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APPLYING MACHINE LEARNING IN THE FINANCIAL SECTOR

Machine Learning is basically giving computers an ability to learn without being programmed. Some key applications include the following: Web Page Ranking: Submitting a query to a search engine returns the most relevant answers that are sorted in the order of their relevance. Facial Recognition based on an input image used in security related applications. Classifying customers based on some criterion e.g. customers who are in need of financial products like insurance. This is done from a base universe consisting of all types of customers. Speech Recognition and handwriting recognition. Credit scoring systems used in financial applications. The techniques of Machine Learning include Regression analysis, clustering, Decision trees, Neural Networks , Support Vector Machines (SVM ) etc. In supervised learning, the output datasets are provided which are used to train the machine and get the desired outputs whereas in unsupervised learning no datasets are provided, instead the data is clustered into different classes .

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