Using big data analysis to realize the ultimate risk prevention and control of insurance industry

Using big data analysis to realize the ultimate risk prevention and control of insurance industry

In the Internet era, especially with the increasing popularity of mobile Internet, the collection of big data has become more convenient and feasible. The application value of big data has attracted the attention of all walks of life, and even big data itself has become a specialized industry. As a commercial activity based on the law of large numbers, insurance has a natural tendency to use big data. Focusing on the business practice of risk prevention and control, the author discusses the application of big data analysis in risk prevention and control, analyzes the advantages, points out the limitations, and puts forward the development suggestions of big data analysis in combination with the current situation of the industry.

The insurance industry is facing new challenges of risk control.

Although risk prevention and control is an eternal topic in the development of insurance industry, with the development of economy and society, new risks emerge one after another, malicious fraud methods are constantly being refurbished, and the risk prevention and control of insurance industry is more severely impacted. The specific performance is as follows:

1. Industry competition forces the underwriting and claims to speed up, which may bring negative effects on the quality of underwriting and claims. From a purely theoretical and ideal point of view, underwriting and indemnity can shield all adverse selection and moral hazard for insurance companies. But the price paid is to use a lot of manpower to conduct a large number of detailed investigations on each application for insurance and claims. This is impossible in the actual operation of insurance companies. Especially in today's increasingly fierce competition in the industry, in order to enhance the customer experience, the insurance conditions of insurance companies have become more relaxed and the underwriting speed is fast. Even underwriting, physical examination and quick compensation have become the "standard" for insurance companies to attract customers. Companies try their best to improve the service speed, and the underwriting department often has to bear the double pressure of customers and sales departments. In this case, although the premium income of insurance companies has greatly increased, the risk impact will increase significantly. The company's management's expectation of performance growth has more or less diluted the risk control awareness that should be impregnable.

2. The development of Internet insurance objectively increases the difficulty of risk control. Nowadays, online sales and mobile Internet sales are paid more and more attention by insurance companies. All kinds of insurance sales websites have become new premium growth points for insurance companies. Even customers can easily complete the insurance or claim settlement process through software terminals such as mobile phone WeChat. In this case, it is more difficult to verify the authenticity of materials, information asymmetry is more prominent, and the risk of opportunistic fraud increases. The increase of accidents in different places also puts forward higher requirements for the follow-up work of claims settlement, which is prone to the gap between insurance service processes. In the traditional insurance sales process, the face-to-face communication between salespeople and customers is actually a process of understanding customers. But the development of Internet insurance makes this process disappear. The nuclear insurance department has lost a natural barrier. All these increase the difficulty of risk control.

Practical Significance of Big Data Analysis in Risk Prevention and Control of Insurance Industry

Although the development of Internet technology has brought great challenges to the risk prevention and control under the traditional thinking. However, the author thinks that the progress of any new technology is a double-edged sword. Moreover, it is necessary to tie the bell to solve the problem, and the "trouble" brought by internet technology will definitely be stipulated by the internet technology itself. This prescription is big data analysis.

IBM used five characteristics to describe big data, namely, large quantity, high speed, diversity, low value density and authenticity. These characteristics actually illustrate the significance of big data for risk prevention and control.

1. In the era of big data, the underwriting link has the conditions to conduct systematic risk scanning for customers through big data analysis. Specifically, in the traditional underwriting process, the insurance company will review the contents informed by customers. Underwriters should find clues of risk points from limited information. The risk control in this process mainly depends on the customer's integrity level and the work experience of the underwriting staff. Moreover, a large number of insurance notices also challenge the patience of customers. Faced with a large number of problems, customers are likely to resent it, fail to fill in the notice carefully or simply give up buying insurance products. Under the condition of big data, insurance companies have the conditions to obtain a lot of relevant information about customers from the database. For example, by understanding the customer's medical records, we can accurately infer the customer's health status; By querying the customer's insurance records in various insurance companies in the past, we can analyze whether the insured has repeated insurance, large insurance in a short period of time and other high-risk behaviors, and so on. All these will break the previous management thinking of underwriting and make the underwriting process more accurate. At the same time, the insurance notice that customers need to do is also greatly reduced. As long as the insurance company is authorized to inquire about relevant information, the underwriting results can be obtained quickly.

2. In the era of big data, it is easier for underwriting to find claims fraud clues and plug risk loopholes through big data analysis. In the traditional claims process, the risk is mainly identified by the experience of the claimant, and the occurrence of claims fraud is blocked by the investigator's conscious investigation. In this case, it is possible to artificially create an insurance accident, falsely report an insurance accident that does not really exist, and exaggerate the loss amount of the insurance accident. Under the condition of big data, the past claims data of insurance companies in different regions and even claims data between different insurance companies may be aggregated into a super database. Any claim can be checked through the database first.

3. The theoretical research of big data analysis aided risk control has accumulated to a certain extent, laying a foundation for further application. In recent years, the development and application of big data not only attracted the attention of practical circles, but also attracted more detailed research in theoretical circles, and achieved certain results. For example, fraud analysis technology is the application of integrating big data model, statistical technology and artificial intelligence in the field of anti-insurance fraud. At present, this technology has a relatively complete theoretical model, and the corresponding algorithm system has been established, including supervised algorithm and unsupervised algorithm. The author believes that although these theoretical studies are somewhat obscure to insurance practitioners, the future big data analysis and even the application of artificial intelligence in the insurance industry are based on these theoretical studies.

Improving Risk Control of Insurance Industry Based on Big Data Technology

Combined with the development requirements of big data technology itself and the actual operation of insurance companies. In this part, the author will put forward specific suggestions on the improvement of insurance risk control in the era of big data.

1. On the basis of database construction and integration of internal data resources, strive to establish an industry-wide big data platform. All the advantages of big data analysis discussed here are based on the fact that insurance companies can collect a lot of valuable data. This arrangement of data resources is first of all the arrangement of internal resources of the company. Especially for large financial groups with mixed operations, it is a great achievement to integrate existing internal data resources. It is inevitably difficult for companies to enjoy information, which requires the promotion of industry associations and regulatory authorities, and requires companies to look forward to the development of the insurance industry with a longer-term perspective.

2. Insurance companies should do everything possible to improve the level of IT technology and reserve the technical strength of big data analysis. Big data analysis requires high database technology, and the company's network system and data computing ability are facing challenges. More importantly, in order to further develop big data resources, there must be specialized statistical analysis talents. Technical reserve is not a simple data development such as operational data analysis in the past, but a scientific system. It is necessary for insurance companies to make technical reserves in advance.

3. In the process of big data analysis, special attention should be paid to data security and confidential management of customer information. Big data, like the Internet, is a double-edged sword. Insurance companies can get twice the result with half the effort in risk prevention and control if they dig this treasure well. But it also shoulders the heavy responsibility of maintaining data security. Insurance companies store a huge amount of personal information data, and once it is leaked, the consequences are unimaginable. A data leak may cause a lawsuit from the customer. A batch of data leaks may bring disaster to the company. From a legal point of view, insurance companies should obtain customer authorization before quoting customer information to avoid legal risks. At the same time, rely on big data analysis as much as possible to infer the risk of a certain type of business through simple customer information.

In a word, risk control is an important part of the stable operation of insurance companies. In the era of big data, the insurance industry is bound to use new scientific and technological means to prevent and control risks to the extreme and create value for the development of companies and industries.