Business organizations produce numerous transactions and these transactions or activities lead to accumulate huge volume of data. Data becomes meaningless if not properly used to take up business decisions. The application of analytics in order to take timely and more accurate decisions is significant.
Financial Analytics in specifically, is the process of identifying and interpreting the data in meaningful way. It encompasses the tools and techniques to take up significant financial decisions. The CFO’s of the organizations have understood the role of financial analytics and therefore have increased the investment into financial analytics so that sound financial decisions can be undertaken.
Some of the areas where financial analytics is gaining considerable importance are discussed below:-
- Risk analytics:-
Business organizations are now fully characterised by the presence of risk. Risk is the state of uncertainty and can not be minimized so easily. In order to predict the possible risk in any investment product, the use of financial analytics can be useful. Most of the organizations are now using risk analytics to assess the loss and to gauge the extent to which this loss can lead to financial turmoil.
- Cross selling: –
Banking institutions are widely using financial analytics to adopt cross selling of the products. It means selling products to new or existing customers in addition to existing products. In order to cross sell the products, the banks need to analyse the data about customers in areas such as nature of products bought, frequency of buying a product, previous purchase history, accounts details, etc. This will in turn enable the banks to profile the customers and pitch the personalized financial products.
- Predictive analytics: –
Predictive analytics enable the decision maker to make predictions based on the available data. Some of the key areas in finance where predictive analytics will work are, making predictions about what will be the stock prices, whether a customer will default the bank loan, whether a customer will buy a particular financial product, etc.
- Fraud analytics: –
Banking and financial institutions are prone to financial frauds such as credit card fraud, accounting fraud, fraudulent loan application, phishing, fraud payments etc. These frauds cause tremendously to all stakeholders of the organization. Therefore, machine learning based fraud detection measures are being employed to detect financial frauds. Analytics helps to detect the unusual patterns and keep the institutions to get notified about it. Algorithmic trading is emerging area in this arena, which involves complex mathematical formulas and thereby reduce the possibility of committing frauds. Analytical tools such as SVM and Neural Network are heavily used to detect the frauds.