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The purpose of this paper is to propose a noise identification method for data without temporal structure, in which application of typical mathematical white or colored noise models is very limited due to observation order requirements. The method is used to identify the destructive elements and to eliminate them what finally brings prediction improvement.

The paper concerns noise detection problem presented in the framework of ensemble methods via blind signals separation. The authors utilize the Extended Generalized Lambda Distribution (EGLD) model to compare the signals with the target.

The authors proposed novel signals similarity measure which is based on the EGLD system. The authors showed that it can be applied for data with or without time structure, as well as for data which are mutually uncorrelated. It turned out that method is effective for noise identification and can be an alternative, in many cases, to correlation approach, particularly for noise identification problems.

In this method the improvement of prediction results is associated with elimination of the real physical factors rather than mathematical averaging in terms of arbitrary assumed distributions. In this approach, it does not matter what is the structure of aggregated models, what significantly distinct this approach from such techniques as boosting or bagging, in which the aggregation process applies to the models of similar structure. For this reason the methodology is focussed on physical noises elimination from predictions and it is complementary to the other ensemble approaches.

The purpose of this paper is to present application of recency, frequency and monetary value (RFM) approach to predict customer insolvency using telecommunication data corresponding to RFM of late payments. The study tackles a serious problem that telecommunication companies often face and shows the ways to deal with it.

Based on a real telecom customer data, RFM approach was tested against decision trees and logistic regression models. Proposed models were evaluated with lift measure, area under the receiver operating characteristic and the ability to detect significant amount of money owed by insolvent customers.

The main findings from the research are twofold: RFM approach offers a viable alternative for customer insolvency classification. The proposed models perform well and all of them can capture significant amount of money owed by insolvent customers what is of high importance for the revenue assurance.

In comparison to previous studies proposed research presents novelty in the following areas. First, it deals with RFM applied to insolvency data (previous studies dealt with direct marketing data). Second, with these three variables it is possible to act as an early warning system for predicting the risk level and probable anomalies as quickly as it is possible (data retrieval and computational time is reduced). Third, RFM approach was tested against decision trees and logistic regression and the quality of the models was also assessed three months after the estimation.