We offer data mining solutions in order to detect any risk factors or fraud.
Detection of fraud
The fraudulent claims are one of the biggest problems of the insurance companies and their detection can bring a great benefit to companies.
DatKnoSys help you detect such small claims using association techniques, segmentation and predictive, with which we are looking for anomalies that obey a particular pattern to analyze them in greater depth to detect possible fraud.
DatKnoSys estimates risk on different financial products from a client, including the diversification effects. Using predictive techniques we approach the benefit or a particular financial instrument price, and our capital using optimization algorithms focus on activities that maximize profit or minimize risk as appropriate. Depending on the requirements we seek, we will you portfolio recommendations best suited to their interests.
Estimate claims costs
DatKnoSys helps companies to estimate claim cost in order to improve their forecasts and avoid the disadvantages caused by the prolongation of agreements and the final magnitude that can reach such claims. This estimation depends on factors such as claim importance, time to reach an agreement, inflation and interest, etc..
Money reserved as a provision for this type of event is immobilized until the end of the claims process, which is important to note these costs in advance. Using techniques of data mining created a predictive model based on past claims resolution that gives us an estimate of the final amount of the claim, the time to resolution of this and the amount which should be earmarked for funding.
Customers and Financial market risk analysis of
DatKnoSys calculates the risk of different loans using data mining techniques to determine the risk associated with a credit based on the client history, its socio demographic features, etc. For this purpose we use predictive algorithms to classify clients in a particular risk category, and from this assignment, we know how high is the risk of non-payment of the credit or if it requires further study before being granted.
We calculate the financial market risk through data mining techniques that allow us to develop models and we measure risk of various financial instruments based on indicators: interest rates, stock indices or economic development. Using models for prediction or classification we assign a particular risk to a particular product and thus facilitate decision-making.
DatKnoSys identifies risk factors to establish the price of a premium, thus predicting potential claims and losses. These factors, which is obvious in some cases – a person who lives in the city has more risk of a traffic accident that one alive in the field, for example, other times are not so and there may be relationships between variables that are difficult or impossible to identify without advanced techniques.
We predict the risk with a model of data mining much more precisely, allowing to better adjust the premium prices and consequently decrease costs and increase profits. To build these predictive models, customers are segmented into homogeneous groups according to the end that we want to achieve: identify risk factors, behavior, benefit… Studying these segments we will obtain information on the characteristics of the groups that will help us to determine the risks or behavior of its components. At the same time, these segments apply predictive models that help us to obtain more information about the behavior of each group.