Organizations form the back bone of every country siphoning money out and into the system. Financial institutions are organizations that provide services including lending money to individual persons as well as to huge organizations. Therefore they are directly involved in the financial strength of a country. All types of organization whether product or service have to deal with several risks arising due to a variety of reasons. Financial institutions are prone to more risks and they cannot operate without taking risks. Risk causes a great deal of potential damage and inconvenience for the enterprise stakeholders (Wu and Birge 2016). Thus organizations need to use different strategies to manage or avoid risks. Therefore it becomes important for financial institutions to model risks using historical data in order to gain insight into the risk patterns, so that it falls under their acceptable thresholds. Financial institutions collect a large amount of data and this data is potentially underutilized. To determine the risk patterns more accurately this data should be integrated within a model. (Katal et al. 2013). Insurance companies regularly extract facts from text gathered by using text analytics to parse the mountains of text that result from the claims process, turn text into structured records, then add that data to the samples studied via data mining or statistical tools for risk, fraud, and actuarial analysis (Russom 2011). Business analytics is the process of applying data mining techniques to the huge volume of data collected by organizations and the output of business analytics in business intelligence. Business Intelligence is actionable knowledge that can be used in decision making so as to avoid risks or deal with them in a better manner. Thus business analytics has become mandatory for all organizations for dealing with risks. The proposed study is carried out to gain more insight into the risk analysis process, especially in the financial sector. The rest of the paper is organized as follows. Section 2 gives an overview of the types of risks involved in the financial sector. Section 3 reviews the state of art in using Information and Computing Technologies (ICT) and business analytics for risk analysis and management. Section 4 defines the problem to be studied and a multi-objective genetic algorithm for solving risk analysis as a multi-criteria decision problem. Section 5 explains the experiments carried out on bench mark data sets from the UCI machine learning repository to test the proposed algorithm, the results followed by discussion on the observations obtained from the experiments and lists the contributions of the study. Section 6 concludes with a summary and future research directions.