Essays on Big Data and Operations in Insurance Company Essay

Download full paperFile format: .doc, available for editing

The paper “ Big Data and Operations in Insurance Company” is a   perfect example of an essay on finance & accounting. Vast data represents exceptionally hefty data sets that could be analyzed for the revelation of patterns, trends, and associations that relate to human behavior and interactions. Big data has been able to capture the attention of various industries including insurance. The extraction of important insights from the big data needs careful planning and execution of improved analytical methods and techniques (Llull, 2016). Granting insurance cover involves a complex process to assess and evaluate the associated risks and claims.

Companies have to sort the large volumes of data in assessing the risk in a single proposal for insurance cover. Big data is redefining the industry with insurers becoming competitive and finding new ways of attracting and retaining customers. Big data is important for fraud detection. Companies do this when the claims are registered, and insurance policies are taken out at the onset. Businesses are overburdened with various proposals and claims though with limited time for gathering and sorting through the information.

Hence, fraud detection depends more on regulatory activities instead of facts. Predictive analysis through big data can help companies to detect frauds faster. Big data is important in enforcing subrogation. Companies need to read many pages of information and be entangled in the large volumes of information; hence, they overlook pointers like the margin notes to initiate subrogation resulting in a direct loss to the insurer. Through the text mining methods, it is easy to process large volumes of texts to determine potential subrogation situation and prompting the action to save the company. Big data is important for fund balancing.

Insurance companies tend to maintain huge funds against potential claims. However, it is difficult to determine the claims’ size and predict the occurrence. Trending and predictive analytical tools may relieve businesses from such burden and assist insurers in making informed decisions on optimization and fund balancing.

Works Cited

Cooper, Jeremy. "The Comprehensive Income Product for Retirement - Cuffelinks." Cuffelinks - Connecting Investors with Ideas, Cuffelinks - Connecting Investors with Ideas, 13 Mar. 2015, cuffelinks.com.au/comprehensive-income-product-retirement/. Accessed 10 Apr. 2017.

Llull, Eduardo. "Big Data Analysis to Transform Insurance Industry." Financial Times, Financial Times, 16 Mar. 2016, www.ft.com/content/3273a7d4-00d2-11e6-99cb-83242733f755. Accessed 10 Apr. 2017.

National Wealth Management Holdings Limited. "Big Data in Life Insurance." Superannuation Insurance Investments Retirement | MLC, National Wealth Management Holdings Limited, 2016, www.mlc.com.au/content/dam/mlc/documents/pdf/media-centre/big-data-report.pdf.

Office of Commissioner of Insurance. "Life Insurance Guide." GADOI Home Page, GADOI, 2011, www.oci.ga.gov/ExternalResources/Documents/Consumer%20Services%20Documents/Life%20Insurance%20Guide.pdf. Accessed 10 Apr. 2017.

Trowbridge, John. "Review of Retail Life Insurance Advice." The Financial Services Council: Home, Financial Services Council, 2015, www.fsc.org.au/downloads/file/MediaReleaseFile/FinalReport-ReviewofRetailLifeInsuranceAdvice-FinalCopy(CLEAN).pdf. Accessed 10 Apr. 2017.

Download full paperFile format: .doc, available for editing
Contact Us