Vol: 9, Issue: 2 2019

Page: 32-38

An Analysis of the Supervised and Unsupervised Machine Learning in Enhancing the Efficacy of Financial Analysis

Himanshu Dahiya

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 misjudgments, to classify the customers directly, and to reduce the loss of the financial institutions. We have analyzed a lot of machine learning techniques for financial analysis, namely models of supervised classification (Artificial Neural Networks, SupportVector Machine, Decision Trees), those of prediction (Cox survival model, CART Decision Trees), and also models of clustering(K-means clustering).