Training Machine Learning Algorithms to Recognize Fraud Patterns
Machine learning is increasing in usage across several industries, from manufacturing and engineering to retail and marketing. While ML has many applications, the average person has yet to understand it fully.
In simple terms, ML allows software applications to continuously improve over time and better predict outcomes with little to no human intervention. A machine learning algorithm is a program the AI system uses to conduct a task. Their algorithms can often analyze historical datasets to predict valuable outcomes.
ML is the foundation of predictive analytics, which is a trend taking the business world by storm. However, they can also train machine learning algorithms for other purposes like fraud recognition.
Learn more about fraud detection, why it’s essential to use machine learning to detect fraud, industries that benefit from this practice, and some tips for data scientists training these ML algorithms.
The Growing Issue of Fraud
According to a press release, the Federal Trade Commission received around 2.8 million fraud reports from consumers last year. That’s an increase of 70% from the previous year in the United States alone.
Some of the most commonly reported types of fraud include:
- Imposter scams
- Online shopping scams
- Prizes and sweepstakes scams
- Lottery scams
- Internet services scams
- Fraudulent business and job opportunities
Other types of fraud are mortgage scams, pump and dump schemes, false insurance claims, corporate fraud, identity theft, and more. People lose billions of dollars yearly due to scams, fraud, and identity theft.
While no one wants to become a victim of fraud, it’s not surprising to hear how common it is. With cyberattacks and data breaches becoming more frequent and sophisticated, it’s easy for malicious actors to get their hands on sensitive and personal data.
The Importance of Training ML to Detect Fraud
Recognizing fraud is not a simple task, which is why more businesses are exploring the option of using ML for fraud detection. Some consider machine learning one of the best technologies to use for financial fraud detection.
ML for fraud detection works by analyzing massive amounts of historical data — namely consumer trends, behavior, and transaction methods. Machine learning algorithms can parse through data much more efficiently and accurately than a human analyst.
As a result, these systems can recognize any slight deviation from these learned patterns and alert users. Fraud detection using an ML algorithm works in real-time, monitoring transactions and flagging them for review if anything suspicious occurs. This tracking is especially crucial around the holidays and for seniors — two instances where fraud rates increase noticeably.
Many machine learning algorithms for fraud recognition are self-learning, including the Amazon Web Services Fraud Detection Using Machine Learning architecture. The self-learning capability allows the ML model to automatically adapt to new fraud patterns.
Fraud-detection systems are also highly cost-effective for companies. Organizations can cut costs by investing in an ML model and reduce or eliminate the need for human data analysts. The system can analyze data in milliseconds, so human workers are not overburdened with manually checking any new data inputs.
Industries That Benefit From ML Fraud Recognition or Detection
What are some of the primary industries that would benefit from using ML models for fraud recognition and detection? Examples may include:
- Financial services, fintech, and banking
- Health care
- Online gaming
This is by no means an exhaustive list. Still, it does provide a glimpse into what types of applications ML has for advanced fraud detection.
Tips for Data Science Professionals Training ML Algorithms for Fraud Detection
Suppose you’re a data scientist or a professional working with information daily. In that case, you may find yourself working on a project that uses machine learning for fraud detection. In that case, it’s vital to know some best practices and tips to handle this type of ML project.
Here are some tips for data professionals who may train machine learning algorithms to recognize fraud patterns. Practitioners must:
- Build intelligent models to detect fraud and discern between fraudulent and legitimate transactions
- Understand imbalanced data issues and how they impact the training approach
- Follow the best software engineering practices
- Expect initial startup challenges and potential high upfront costs when implementing ML for fraud detection
- Define business rules for the model and leverage customer feedback on the legitimacy of specific transactions
The number of use cases for ML could increase, especially regarding how businesses can use it to prevent cybercrime, scams, and other fraudulent activities.
Avoid Future Fraud Using Trained ML Algorithms
Businesses worldwide have already used machine learning and other data-science practices to prevent financial fraud. ML is a promising, innovative, and invaluable tool more companies should consider using — especially if fraud is a significant challenge they face.
As ML evolves, it will expand its capabilities and better recognize fraud patterns. It will be interesting to see what industries use models for fraud detection and if it will reduce the number of reported fraud cases.
Learn more about Fraud Detection and Machine Learning at ODSC West 2022
At ODSC West 2022 coming up this November 1st-3rd, you can learn all about using machine learning and data science in cybersecurity. In the AI for Cybersecurity track, you’ll gain the skills needed to protect your algorithms and company from malicious attacks and other threats. Sign up for our newsletter to get updates about sessions, speakers, and more ODSC West 2022 updates.
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