Big Data-Driven Precision Risk Control in Health Insurance Claims: Breaking Down Data Silos for Intelligent Management

Amid the booming global health insurance market and strong policy support in China, the health insurance industry may be reaching a true turning point. Data show that in January 2019, gross written premiums for health insurance business grew at the fastest rate, reaching RMB 79.8 billion, a year-on-year increase of 49.8%.

Meanwhile, data from the U.S. news and information website Axios shows that “the U.S. commercial health insurance market has recently surged and is expected to surpass the internet technology sector: The combined 2019 revenues of five major U.S. commercial health insurers—Anthem, Cigna, CVS Health, Humana, and UnitedHealth Group—are projected to approach $787 billion, exceeding the estimated combined revenue of $783 billion for the five largest tech companies—Facebook, Amazon, Apple, Netflix, and Google.”

The health insurance industry may be approaching a true inflection point, with insurtech serving as the core driving force behind its development. Among key factors, precise data application services are particularly critical in addressing current data-related challenges—such as the lack of data sharing, misalignment between clinical medicine and insurance medicine data, and inadequate internal data-driven risk control—thereby accelerating the establishment of a favorable internal and external environment for the growth of commercial health insurance.

At this year’s “Two Sessions,” a member of the Chinese People’s Political Consultative Conference (CPPCC) proposed that the development of health insurance requires the establishment of mechanisms for medical data sharing and updating to break down “data silos.” Across most insurance enterprises in China, the lack of information-sharing platforms with healthcare institutions and insufficient access to medical data sources have led to severe “data silo” problems, imposing significant constraints on the growth of insurers. Many industry insiders share this view. An executive at China Life Health Insurance noted that domestic insurance institutions struggle to obtain medical data during the initial underwriting process, resulting in shallow levels of cooperation and limited tools for managing healthcare costs. Meanwhile, a vice president of Taikang Group stated that insurers’ relative weakness in data support has long hindered breakthroughs in the pricing of health insurance products.

Insurance companies collect clinical medical data from various healthcare institutions, but practical applications have revealed issues such as coarse data granularity and imprecise matching. These challenges not only increase the cost of manual analysis but also elevate the risk costs associated with health insurance operations. Therefore, precise data matching is essential to help insurers obtain comprehensive historical medical records of customers, mitigate adverse selection during underwriting, enable efficient rapid and direct claims settlement, and automatically detect concealed information or suspected fraudulent activities for risk control purposes.

In the claims processing segment of health insurance companies, there has been a persistent and urgent need to strengthen risk control capabilities and implement effective cost containment measures. The prevalence of non-standardized data has led to numerous cases of significant financial losses for insurers. Consequently, there is an immediate need to leverage advanced artificial intelligence systems for automated risk control, enabling the identification of risks at various levels, the analysis and assessment of claims data, and the continuous optimization of operational processes.

In response to these challenges, industry solutions have become increasingly mature. Currently, there are more than 200 insurtech companies in China, empowering insurers across vertical segments such as product development, marketing, and claims processing. Additionally, they provide innovative, technology-driven solutions that offer full-process, full-lifecycle support within the health insurance service chain.

For example, Haorensheng Technology’s “Health Insurance Cloud” provides data services across the entire workflow of health insurance services, playing a significant role particularly in claims risk control. It breaks down “data silos” and leverages “Intelligent Drug Testing” to identify abnormal medication use and prevent drug abuse. The system enables automated “Intelligent Claims” risk control, detecting economic losses arising from patients’ anomalous medical-seeking behaviors and non-compliant operational practices by insurers during the claims process. In practice, the “Health Insurance Cloud” has achieved multiple successful cost-containment outcomes, helping insurance companies recover nearly RMB 10 million in economic losses.

The future development trend of health insurance is promising, but the existing issues require not only policy support but also precise big data applications to help insurance companies efficiently improve efficiency, identify risks, and usher in a new era of comprehensive data integration.

By Haorensheng Technology