How Machine Learning Works in Fraud Detection

As Artificial Intelligence technology continues to evolve, the multitude of ways in which it can help us appears endless. From building brand recognition to customer service support to image generation, when used correctly, AI can be an extremely helpful tool.

Present and future legal regulations can help prevent AI from being used for nefarious reasons, and allow engineers and researchers to continue to develop machine learning that is used for good in both the public and private sectors.      

One of the ways machine learning can benefit society and individuals is through being. It can enhance business and personal cybersecurity, and help prevent wannabe fraudsters from stealing your finances, identity, and more.

Let’s take a look at the history of machine learning, the roles AI plays and techniques it uses in fraud detection, and the benefits of incorporating machine learning into cybersecurity protection.

What is machine learning? 

Machine learning is a type of AI that allows computers to learn from data and organically improve their performance without explicit programming. This sounds creepier than it is — machine learning doesn’t create autonomous, sentient computers, but it does help AI to become far more effective. 

Think of this AI as a savvy cyber detective that constantly sifts through endless data to uncover patterns and insights to imitate how humans learn. Just as the way we learn evolves as we grow, machine learning allows AI capabilities to grow, too.

Among other benefits, machine learning enables computers to:

• Make decisions
• Predict analytics
• Make recommendations
• Adapt
• Create fraud detection algorithms
• Develop fraud detection techniques
• Support cybersecurity measures

How machine learning works in fraud detection

Every click and swipe we make online leaves a digital footprint and leaves us vulnerable to cybercriminals looking for tactics to hack our most sensitive and confidential data. That’s why safeguarding our online transactions is crucial to cybersecurity. As cybercriminals become increasingly sophisticated, advanced AI tools are necessary to thwart their evil plans.    

Enter machine learning — the unsung hero of cybersecurity and a revolutionary ally in fraud detection. The roles machine learning plays in successful fraud detection cannot be overstated.

Machine learning allows the fraud detection systems that guard troves of personal and confidential data the ability to analyze data, recognize subtle anomalies, and flag suspicious patterns. 

It uses techniques to allow AI to learn from past fraudulent activity and adapts detection mechanisms to stay a step ahead of hackers and other cybercriminals. Here’s how it works.

Three major steps of machine learning

According to UC Berkeley, machine learning has three main components:

• A decision process that allows AI to guess what type of data you’re looking for based on previous data collection and algorithms.

• An error function that gauges the success of the AI guesses, and compares them to known or proven examples. This allows AI to “learn” from its mistakes and make more accurate guesses in the future.

• An optimization or updating process that builds its knowledge based on the results of the error function. AI then course corrects to fine-tune the algorithm, revise the decision process, and develop precision in its future guesses.

Machine learning techniques

Machine learning software uses several techniques to promote fraud detection. These techniques are extremely beneficial for large organizations such as national banks and vast healthcare systems. Let’s take a deeper look at some of them.

Data collection

As databases grow larger, fraud detection becomes more difficult. Machine learning software begins with data collection and specifically targets user behavior, transactions, and user profile information, among other data. This type of data collection isn’t the same used by marketers to track your online movements —the data collected here is protected.

The software looks for patterns in details such as transaction amounts, locations accessed, timestamps, and device information.

Detail extraction

After machine learning-based algorithms collect raw data, the patterns they discover in targeted details help AI distinguish between fraudulent and legitimate user activity and transactions. 

For example, a bank that uses machine learning fraud detection might see user alerts if a customer makes multiple transactions in multiple countries on the same day.

Multiple machine learning-based algorithms are often used simultaneously to discern which works best with accessed data sets.

Model training

These details are then used as historical data to train machine learning software. As the machine learning model is trained, it learns to identify and detect patterns associated with fraudulent activity. This helps it to categorize transactions and activity as either legitimate or fraudulent.   

Model evaluation

Machine learning models are evaluated with metrics like accuracy, precision, recall, and the F score (which measures a model’s accuracy based on binary classification systems) to measure how successful fraud detection is, and to minimize false positives and negatives flagged by machine learning software.

Model monitoring

After the evaluation process, the machine learning model monitors real-time transactions and user activity. Any transaction that triggers suspicion of fraud based on the learned patterns is then flagged for further investigation.

Feedback and updates

As new data becomes available and fraud techniques change, the machine learning model is updated and retrained periodically based on evolving fraud patterns. This process allows the model to adapt to new patterns, and update its process to maintain consistent accuracy.

The benefits of machine learning in fraud detection 

Machine learning in fraud detection reduces the need for constant human monitoring of databases, and gives both businesses and online users additional cybersecurity protections.

Other benefits of the roles machine learning plays in fraud detection include:

• Real-time detection: Fraudulent activity can be detected in real-time to minimize financial losses and mitigate risks.
• Cost-efficiency: Machine learning fraud detection can save businesses time and money and reduce overhead costs by requiring less human labor.
• Adaptability: Machine learning models adapt to new levels of sophistication in cybercriminal activity by constantly learning from new data and determining how to combat new fraud threats.
• Reduces false alarms: If you’ve ever taken a road trip and used your debit card in multiple locations, you may have received a call from your bank’s fraud department. The latest machine learning technology recognizes subtle patterns and reduces false alarms, which allows businesses to respond to genuine threats of fraud.
• Automation, accuracy, and speed: Machine learning models allow fraud detection systems to simultaneously analyze billions of transactions. This allows organizations to quickly respond to real fraud threats.
• Effectively used to detect multiple types of fraud: Machine learning models can detect credit card and banking fraud, phishing attacks, fraudulent malware, and various types of identity theft.

The challenges of machine learning in fraud detection

Although machine learning software has a plethora of benefits, AI technology isn’t perfect (yet). Thus, there are challenges you need to be aware of as well. Some of the challenges of machine learning in fraud detection include:

• Cybercriminal attacks: Machine learning can greatly reduce the risk of fraudulent cyberattacks. However, cybercriminals may learn to intentionally manipulate data and data input to deceive models.
• Data quality: Machine learning models rely on data input. Corrupt, biased, or incomplete datasets can reduce the accuracy of fraud detection algorithms.
• Algorithm diversity: The fraud detection algorithms developed by machine learning models are not one size fits all. Diversity in data set combinations, business variables, and learning techniques are needed to ensure fraud detection works.

Machine learning and cybersecurity  

The beneficial use of machine learning isn’t limited to fraud detection. From malware and network hack attack detection to user behavior analytics and threat intelligence, machine learning is crucial to digital defense against cyber threats.

Machine learning is a powerful cybersecurity tool that plays pivotal roles in and utilizes excellent techniques for fraud detection. This AI technology can sift through enormous amounts of data, adapt to threat evolution, and detect suspicious activity in real time to protect sensitive and confidential data. 

However, it’s vital to address machine learning challenges to maximize its effective cybersecurity protection. Maximizing the fraud detection potential of machine learning can help build a safer digital world for all of us.

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