Machine Learning & Rule-Based Systems in Fraud Detection
The approach of Machine Learning (ML) to fraud detection has gained widespread publicity in recent years. It has shifted the business’s interest from Rule-Based systems to ML-based solutions. Why is it so? What is the difference between these two solutions? Find the answers in our infographics below.
Rule-based Fraud Detection
- Requires many manual resources to recount all possible detection rules
- Reactive – long-term process (tells you what have happened)
- Subjective – strong dependence on the expertise of anti-fraud analysts
- Detect already known fraud
- Harmfully influence the User Experience (due to a big number of verification measures)
- Operate on a small data set – less accuracy at scale
ML-based Fraud Detection
- Automatic detection and prevention of fraudulent scenarios
- Proactive – real-time process (tells you what is happening)
- Objective – detection of dependencies that are not obvious to a human; quick analysis of huge amounts of data
- Detect hidden and unknown fraud
- Minimized impact on User Experience (due to the reduced number of verification steps)
- Scalable – produce higher accuracy through automation
In fact, most organizations currently still leverage rule-based systems as their primary tool against fraud. However, from the above infographics, we can assume 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 cannot resist the actions of fraudsters who are becoming more sophisticated within time. Only ML-based systems can handle this.
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By implementing a range of fraud detection schemes, businesses can provide the necessary protection to their systems and customers; as well as avoid significant time and cost losses.
Systems and individual modules based on ML algorithms are one of the FreySoft specialties. As a full-cycle development company, the FreySoft team helps organizations to build and maintain a modern fraud and financial crimes management infrastructure, covering every stage of the work: from analysis to documentation.