The Role of Machine Learning in Fraud Detection

Fraud has been a thorny issue in the digital space, and taming fraudsters has proved to be an overwhelming task because they rely on technology to advance attacks. In the e-commerce sector only, for instance, global fraud losses in 2022 amounted to $41 billion and are estimated to rise to $48 billion in 2023. However, all is not lost because fraud-mitigating approaches like Machine Learning have helped significantly minimize fraud activities by detecting them before execution. 

If you own a business, you wouldn’t want your customers to lose their money while transacting on your ecommerce platform or online business. Your customers expect you to facilitate safe transactions; otherwise, they will decamp at the competitor’s territory. 

One of the surest ways to keep fraud at bay is by embracing ML in your operations, whether in the ecommerce, health, finance, supply chain, or real estate sectors. ML even works better when your business runs on a blockchain because it offers multiple benefits like speed, safer transactions, and transparency which are essential in outshining fraudsters. 

Feel free to contact blockchain consulting services for more information on how ML and blockchain can apply in your area of specialization. Ecommerce and iGaming sites are some of the use cases of ML where the systems have pinpointed suspicious transactions, preventing hackers from stealing consumers’ identity or money laundering. So, how does ML help mitigate or detect fraud, and what are the benefits? First things first, let’s define Machine Learning.

What is Machine Learning, and How Does it Help Combat Fraud?

Machine Learning involves running a set of computer algorithms through historical data to pick known and unknown data patterns over time. The more data the algorithm has to study, the more it will accurately give true positives, implying that you should have data worth learning. In its role in fraud detection, ML assesses risks and helps to determine the possibility of a transaction being a fraud. 

After the assessment, the system can identify and stop suspicious activities, such as identity theft and chargebacks. ML has thrived where traditional fraud detection methods have failed. In saying so, institutions and businesses can detect fraudulent transactions that could have gone unnoticed by manual means. Another way that proves traditional fraud detection methods are unreliable is the false flagging of genuine transactions, which doesn’t sit well with affected customers. ML has been vital in detecting scrupulous accounts and transactions, allowing business owners to be one step ahead of hackers and other online threats. 

How Does ML Achieve Fraud Detection?

What steps are taken in implementing fraud detection via ML? The below guidelines offer a clear picture of what the process looks like:

• Data Extraction

There is no ML without data. The data can be transactional or customer details going from billing data and credit card types to the types of devices and IP addresses. With a clearly defined objective of what the ML aims to achieve, it will be easy to pick the data type to work with. Extracted data should be correct and not distorted; otherwise, the ML may produce inaccurate predictions at the expense of the business. 

• Generating and Activating Risk Rules

ML aims to produce true positives, depending on the risk rules or parameters that distinguish genuine records from fraudulent ones. An example of a risk rule is blocking users who log in from different devices. Another example of a risk rule is preventing payments to unauthorized vendors. Activating such rules and more can help eliminate instances where fraudsters go undetected and genuine users end up blocked. Remember that data accuracy is critical for true positives to be achieved because no real customer would like to be flagged for fraud-related reasons. 

• Algorithm Training

After activating risk rules, training the algorithm from data points to uncover consistent and inconsistent consumer behavior for some time follows. The program is taught to pick anomalies in historical and new data and either decline, review, or approve transactions or other user requests. At this stage, a detection frequency and a fraud scoring mechanism are also developed. Algorithm training is a continuous process for businesses that receive data daily from different data points.

• Evaluate and Test the Algorithm

Here, you seek to determine if the ML algorithm has learned the data sets accurately and implemented the proposed risk rules and other logic as expected.

What are the Advantages of Machine Learning in Fraud Detection?

Given the technology’s capability to process big data within short timeframes, there are several benefits to relying on ML to detect fraud. 

• Cost-effectiveness: Reviewing extensive data manually would require hiring adequate labor to foresee a process that would take seconds with ML. Machine learning is a one-time investment that serves you in the long term, saving you time and money.

• Accuracy: Traditional fraud detection methods are monotonous, tedious, and error-prone, triggering false positives. ML, on the other hand, is a sure bet on producing accurate distinctions between genuine users and fraudsters. Where large data sets are intimidating to humans, that’s where ML works best.

• Ability to Dissect Large Datasets: ML overperforms with extensive data. You can count on the technology to yield correct results no matter the data points involved. The same cannot be said about manual methods, which could take months to deliver tangible results.

• Speed: Speed is everything in preventing a potential fraud from materializing. ML quickly detects system anomalies and stops them before irreversible crimes are committed.

Bottom Line

The upsides of incorporating ML into your business must be addressed because, besides preventing you from suffering losses, the technology adaptation can also uphold your brand’s reputation. There is also a need to accept that online criminals are always looking for ways to advance their attacks, and failure to deploy effective strategies to stop them can bring your business to its knees. 

The bottom line is that if you have customer data that is a candidate for manipulation, you can create a Machine Learning model that offers accurate fraud probes, leaving no room for fraudsters to access your system for their gain. You must work with ML industry experts for personalized services to achieve long-term fraud detection goals. Experts are also better placed to advise you between choosing a white box and black box machine learning depending on the model that suits your business.