APPLYING MACHINE LEARNING IN THE FINANCIAL SECTOR
Abstract
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 .
References
In [1] the authors have discussed about Automatic reply of incoming messages ,automatic organization of mail into folders. The summarization of e mails by extracting key sentences is also discussed here.
Speech recognition is described including customization in [2].
Recognizing handwriting for postal mail forwarding using machine learning is described in [3].
Information retrieval consisting of indexing, querying, comparision&feed back can be done efficiently using automated learning [4].
Anomaly detection e.g detection of unusual sequences of card transactions by looking at a sequence of operations using machine learning techniques is discussed in [5].
Stock market analysis is done using support vector machines(svm) & reinforcement learning in order to maximize profit of stock purchased, minimizing risk & use of sentiment analysis for this purpose is discussed in [6].
Sentiment analysis in product reviews can be used in business intelligence gathering and recommender systems[7].
The use of cluster analysis in organizing large computer systems for efficient data center management is discussed in [8].
Social network analysis including grouping of people & community identification is explained in [9].
The use of unsupervised learning can be done for doing market segmentation – for the purposes of customer differentiation [10].
Electronic medical records of patients can be analysed for understanding disease profiling in a community [11].
Understanding of genetic structures through unsupervised learning can be done for identifying common genetic sequences[12].
The organization of Documents through indexing using a controlled vocabulary can be done using machine learning[13].
The differentiation between genuine and spam mails can be done using a spam filter which uses machine learning [14].
Disease profiling in community health profiling is discussed in [15].
The use of machine learning techniques for studying financial markets is described in [16],17],[18], [19]. The use of pattern analysis for future prediction of stock options based on past patterns is done dynamically.
E commerce sites are using recommender systems to give users customized choices and summaries of goods he or she is searching for. Both content based and collaborative recommendation systems are being offered giving novel & intelligent options to a user[20].
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