monitoring of ML fraud detection systems is the indisputable key to excellent long-term performance. New ML methods are able to effectively adapt to new, as yet unknown patterns. This allows you to reduce the number of necessary activities for retraining and evaluating the operation of the ML system.
An effective monitoring system actively examines the data that enters the system, evaluates the predictions and explanations generated by the ML model. Moreover, it also alerts administrators to changing trends in data and statistics before radical changes affect the operation of the entire company.
Apply for the fraud detection solution
ML is critical to effectively detect and prevent fraud in businesses concerning credit cards, accounting, insurance, and more. Early detection of fraud is essential to keep customers and employees safe. The sooner a company detects fraud, the sooner it can restrict access to the business account to minimize losses. By implementing a range of fraud detection schemes, businesses can provide the necessary protection and avoid significant losses.
Systems and individual modules based on ML algorithms are one of the FreySoft specialties. As a full-cycle development company, the FreySoft team provides work on every stage of the project: from analysis to documentation.