A STUDY OF THE EFFECTIVENESS OF CLASSIFICATION ALGORITHMS USING DECISION TREES FOR ECONOMIC FORECASTING BASED ON A POSTERIORI DATA
The current, downturn state of the financial sector is still difficult. The key institutions, i.e., the Russian banks, are experiencing hard times. Risk growth and capital outflow, as well as the permanently changing funding conditions of the Central Bank, make lending institutions search for risk reducing methods, especially in consumer lending. Therefore, the problem is topical of improving the mechanisms of managing the credit portfolio of a commercial bank, using advanced methods and information systems. The customer credit scoring systems are being developed and implemented practically in all over the world. In Russia, there are also some instances of such systems that function successively; however, they cannot be named mainstream. The complication in designing and constructing scoring systems consists in the development of an algorithm, on one hand, and in selecting the indicators for this purpose, which would reflect the customer’s profile, on the other hand. Conventionally, scoring systems are constructed based on apriori data and on a pre-developed mathematical model. A special feature of our research is the fact, that we used for building the model the aposteriori data on the customer’s money transfers within the lending company, which transfers had not been related directly to the crediting process, i.e., we assessed the possibility to use the parameters of the existing sets for building scoring systems. To solve the problem stated, we used an algorithm based on decision trees, namely: Bagging and boosting. This paper presents a study performed regarding the efficiency of the existing algorithms. The performed comparative analysis of different implementations of bagging and boosting algorithms has shown that their forecasting ability is good enough and that the quality of the solution for the selection we use. According to our findings, the best algorithm is the gradient boosting algorithm implemented in the XGBoost library. As a result, the possibility has been proved of using the feature space characterizing the customer’s money transfers as a basis for constructing scoring systems using decision-tree-based classification algorithms.