"The specificity of anti-fraud in car insurance
Posted: Wed Jan 22, 2025 4:58 am
Viktor Bondarenko believes that this indicator is very good: "The Gini index in fraud detection systems is usually calculated during the model training process. It represents the degree of inequality in the distribution of fraudulent and non-fraudulent cases across different segments or nodes of the model. A higher Gini index indicates better discrimination between fraudulent and legitimate cases, with values close to one representing a strong distinction and values close to zero indicating a weak distinction. A Gini index in the range of 0.72 to 0.75 is usually considered very good for fraud detection systems. In the context of binary classification, such as fraud detection, where the Gini index is commonly used, values close to 1 indicate better discrimination. It is also worth noting that the model exhibits strong discrimination between fraudulent and non-fraudulent cases, meaning that the model effectively separates the two classes and makes predictions that match the real-world labels well."
Lev Afanasyev, Head of the cambodia whatsapp number database Innostage Anti-Fraud Solutions Group, reminds that any advanced anti-fraud system is a whole complex of different technologies that work in conjunction with each other in a high transaction activity mode: "The choice of specific technologies and components is dictated primarily by reliability and performance, and for some areas, open source solutions have become a de facto industry standard. The choice of such technologies is a completely justified step. They have proven themselves well, have a wide community with development and maintenance competencies, a low probability of deliberate damage to the source code or termination of support for the development of the project. At the same time, they provide the ability to choose a technological platform for development and deployment, do not have mandatory license payments, and often require fewer hardware resources. From the point of view of the security of open source software when used to build anti-fraud systems, this issue is slightly less critical in this case, since usually the main components of such systems are located inside a secure circuit, which reduces the likelihood of exploitation of possible vulnerabilities."
is that it is aimed at identifying and preventing fraud in order to receive undeserved payments for insurance cases. Anti-fraud in this area may include the use of machine learning algorithms to analyze insurance case data, check the history of the driver, car and other factors to identify potential fraudsters. At the same time, the economic effect of the implementation of anti-fraud systems can be significant, since this allows you to reduce the cost of payments for fraudulent cases and reduce losses. The payback of such systems depends on the scale and specifics of use, but they usually pay off within a few years due to the reduction in losses from fraud," says Valery Stepanov.
"This is an ambitious and large-scale project on the Russian insurance market, which we were able to implement exclusively on open-source software. The new system allows the customer to reduce the time for identifying complex fraudulent schemes from several days to several hours, as well as to promptly identify hidden fraudulent schemes. This makes the business more efficient and stable," Evgeny Chernoburov, head of the insurance practice at GlowByte, assessed the significance of the project.
"The new data analysis system now allows us to see hidden connections and patterns that previously went unnoticed. This makes it easier for Ingosstrakh to identify suspicious activities and prevent financial losses. The system opens up new prospects and opportunities for the effective use of graph analytics in various areas of professional activity," said Ivan Kotlyarovsky, Project Manager for Ingosstrakh's Retail Business Loss Settlement Department.
"In general, anti-fraud systems using AI in the field of information security have the potential for significant economic benefits and provide protection against financial losses and reputational risks associated with fraud," sums up Viktor Bondarenko.
Lev Afanasyev, Head of the cambodia whatsapp number database Innostage Anti-Fraud Solutions Group, reminds that any advanced anti-fraud system is a whole complex of different technologies that work in conjunction with each other in a high transaction activity mode: "The choice of specific technologies and components is dictated primarily by reliability and performance, and for some areas, open source solutions have become a de facto industry standard. The choice of such technologies is a completely justified step. They have proven themselves well, have a wide community with development and maintenance competencies, a low probability of deliberate damage to the source code or termination of support for the development of the project. At the same time, they provide the ability to choose a technological platform for development and deployment, do not have mandatory license payments, and often require fewer hardware resources. From the point of view of the security of open source software when used to build anti-fraud systems, this issue is slightly less critical in this case, since usually the main components of such systems are located inside a secure circuit, which reduces the likelihood of exploitation of possible vulnerabilities."
is that it is aimed at identifying and preventing fraud in order to receive undeserved payments for insurance cases. Anti-fraud in this area may include the use of machine learning algorithms to analyze insurance case data, check the history of the driver, car and other factors to identify potential fraudsters. At the same time, the economic effect of the implementation of anti-fraud systems can be significant, since this allows you to reduce the cost of payments for fraudulent cases and reduce losses. The payback of such systems depends on the scale and specifics of use, but they usually pay off within a few years due to the reduction in losses from fraud," says Valery Stepanov.
"This is an ambitious and large-scale project on the Russian insurance market, which we were able to implement exclusively on open-source software. The new system allows the customer to reduce the time for identifying complex fraudulent schemes from several days to several hours, as well as to promptly identify hidden fraudulent schemes. This makes the business more efficient and stable," Evgeny Chernoburov, head of the insurance practice at GlowByte, assessed the significance of the project.
"The new data analysis system now allows us to see hidden connections and patterns that previously went unnoticed. This makes it easier for Ingosstrakh to identify suspicious activities and prevent financial losses. The system opens up new prospects and opportunities for the effective use of graph analytics in various areas of professional activity," said Ivan Kotlyarovsky, Project Manager for Ingosstrakh's Retail Business Loss Settlement Department.
"In general, anti-fraud systems using AI in the field of information security have the potential for significant economic benefits and provide protection against financial losses and reputational risks associated with fraud," sums up Viktor Bondarenko.