Significanceof Business Analytics in Financial Institutions
Everyyear, several banks and credit institutions lose millions of dollarsto fraudulent clients. The majority of the customers apply for loans,but later default and use bankruptcy protection. However, some firmscontinuously record huge profits because they only lend tocreditworthy individuals. According to Bekmamedova and Shanks (2012),monetary organizations can improve their performance by usingbusiness analytics to make decisions based on facts. The approachfacilitates identification of reliable clients, appropriate pricingof loans, and popular packages that would sell quickly among thetarget customers.
Sullivanand Feinn (2012) provide that the sample sizes do affect statisticaloutcomes and tests. In many cases, it is hard to discover thedifferences between dissimilar people. However, using a large samplesize enhances the probability of researchers noting patterns amongthe subjects being analyzed. For instance, banks can hardly tell theeconomic capacity of individuals with high salaries. However, theapplication of business analytics can help an institution to discovercreditworthy clients through observing the long-term fiscal stabilityof similar people and the way they use their resources. Nevertheless,an investigator should identify an appropriate sample size, which isjust enough to give accurate results, to avoid causing burdens to theparticipants or including unnecessary stakeholders.
Itis critical to plan a sample size before embarking on data collectionbecause it facilitates confidence in the results acquired. Theobjective of the statistical analysis is determining whether thedifferences between two variables emanates from chance. Moreover,strategizing a sample size in advance is significant since when thepower is determined at the end of the evaluation, the accuracy levelof the outcome is very low because of erroneous assumptions. Planningthe sample size is also crucial because it prevents unnecessarywastage of resources. Big sample sizes may cost a lot of time andmoney. On the contrary, small studies could waste funds as they maygive misleading outcome (Arifin, 2013).
Arifin,W. (2013). Introduction to sample size calculation. Education inMedicine Journal, 5(2), 89-97. DOI: 10.5959/eimj.v5i2.130
Bekmamedova,N. & Shanks, G. (2012). Achieving benefits with businessanalytics systems: An evolutionary process perspective. Journalof Decision System, 21(3).DOI: 10.1080/12460125.2012.729182
Sullivan,G.M., & Feinn, R. (2012). Using Effect size—or why the Pvalue is not enough. Journalof Graduate Medical Education,4(3),279–282. http://doi.org/10.4300/JGME-D-12-00156.1