The idea of “learning” is the direct contrast between AI and machine learning. For a computer to learn how to evaluate information as a person does, we may give it a lot of data using machine learning.
Machine learning is used in many aspects of daily life, such as spam email detection, image identification, and Netflix user product recommendations. Machine learning will ultimately be used to identify fraud, it seemed inevitable.
According to the Global Economic Crime and Fraud Survey 2022 by PwC, 46% of respondents said they had experienced fraud, corruption, or another sort of economic crime in the previous two years. It should be no surprise that the most prominent businesses incurred the most significant losses; 20% of worldwide annual sales of at least $10 billion reported at least one scam that cost them at least $50 million.
Deep learning is a subset of machine learning. Deep learning’s ability to create flexible models for specific applications is its main advantage (like fraud detection). Traditional machine learning would not have allowed us to develop customized models; we will discuss why this is important later.
Machine learning can be viewed as a segment of artificial intelligence that uses your prior data to provide risk factors for fraud detection. Once the rules have been established, certain user behaviors, such as dubious identity theft, logins, or fraudulent transactions, may be prohibited or authorized.
Machine learning training enables you to distinguish between prior occurrences of fraud and non-fraud to avoid false positives and improve the accuracy of your risk criteria. As the algorithms run more prolonged, the suggestions for the rules get more accurate.
The evaluation of fraud requires quick results! According to a poll, online customers are less likely to complete the checkout procedure the longer they stay in the store.
It just takes a few seconds for multiple analytics teams to compare the responses to hundreds of thousands of real-time queries using machine learning. Making decisions using continual research into a group of consumers’ behavior is known as machine learning. It constantly observes “normal” consumer behavior and, should it spot something unusual, will swiftly postpone or flag a payment for further examination.
Every internet business wants to increase the volume of transactions it handles. Expanding the rules library of a rules-only system becomes even more pressing as payments and client data volumes increase. On the other side, more data is better regarding machine learning in fraud detection.
Larger datasets provide more examples of both good and bad behavior, such as honest and dishonest consumers. Thus, machine learning algorithms perform better. This means the model may benefit from behavioral similarities and differences to detect fraud in future transactions more quickly.
If machine learning were used, hundreds of teams would review thousands of payments per second. While recruiting more staff would be expensive, the only expense related to machine learning is the cost of machine maintenance. Similarly, machine learning may commonly outperform humans in identifying weird patterns or undetected tendencies that a fraud analyst would overlook immediately.
Machine learning and artificial intelligence are frequently used interchangeably. Although the word “artificial intelligence” describes all types of ML, not all AIs fit under this definition. The goal of AI is to build robots that can mimic the human mind. A subfield of artificial intelligence called “machine learning” enables computers to learn from data without reprogramming.
It’s also important to remember that deep learning is a subset of machine learning. It uses algorithms and architecture that is inspired by the brain. Because robots can study large datasets far more quickly than people can, enormous volumes of data can be chopped and diced.
Traditional manual examinations are still preferred in some situations, notwithstanding their benefits.
- Lower controls: Blackbox machine learning engines in particular, are prone to undiscovered errors.
- False positives: The entire system will suffer if good behavior is incorrectly classified as fraud. In this regard, a poorly designed machine learning engine might cause a feedback loop where other false positives go undetected, lowering the accuracy of your discoveries in the future.
- No human understanding: It may be challenging to look beyond fundamental psychology when trying to understand why a user’s behavior appears suspect.
In AML (anti-money laundering) and the evaluation of essential transactions, human reviewers are frequently favored over automated techniques.
Not all solutions supported by this technology are created equal, even though the bulk of fraud prevention businesses usually boast their use of this technology. It’s important to underline the differences between these two machine learning types:
Blackbox machine learning is a sort of automated decision-making that is designed to operate in a “set and forget” way. It could be great for small businesses that do not have to dig into the technicalities of changing risk policies.
Whitebox machine learning will be used in the workshop to justify a proposed risk regulation. This gives fraud managers more leeway to improve their fraud prevention strategy and makes it easier to pinpoint high-risk areas.
Both approaches have benefits and drawbacks. Instead, we at SEON used a whitebox approach that offers risk managers more command over the engine and allows them to modify, test, and assess the results of each risk rule.
Fraud detection is crucial, especially if it can stop a scam before any damage is done. However, both planning and real-time fraudster identification and blocking are necessary for successful fraud protection. A fully integrated picture of transactional and behavioral data is required for this, as well as ML models that can foresee the risk of fraud months in advance.
The best comprehensive fraud protection solutions combine the ability to create and assess fraud prevention models based on AI and ML with first-rate machine learning consulting services that include gathering first-party behavioral data points from different channels (e.g., digital engagement, location, time, etc.).