Predictive Analytics in Health Insurance

Table of Contents

Introduction
Use Cases of Predictive Analytics in Health Insurance
Claim Metrics Used by Predictive Analytics in Health Insurance
Conclusion
Topics in Predictive Analytics

Introduction

Predictive analysis is creating a disruption in every sector and health care has not been left behind. Using the rich historical data, machine learning and AI tools, the healthcare players are reaping big benefits.

Predictive analytics retrieves data from the hospital servers. It then combines, weaves and interprets these huge data sets and apply them to places where it suites. With the information background, health professionals can treat patients based on the historical data available, they can make treatment plans based on future possible outcomes, this works especially for terminal diseases patients. Predictive analytics combined with physician’s insight brings the best outcome in healthcare.

Health insurance firms are also taking advantage of predictive analytics. Lexis Nexis Risk Solutions claims that the main benefits of predictive analytics in health care insurance are data-driven claims, improved profitability and minimized operating costs.

The use of this behavioural intelligence and AI in claims and applications are many.

Use Cases of Predictive Analytics in Health Insurance

General Reinsurance Company argues that some of the uses of predictive analytics are reserving settlement values, Early warning of high-value losses and trend analysis just to mention but a few.

Now, let’s further dig into this.

Use Case	Description
Identifying Potential Markets	Here, rich data provide detailed customer reports which show patterns and behaviours, common characteristics and demographics. This way insurers will know where to focus their marketing efforts. By use of digital marketing, customer identification has been made easy; it has further influenced customer service through easy customer reach out and feedback.
Risk and Fraud Detection	Predictive analytics can significantly reduce cases of broker fraud and client fraud. Hospitals and insurance agents can collect data and analyze the relationship of patients with fraudulent behaviours in the past so that whenever a suspicious fraud case arises the system can automatically flag it before the fraud happens. Hospitals can embrace predictive analytics models in insurance to promote transparency and accountability.
Policy Optimization	Insurance companies are fast following a personalized kind of approach when pricing policies as one size fits all models is not working. Predictive analytics gives health insurers a chance to customize policy plans to suit specific clients based on the historical and current data available. With access to insights like behavioural signals, price sensitivity and customer preference; health insurers can tweak premiums on a case-to-case basis.
Dynamic Customer Engagement	An important KPI of measuring health insurance performance is through customer experience, this alone sets a benchmark for healthcare insurance industry players to streamline customer engagement. Happy members mean more business and an increase in revenue.
Insurance Claims Management	For successful insurance claims management, health insurers can use predictive analytics to automate data-intensive insurance processes, detect fraud, offer payouts and extend service options. This straightens and standardizes the process having in mind the disruptive demands.

Claim Metrics Used by Predictive Analytics in Health Insurance

Metric	Description
Count of Paid/Denied Claims by the Provider	This metric reads the breakup of denied and paid claims. It feeds insights for business decisions for the future, potential opportunity and operational performance.
Claims Inventory	Predictive analytics uses this metric to predict expected workloads and plan teamwork’s schedule or foresee a dip or rise. The claims inventory tracks the weekly dump of open claims.
Claim Frequency	To predict loss is a step ahead for any business. This key indicator measures the potential of loss occurrence by analyzing the number of policies outstanding. This helps reduce risk exposure, manage rate settings and cash flows.

Conclusion

Health insurance is a step ahead in implementing predictive analytics with profitability and efficiency in mind. However, Healthcare and insurance industries are a newbie when dealing with artificial intelligence and big data. Predictive analytics will be a key tool in the healthcare insurance industry and grow as technology improves.

By fully utilizing predictive analytics, health insurers will have a better completive edge than the players who don’t use predictive analytics. For sure as AI and predictive tools become more user friendly, they will bring a profound impact on insurance companies and customers in future.

Topics in Predictive Analytics