Combining expert knowledge and databases for risk management

Correctness, transparency and effectiveness are the principalattributes of knowledge derived from databases. In current data miningresearch there is a focus on efficiency improvement of algorithms forknowledge discovery. However important limitations of data mining canonly be dissolved by the integration of knowledge of experts in thefield, encoded in some accessible way, with knowledge derived formpatterns in the database. In this paper we will in particular discussmethods for combining expert knowledge and knowledge derived fromtransaction databases.The framework proposed is applicable to widevariety of risk management problems. We will illustrate the method ina case study on fraud discovery in an insurance company.risk management;datamining;knowledge discovery;knowledge based systems

Combining expert knowledge and databases for risk management

Correctness, transparency and effectiveness are the principal attributes of knowledge derived from databases. In current data mining research there is a focus on efficiency improvement of algorithms for knowledge discovery. However important limitations of data mining can only be dissolved by the integration of knowledge of experts in the field, encoded in some accessible way, with knowledge derived form patterns in the database. In this paper we will in particular discuss methods for combining expert knowledge and knowledge derived from transaction databases.The framework proposed is applicable to wide variety of risk management problems. We will illustrate the method in a case study on fraud discovery in an insurance company

Integrating Economic Knowledge in Data Mining Algorithms

The assessment of knowledge derived from databases depends on many factors. Decision makers often need to convince others about the correctness and effectiveness of knowledge induced from data.The current data mining techniques do not contribute much to this process of persuasion.Part of this limitation can be removed by integrating knowledge from experts in the field, encoded in some accessible way, with knowledge derived form patterns stored in the database.In this paper we will in particular discuss methods for implementing monotonicity constraints in economic decision problems.This prior knowledge is combined with data mining algorithms based on decision trees and neural networks.The method is illustrated in a hedonic price model.knowledge;neural network;data mining;decision trees

Risk Management Based on Expert Rules and Data Mining: A Case Study in Insurance

Correctness, transparency and effectiveness are the principal attributes of knowledge derived from databases using data mining. In the current data mining research there is a focus on efficiency improvement of algorithms for knowledge discovery. However, improving the algorithms is often not sufficient. The limitations of data mining can only be dissolved by the integration of knowledge of experts in the field, encoded in some accessible way, with knowledge derived from patterns in the databases. In this paper we discuss an approach for combining expert knowledge and knowledge derived from transactional databases. The approach proposed is applicable to a wide variety of risk management problems. We illustrate the approach with a case study on fraud detection in an insurance company. The case clearly shows that the combination of expert knowledge with monotomic neural networks leads to significant performance improvements

Derivation of Monotone Decision Models from Non-Monotone Data

The objective of data mining is the extraction of knowledge from databases. In practice, one often encounters difficulties with models that are constructed purely by search, without incorporation of knowledge about the domain of application.In economic decision making such as credit loan approval or risk analysis, one often requires models that are monotone with respect to the decision variables involved.If the model is obtained by a blind search through the data, it does mostly not have this property even if the underlying database is monotone.In this paper, we present methods to enforce monotonicity of decision models.We propose measures to express the degree of monotonicity of the data and an algorithm to make data sets monotone.In addition, it is shown that monotone decision trees derived from cleaned data perform better compared to trees derived from raw data.decision models;knowledge;decision theory;operational research;data mining

Generating artificial data with monotonicity constraints

The monotonicity constraint is a common side condition imposed on modeling problems as diverse as hedonic pricing, personnel selection and credit rating. Experience tells us that it is not trivial to generate artificial data for supervised learning problems when the monotonicity constraint holds. Two algorithms are presented in this paper for such learning problems. The first one can be used to generate random monotone data sets without an underlying model, and the second can be used to generate monotone decision tree models. If needed, noise can be added to the generated data. The second algorithm makes use of the first one. Both algorithms are illustrated with an example

Integrating Economic Knowledge in Data Mining Algorithms

The assessment of knowledge derived from databases depends on many factors. Decision makers often need to convince others about the correctness and effectiveness of knowledge induced from data.The current data mining techniques do not contribute much to this process of persuasion.Part of this limitation can be removed by integrating knowledge from experts in the field, encoded in some accessible way, with knowledge derived form patterns stored in the database.In this paper we will in particular discuss methods for implementing monotonicity constraints in economic decision problems.This prior knowledge is combined with data mining algorithms based on decision trees and neural networks.The method is illustrated in a hedonic price model.

Derivation of Monotone Decision Models from Non-Monotone Data

The objective of data mining is the extraction of knowledge from databases. In practice, one often encounters difficulties with models that are constructed purely by search, without incorporation of knowledge about the domain of application.In economic decision making such as credit loan approval or risk analysis, one often requires models that are monotone with respect to the decision variables involved.If the model is obtained by a blind search through the data, it does mostly not have this property even if the underlying database is monotone.In this paper, we present methods to enforce monotonicity of decision models.We propose measures to express the degree of monotonicity of the data and an algorithm to make data sets monotone.In addition, it is shown that monotone decision trees derived from cleaned data perform better compared to trees derived from raw data.