Insurance is an immaterial product which is valued subjectively by

Insurance is an immaterial product, which is valued subjectively by its recipients. Buying insurance is buying a “feeling”. Because it involves the subject and insurance products, which differ from one person to another. On top of personal need, there are other differences between people that follow professions, the difference between countries (Hofstede, 1995). However, an insurance company maintains its business by providing “coverage in the form of compensation resulting from loss, damages, injury, treatment or hardship in exchange for premium payments. The company calculates the risk of occurrence then determines the cost to replace (pay for) the loss to determine the premium amount” (Anon.

, n.d.).

Now a day’s companies have started to use artificial intelligence (AI) in order to tackle insurance coverage. “Artificial Intelligence (AI) is a sub-field of Computer Science that focuses on the simulation of intelligent behavior in computers”, according to Merriam Webster. The objective of AI has been to give better administrations and items dependent on human thinking instead of to recreate the human personality (Gbande, 2018).

Machine learning is a subfield of artificial intelligence. Its goal is to empower computers to learn without anyone else. “Machine learning algorithms are commonly used to guide computers to process and analyze data in a useful way i.e. to identify patterns in observed data, build models which clarify the world and anticipate things without having express pre-modified principles and models (Maini, 2017). In other words, machine learning is a subset of computer science in which algorithms “learn” patterns from datasets and improve their predictions about this data over time.

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The task for organizations to use machine learning in them is the use of these algorithms to collect data from millions of internet users and generate personalized recommendations for each person.” A company should have these elements to use machine learning i.e. a large amount of user data and a well-specified goal that the company wants to achieve (Jennifer Wei, 2017).

In general, the more data you feed, the more accurate are the results. Coincidentally, tremendous datasets are extremely regular in the financial services industry. There is a wide scope of open-source machine learning algorithms and tools that fit enormously with financial information. There are petabytes of data on transactions, customers, bills, money transfers, and so on. That is an ideal fit for machine learning (Boichenko, 2018). In the case of financial services such as banks, insurance companies have the maximum gain from data. For ease of capturing and value potential, financial players get the highest marks for value creation opportunities. Banks and Insurance companies can add value to the market by improving a number of products, e.g., customizing UX, improved targeting, adapting business models, reducing portfolio losses and capital costs, office efficiencies, and new value propositions (Cavanillas, et al., 2015).

Financial services have a wide degree to outline plans of action and systems by mining and analyzing the information they have which can be utilized to the discovery of fraud, and improvement of operational efficiencies. Prescient investigation of both interior and outer data results in better, proactive administration of a wide scope of issues from credit and operational hazard (for example fraud and reputational risk) to client dependability and productivity. A challenge for the financial sector is “how to use the breadth and depth of data available to satisfy more demanding regulators while also providing personalized services for their customers”. The measurement of data can be fulfilled by implementing “machine learning” oriented applications (Cavanillas, et al., 2015). As indicated by the Global Fraud Study 2014, an association loses around 5 % of incomes every year due to fraud. The banking and financial services sector has a great number of frauds. Around 30 % of fraud plans were distinguished by a tip-off and up to 10 % unintentionally, yet just up to 1 % by IT controls (ACFE, 2014). Better and improved fraud detection methods depend on the constant examination of real-time data. By utilizing machine-learning algorithms, current frameworks we can recognize fraud more reliable and quicker (Sensmeier, 2013). Nevertheless, internal business processes are regularly copied, bureaucratized, and tedious. As the pervasiveness of machine learning and artificial intelligence frameworks expands, they can possibly robotize activities in insurance agencies in this way cutting expenses, increase productivity and expanding profitability. On average, every day an insurance agent spends up to 50 percent of their time manually filling in different forms to handle claims. Speech recognition algorithms are used in Natural Language Processing (NLP) field in AI, which can decipher and even translate human discourse to streamline this lumbering everyday practice (Harnat, 2018).

Since, a wide range of insurance claims contain pictures, including health and medical services, vehicle insurance, and even agro-cases. Image recognition has turned out to be a standout amongst the most broadly utilized technique in machine learning (i.e. using deep neural networks) (Harnat, 2018). The recent work from (Le, et al., 2012) in which a dataset of ten million images was used to teach a face detector using unlabeled data. Based on the resulting features in an object recognition task, which has resulted in a performance increase of 70 % (Le, et al., 2012). Utilizing large amounts of data without labeling could become an important trend. By using unlabeled data, one of the biggest bottlenecks to the broad adoption of machine learning is bypassed. This example gives us clear indication machine learning can identify patterns and give conclusions by itself by continuous learning process from unlabeled data, which has a wide scope in applications.

Organizations today are focused on Artificial Intelligence as it has wide areas of application with reliable results in various industries. Today’s most of the functions of insurance industry such as claims management, policy management, underwriting, detection of fraud, understanding market trends, product portfolio, etc. follow traditional or semi-automated methods of analyzing and approach. This is time-consuming and ultimately reduces the customers’ satisfaction which can lead to a loss in market share. Applying suitable algorithms of machine learning enables AI tools to analyze and learn from data patterns which can create a huge positive impact on the functioning of insurance organizations which ultimately attracts the markets and customers. This

research gives recommendations to insurance companies regarding the application of machine learning in possible areas of functioning. Also helps organizations to build future business models and decisions, modify the organization functioning.