Introduction
AI is the acronym for artificial intelligence, computers’ capacity to analyze and understand information like human beings. It is the power to quickly process big data and to make quick decisions by machines. Artificial Intelligence has proven usefulness in insurance firms in the world today. Insurance companies have begun to apply AI in various processes that involve customer and stakeholder relationships. This application has primarily been reached by insurance firms and businesses to achieve the ultimate goal of better services and reduction in cost.
Thus, there is a need for evolving insurance organizations and companies to adopt AI in their operations to continue enjoying businesses with reduced costs and better services to customers. Artificial intelligence is a significantly increasing computer system automation that is becoming pivotal to varied businesses due to its ability and potential for increased customer service and cost savings (Cipra, 1996). AI benefits insurance companies in fraud detection, improving customer services, reduction in human errors, and global expansion of insurance activities and products.
AI and its Benefits
Roles that can be played by AI include streamlining processes not only in businesses but also in insurance companies. It is impactful in reducing manpower costs through the automation of day-to-day tasks (Makala & Bakovic, 2020). This impact has helped to boost productivity by allowing people to put more focus on more creative works, such as the development of new strategies and the addition of new services and products (Zalocusky, 2016). Offering lower prices and giving back employees’ time would enhance their attention with customers who usually get reluctant when it comes to paying higher prices if they perceive to get less value for their money. Deployment of AI in insurance can be used to bring a better understanding of customers based on previous interaction history, through familiarization of data from calls, text inquiries, and emails. AI will also be helpful in working with extensive and large quantities of data and information. Working with large data and information can be used by the company to promote new products and services and marketing to different categories of customers.
AI has the potential to reduce costs by significantly improving customer service. Companies that have employed AI in their business operations more effectively have been able to gain a competitive advantage over their competitors in the market (Daqar & Smoudy, 2019). If the company incorporates the use of AI in its operations, it would benefit in gaining a positive public reputation and lower prices while developing and gathering trust from its customers. Even though the company could spend time and money to develop new processes and systems on AI installation, its deployment will result in lower costs over time while improving the quality of customer services (Hassanat et al., 2018). An increase in the number customer satisfaction would lead to a higher probability of more customers staying loyal and having continuity in business.
AI is a tool that has promising future for international expansion of businesses. Thus, the use of AI in the insurance industry will help the companies to expand into global markets. The company can easily use the data it has gathered to tailor their products and services to meet the expectations and needs of customers. Further, the company will provide new offerings that are fine-tuned with their preferences, customs, culture, and location.
As a new and developing insurance company, AI will boost and enhance efficient customer services that aim at minimizing costs to the company. It will help in reducing human error in the application processes of insurance. AI has the ability to reduce human error in insurance practices due to its ability to automate labor-intensive processes and underwriting probabilities (Kautz & Singla, 2016). Since the human labor force is slow and subject to tiredness, it prompts the insurance company to employ more workforce to meet service delivery to its customers (Issa et al., 2016). Thus, an increase in human labor induces more operation and production costs for the company. AI, therefore, provides a solution to minimize cost and save time for production through streamlined and smooth claims processing (Kowert, 2017). Further, AI is significant in the rapid document digitization that companies are gradually adopting in the 21st century. Digitization of documents will save the company the cost of on-premise and storage facilities because AI stores documents and information in non-tangible software (Larrañaga & Moral, 2011). Most often, companies undergo loses through the loss of documents due to theft or uncertain incidences such as fire. This problem will be solved through the incorporation of AI because it guarantees the securitization of company property through factor verification and authentication of features such as access to passwords and personal data. The cost of retrieving lost documents and information on customers’ data will, therefore, be evaded through the implementation of AI procedures.
Whether AI Is a Worthwhile Investment for the Company to Make
Incorporation of AI in the company will be a worthwhile investment due to various benefits such as reduced costs and minimized risks. The company needs to stay on the move that will support and fully sustain its operations (Youssef et al., 2017). In addition, the company will need to continue enjoying a competitive advantage and be able to compete favorably with rival companies. This advantage can however, be only achieved through reduced cost and efficiency in the use of resources and finances. AI is highly applauded for its essence in reducing insurance costs by helping insurers assess risk, detect insurance fraud, and enhancement of customer services.
AI will help the company in reducing costs by helping insurers to assess risk. Assessment of risk helps the company to foresee and predict possible losses due to risks (Parreco et al., 2018). AI uses available data from past trends and uses the data to highlight areas that stakeholders need to change. Assessment of risk will help the company to create awareness of hazards and risks and determination of the magnitude, likelihood, and consequences associated with a certain risk. Earlier predictability of risks will be of significant help for the company’s stakeholders to prepare mitigation means for the varied nature of risks. As a result, the company can be guaranteed less cost whenever an uncertain event occurs.
Investment in AI is a milestone approach that is worthwhile for the company to adopt to avoid future losses due to insurance fraud. AI has proven effective in the detection of various frauds that occur in the insurance field. The evolution and advancement in technology have, over the past, cost enormous losses of capital and funds by insurance companies (Howley, 2009). People have lost billions of money due to fraud and forgery of claims in the insurance sector because technology has preceded human intelligence in the visualization of all activities in insurance claims to underwrite (Sandewall, 2014). Thus, AI has evolved a cushioning solution that deploys machine learning intelligence and operationalization in performing several activities at a glance (Chang et al., 2018 & Jaeger, 2016). AI will help the company monitor the system’s activities and sense any malicious activities that might result in losses.
Claims underwriting can be easily simulated by a press of a button and with high efficiency. Therefore, there will be fewer chances of people creating fictitious accounts to make false claims due to the high programming intelligence AI has in detecting fraud (Zeleznikow, 2016). AI is sure that it will safeguard the company from losing money due to fraud. Hence, it will reduce possible costs that the insurance may incur.
Risks of AI and Solutions
The potential that underlies AI is great and needs to be applied by insurance companies to meet the new trend of technology in business. However, there are accompanying risks that need to be addressed before companies can push ahead with the application of AI into business operations. Many are against the current urge by organizations to incorporate AI into business because it may lead to the loss of jobs in favor of machines. Organizations that deeply rely on the automated processes promoted by AI pose a risk of a high rate of unemployment. Thus, people will lose their jobs, and they will not be able to earn a living and sustain their families. For this reason, it will be challenging for unemployed individuals to meet their financial goals and maintain their living standards.
The company should not become too over-dependent on AI solutions. Over-reliance on AI may lead to the inability of the company to adapt to new available information and decreased degrees of trust when errors are experienced in machines. The company will need to be more cautious about increasing the quality and amount of data it is storing since machines do not always trust different types of data and information provided by users. Therefore, the company will be required to collect more data from relevant systems and sources to accurately give a true representation of its processes and customers.
Another risk associated with the incorporation of AI in the company is decision-making silos and cross-functional misalignment in machine learning initiatives. A company that depends on consensus decisions may face a risk of trusting machine-predicted values even when they are not appropriate (Foxcroft et al. 2009). Due to dependence on AI initiatives, there is a further risk of opaqueness and bias in the stakeholders’ activities in the company. However, this problem can be solved through continuous checks and scrutiny and update of the AI system used by the company. This process helps to identify hitches and errors that arise due to faultiness in the machine automation. Employment of an experienced and skilled labor force will help to eliminate biases because they scrutinize machine-automated processes.
The company will need a plan before taking action while incorporating AI operations into their business. It is advisable that the company should have a test run of its AI system to ensure smooth interaction between human intelligence and artificial intelligence and that it has the capability to present all information data within the organizational documents. This precaution can be achieved through the collection of data from past history of interactions with customers and employees and from integrating this data with accurate and relevant information. Though the company may have the best of intents to use AI in promoting cost efficiency, the company will have to make sure that they have prepared with other means of human resource before implementing AI and avid over-dependent on the technology. It is estimated that insurance companies report annual losses of over $305 billion annually. This loss is subject to insurance and human error costs in the insurance sector and firms.
Type of Data Collected
Different types of data will be required to be collected as the company dives into the incorporation of AI. For instance, the data needed to be collected include visual, textual, and numerical to provide predictions about which approaches work and which do not work for the company (Parreco et al., 2018). The company will use this data to offer analysis of the customers and predict consumer preferences, marketing channels, and product development. Further, data collected can be stored to make comparisons in the future expectations of the market and use the predictions to develop or improve on products in demand.
What it would take to Scale AI across the Organization
The successful scaling of AI in the company will not only rely on the technical teams but majorly on the leadership management. Leaders shall be expected to empower and build on specialized and dedicated teams that are focused on high-value strategic priorities that need to be accomplished through teamwork. Specialization will be primarily needed at each service and production stage. For example, data scientists shall be required to deal with data science and harness scientific data that the company needs for efficiency in the execution of its processes and activities (Zeleznikow, 2016). On the other hand, engineers will be required to tailor engineering processes that involve installation and construction of the needed infrastructure of the Organization and information technology (IT) shall be mutually deployed in issues to do with communication and information management.
Another scaling factor for the company will be to build an enabling culture that will promote different ways and forms that diversify sources of communication. An enabling culture will help the company invest in data strategies and transform operations for AI integration. The company will also be required to pick tools that support creativity, safety, and speed, which will make it easy for the company to orchestrate solutions for technology and talent. Interoperability, governance, and collaboration is a prerequisite scaling that the company will need while installing AI. AI will require an interactive environment between stakeholders and employees to create a chain linking every interface of the Organization to ensure that every activity adheres to the alignment of the company’s codes of ethics.
Groups and Processes that will benefit the Company
Different groups and processes will benefit from AI in myriad ways that are primarily significant in reducing cost and betterment of services. The groups and processes that will benefit from AI incorporation include the leaders, employees, customers, and claims underwriting processes.
Leadership
Leadership will benefit from AI through fast analysis and prediction of future trends in the insurance market and products in demand. Leaders and investors will be able to consciously make safe investments in the company without fear of loss and inappropriate investment portfolios. AI data analytics will help leaders learn about areas that need more investment than others based on decision techniques such as the Bayesian (Foxcroft et al., 2009 & Koehrsen, 2018). Thus, managerial skills and decision-making can be further enhanced through automated processes such as machine learning-initiated decisions and Bayesian trees.
Employees
AI will increase the productivity of employees by handling repetitive tasks in the company, which facilitates focus on creative solutions, impactful work, and complex problem-solving. For instance, employees can use chatbots that have been initiated by AI reduces employees’ work of disseminating messages to many customers about policy formulation and payment of premiums at a glance (Tsvetkova et al., 2017). Employees will be boosted and augmented in their work to make them more effective and fast at work. In addition, employees will benefit from AI because it will provide them with invaluable opportunities that are useful for career advancement.
Customers
Customers will be greatly helped in deeper engagement in services offered by the company. There will be reduced customer handling time which saves time while delivering proactive support. Customers will be able to track performance of the company and predict future trends while having more time of focusing on complex problems.
Claims Underwriting
AI-guided underwriting system will be vital in the underwriting process through accurate quantification of the unstructured and qualitative data points such as news feeds, social media, third parties, and reliable statistics form public sources. This process helps in conveying a comprehensive risk profile to underwriters in high degrees of interpretable manner.
Conclusion
AI has the potential to play an essential role in the insurance industry in the world today. AI is used to better services and reduction of costs in different ways. The incorporation of AI in insurance practices helps in fraud detection, which prevents the loss of money due to theft. Customer services have been streamlined through the use of AI since it reduces the time taken in service delivery and user interface. Further, AI is deployed by insurance firms through predictive cost analytics for claims underwriting. Risks associated with AI include unemployment due to the replacement of human labor by machines and bias in administration actions such as decision-making. AI helps stakeholders achieve efficiency in organizational goals while automating customers’ services like claim underwriting.
References
Brown, S. F. (2004). Send in the swarm. Fortune, 149(12), 52–54.
Brown, S. F. (2010). Science and technology: Riders on a swarm; artificial intelligence. The Economist, (396), 65-66.
Carty, S. (2017). AI: Rise of the machines?. Occupational Health & Wellbeing, 69(7), 20.
Chang, C. W., Lee, H. W., & Liu, C. H. (2018). A review of artificial intelligence algorithms used for smart machine tools. Inventions, 3(3), 41.
Cipra, B. (1996). Will a computer checkmate a chess champion at last?. Science, 271(5249), 599-599.
Daqar, M. A. A., & Smoudy, A. K. (2019). The role of artificial intelligence on enhancing customer experience. International Review of Management and Marketing, 9(4), 22.
Foxcroft, D. R., Kypri, K., & Simonite, V. (2009). Bayes’ theorem to estimate population prevalence from alcohol use disorders Identification test (AUDIT) scores. Addiction, 104(7), 1132-1137.
Hassanat, A. B., Prasath, V. S., Abbadi, M. A., Abu-Qdari, S. A., & Faris, H. (2018). An improved genetic algorithm with a new initialization mechanism based on regression techniques. Information, 9(7), 167.
Howley, K. (Ed.). (2009). Understanding Community Media: SAGE Publications. Sage Publications.
Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1-20.
Jaeger, H. (2016). Deep neural reasoning. Nature, 538(7626), 467-468.
Kautz, H., & Singla, P. (2016). Technical Perspective: Combining logic and probability. Communications of the ACM, 59(7), 106-106.
Koehrsen, W. (2018). Bayes’ rule applied – Towards data science. Towards Data Science.
Kowert, W. (2017). The foreseeability of human-artificial intelligence interactions. Tex. L. Rev., 96, 181.
Larrañaga, P., & Moral, S. (2011). Probabilistic graphical models in artificial intelligence. Applied soft computing, 11(2), 1511-1528.
Makala, B., & Bakovic, T. (2020). Artificial intelligence in the power sector.
Parreco, J., Hidalgo, A., Kozol, R., Namias, N., & Rattan, R. (2018). Predicting mortality in the surgical intensive care unit using artificial intelligence and natural language processing of physician documentation. The American Surgeon, 84(7), 1190-1194.
Rico-Contreras, J. O., Aguilar-Lasserre, A. A., Méndez-Contreras, J. M., López-Andrés, J. J., & Cid-Chama, G. (2017). Moisture content prediction in poultry litter using artificial intelligence techniques and Monte Carlo simulation to determine the economic yield from energy use. Journal of Environmental Management, 202, 254-267.
Sandewall, E. (2014). A perspective on the early history of artificial intelligence in Europe. AI Communications, 27(1), 81-86.
Tsvetkova, M., García-Gavilanes, R., Floridi, L., & Yasseri, T. (2017). Even good bots fight: The case of Wikipedia. PloS one, 12(2), e0171774.
Youssef, A., El-Telbany, M., & Zekry, A. (2017). The role of artificial intelligence in photo-voltaic systems design and control: A review. Renewable and Sustainable Energy Reviews, 78, 72-79.
Zalocusky, K. A. (2016). Dopaminergic Control of Individual Variability in Risk Preference. Stanford University.
Zeleznikow, J. (2016). Can artificial intelligence and online dispute resolution enhance efficiency and effectiveness in courts. In IJCA (Vol. 8, p. 30).