Big Data and Operations in Insurance Company - Essay Example

Name Professor Course Date of submission Insurance Company Operations Big Data in Insurance Company Vast data represents exceptionally hefty data sets that could be analyzed for the revelation of patterns, trends, and associations that relate to the human behavior and interactions. Big data has been able to capture the attention of various industries including insurance. 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 the 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 into 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 the 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. Moreover, big data assist in managing litigation costs and underwriting (National Wealth Management Holdings Limited, 2016). Most earnings from insurance companies go to lawyer dues. Litigations occur due to poor handling of the case. Predictive analysis through big data may assist businesses in identifying difficult cases and assigning them to experienced personnel. Underwriting requires a large amount of data. Manual assessment involves high human error; however, through the digital form, businesses can eliminate the errors. The aim of insurers is to lower the premium rates to maintain their competitive advantages. Businesses calculate premiums based on the probability of the risks to occur. The calculations are complex, which hinders periodic monitoring of the rates and adjustments. Big data assist businesses in performing quick calculations and undertaking analysis that predicts future rates. As a result, insurers maintain the market edge at low cost. Big data is also important in customer segmentation, and value added service. Calculations of the premiums are based on the claim experience of the groups of customers. With big data analytical tools, businesses can group their customers and calculate the risk-based premiums. Regulation of life risk policies and advice Quality advice and improvement in the life insurance coverage across the communities are important o the wellbeing of the people. This is recognizable through the financial advice and life insurance businesses that are committed and determined in rebuilding trust and confidence within the insurance sector. To ensure effective and efficient performance, the Association of Financial Advisers and Financial Services Council founded the Life Insurance and Advice Working Group. At the basic level, industrial productivity reflects inclusive activities of various individual agents and firms within the industry. Within the retail life insurance and advice industry, the actors are considered insurers, advisers, and life insurers. The manners in which life insurers, advisers, and licensees allocate their resources depend on incentives experienced within the marketplace and interactions with each other. Within the third part reliance scenario, the third party often has existing business relationships with the client that is independent of the relationship formed between the insurance company and client. An effective Life Insurance Code of Practice needs to contribute to the improvement of wide arrays of practices within the industry. Some of these practices should enhance the decision-making process and efficiency of both the advisers and licensees in due course, as they work improvement of the methods used to operate businesses (Trowbridge, 2015). It is recommended that life insurers address the lapse rates, misaligned incentives within their channels of distributions, and reviewing their remunerations arrangements for supporting good-quality results, and better management of the conflicts of interests. Review of the retail life insurance advice noted that the current commission arrangements of the life insurance need to create an incentive for the advisers recommending to the consumers to consider replacing the policy rather than retaining it that occurs irrespective of the best interest of the customers. There are several consumer detriments in which the adviser focus on making the product sale instead of providing the non-product specific advice The fundamental policy objective is to ensure that the consumers are insured properly. With the recent growth in the direct and group distribution channels, there has been an improvement in the life insurance coverage within Australia, which has remained the case that most consumers purchase life insurance through the financial adviser. For the best interest of the consumers, there is need to consider long-term practices. This will ensure that life insurance advice channels of distribution are effective and sustainable. However, such practices require policy intervention within the commission arrangements. Various factors are expected to improve the outcomes of customers and restore their confidence and trust. Policy intervention plays an important role in improving the current market dynamics and engenders the type of competition aligned towards the interest of the consumers. Traditional Life Policies The government is committed to supporting the development of efficient retirement income products and facilitation of the trustees that offer such products to the members. This is in response to the Financial System Inquiry (FSI). In Australia, the retirement income stream is unusual through its global standards since it is dominated by a balanced account-based pension (ABP). It is recommended for investors based on the underlying choice, liquidity, flexibility, and control of investment. FSI made it clear that average balanced ABP cannot manage properly the unique risks associated with retirements. Hence, it needs to be viewed as part of the retirement portfolio instead of the whole solution (Cooper, 2015). It is clear that FSI recommended that APRA-regulated super funds select CIPR) in a bid to address the needs of the retirees: flexibility, high income, and risk management features. According to FSI, such requirements are likely to be satisfied through a combination of products starting with the ABP. The final report submitted suggested the potential for sing an array of CIPR besides ABPs and annuity products. The combinations are future innovations, deferred GSAs, deferred lifetime annuities, and the Group-acquisition schemes. Recently, there have been many markets focusing on the annuities; however, the case for annuities is weak within the Australian retirement context characterized by low super balances. For the retiree considered to have moderate superannuation balances, their choices for the income product products depend on the existing trade-offs between management of the risk and flexibility. Moreover, the components of annuities often assist with the longevity of risks. With post-retirements markets, annuity products account for about 5%. To enable the retirement phase better achieves the objectives associated with the superannuation system, Australia needs to manage the challenges associated with the aging population. Term life insurance Various rating factors are used to set the price of insurance. For the latter, age has the ability to influence both the likelihood of the policyholders that make claims and the cost of such claim whenever they occur. In vehicles, if the person in question is not responsible for the crash or is under the age of 16, then such a person is entitled to claim CTP insurance claim for personal injury compensation. This is achievable through contacting the regulator. Most people often feel vulnerable to various risks and hazards especially the drivers (Office of Commissioner of Insurance, 2011). Based on the research by Fox Business, motorists file insurance claims about once in each 17.9 years. Even though the age may seem long for young people, meaning that 34 years, each person that drives since 16 years are likely to have filed an insurance claim. Not each person needs life insurance. The decision on the significance of life insurance depends on the age, assets, and availability of the dependants. Insurance companies use underwriting process to decide whether to see life insurance policy to someone and the amount to charge. However, the companies consider the age, gender, hobbies and occupation, use of tobacco, and medical condition. The whole life insurance covers are fixed but based on the age issue. The insured person pays premiums until death except if the policyholder subscribed to limited pay policies that are payable in 10, 20, or 65 years of age. Life insurance covers mature at death or at the age of 100 years. This is different from other policies that are claimable upon the occurrence of the risk insured against, irrespective of the age. 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. Read More