Viktor Bondarenko, CEO of BSS-Security LLC, named several

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The new system allows to reduce the time of identification of existing fraudulent schemes, increase the speed of detection of new fraudulent methods, as well as identify potential intruders and prevent them from entering the company as both clients and employees. The solution is implemented on open source software Camunda, Python, ArangoDB, PostgreSQL.
scenarios for using AI in cambodia whatsapp number database anti-fraud systems that help specialists respond to target situations faster: "For example, intentional accidents. AI can analyze data from telematics devices and car systems to identify anomalies in the car's movement before the accident, which may indicate an intentional collision. Using machine learning algorithms to analyze driver behavior and identify unusual situations that may indicate suspicious accidents. Or fake losses. AI can be used to analyze images and documents related to losses to identify signs of forgery. Image processing algorithms can automatically check the integrity of photographs and documents to identify inconsistencies and document manipulations. It can also identify fake insurance claims. This uses data analytics and machine learning algorithms to analyze large volumes of insurance claims data to identify anomalies. AI analyzes text data from insurance claims to identify inconsistencies and suspicious phrases. Another option is “ghost insurance.” AI can analyze customer and transaction data to identify inconsistencies and anomalies that may indicate fictitious insurance policies. Data mining algorithms identify unusual patterns of behavior, such as an unusually high frequency of claims. Or collaborative fraud. AI analyzes the connections between different insurance claims and participants to identify patterns of collaborative fraud. Graph database algorithms can help find connections and patterns between different fraud participants.”

Valery Stepanov, Head of the Information Security Competence Center at T1 Integration LLC, believes that using open source components in the anti-fraud system is appropriate: "This allows for increased transparency and reliability of the system due to the ability to independently verify and modify the code, and also reduces the costs of developing and supporting the system."

"In the context of anti-fraud systems using AI, it is critical to ensure the security and reliability of components, including open libraries and frameworks. We must remember the existence of 0-day vulnerabilities, which are more often found in open-source products, because their source code is available to everyone," notes Viktor Bondarenko. "Therefore, we need to devote as much time as possible to finding and eliminating vulnerabilities. This can be achieved by actively monitoring vulnerabilities, updates, and following best practices for development security. In addition, companies must have a strategy for responding to possible vulnerabilities and action plans in the event of security issues being discovered."

The automated fraud risk assessment is based on machine learning methods. By analyzing loss information, the mathematical model allows us to identify hidden patterns and statistical dependencies in the data, a certain combination of which indicates signs of fraud. At the training stage, the Gini index value from 0.72 to 0.75 was achieved for various products.