Machine learning is improving the detection of financial fraud; it also makes the process easier and provides precise results.
FREMONT, CA: The fraud-related activities are increasing in the financial sector day by day. Due to increasing fraudulent activities, all the financial sectors are suffering damage and loss. Adoption of machine learning by the industry can help the system to reveal scams and deal with them.
1. Machine learning vs. rule-based systems in fraud detection
In recent years machine learning (ML) approach to fraud detection has received a lot of publicity. It has shifted industry interest from rule-based fraud detection systems to ML-based solutions. Let’s have a look at the difference between rule-based fraud detection systems and ML-based solutions.
• The rule-based approach:
By looking at on-surface and evident signals, fraudulent activities in finance can be detected. Generally, a significant transaction that happens in typical locations requires additional verification. Mainly rule-based systems involve using algorithms that perform several fraud detection scenarios, manually written by fraud analysts. Moreover, this system also uses legacy software that can hardly process the real-time data streams that are critical for the digital space.
• ML-based fraud detection:
There are certain hidden activities in user behavior that may not be clear by the above process. Machine learning makes algorithms that process large datasets with several variables and help find these hidden correlations between user behavior and the likelihood of fraudulent actions. The data processing done by ML is faster than a rule-based approach, and it also reduces the manual works.
2. Fraud scenarios and their detection
• Insurance claims analysis for fraud detection
Although insurance companies spend several days assessing a claim; still, the insurance sector is affected by scams. The most common issues include car insurance scams, property damage, and fake unemployment claims.
• Fake claims:
All the fake claims can be detected with the help of semantic analysis. It is a machine learning task that allows for analyzing both structured, table-type data, and unstructured texts. This feature enables detect falsified claims. Machine learning algorithms evaluate files written by insurance agents, police, and clients, searching for inconsistencies in provided evidence. The rule-based engines don’t catch the suspicious correlations in textual data, and fraud analysts can easily miss relevant evidence in annoying investigation files. That is why analyzing claims is the most promising spheres for machine learning applications.
• Duplicate claims and overstating repair cost:
If a company owns machine learning, then it becomes easier for them to detect duplicate claims inconsistencies in car repair costs with the help of advanced algorithms. Classifying data in repair claims solves the problem by uncovering hidden correlations in claim records or even behaviors of insurance agents, repair services, and clients.
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