Indian Journal of Science and Technology
DOI: 10.17485/ijst/2017/v10i39/119861
Year: -0001, Volume: 10, Issue: 39, Pages: 1-7
Original Article
Thitimanan Damrongsakmethee1,2* and Victor-Emil Neagoe2
1Department of Business Computer, Suratthani Rajabhat University, Thailand; [email protected] 2Department of Applied Electronics and Information Engineering, Polytechnic University of Bucharest, Romania; [email protected]
*Author For Correspondence
Thitimanan Damrongsakmethee
Department of Business Computer, Suratthani Rajabhat University, Thailand;
Department of Applied Electronics and Information Engineering, Polytechnic University of Bucharest, Romania; [email protected]
Data mining is the process of discovering patterns, corresponding to valuable information from the large data sets, involving methods at the intersection of machine learning, statistics and database systems. Evolving from the fields of pattern recognition and artificial intelligence, machine learning explores the study and construction of algorithms that can learn from sample inputs. Financial data analysis is used in many financial institutes for accurate analysis of consumer data to find defaulters, to reduce the manual errors involved, for fast and saving time processing, to reduce the mis judgements, to classify the customers directly and to reduce the loss of the financial institutions. We have analysed a lot of machine learning techniques for financial analysis, namely models of supervised classification (Artificial Neural Networks, Support Vector Machine, Decision Trees), those of prediction (Cox survival model, CART Decision Trees), and also modelsof clustering (K-means clustering).
Keywords: Artificial Neural Networks, Bankruptcy Prediction, Classification, Clustering, Credit Risk, Credit Scoring, Data Mining, Financial Analysis, Machine Learning, Risk Management
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