2024 Trends: Data Analytics in Health Insurance for Better Risk Management
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2024 Trends: Data Analytics in Health Insurance for Better Risk Management

By Rajarshi January 08, 2024 - 118 views

The health insurance industry has witnessed a rapid evolution in terms of digitisation, which has also swiftly transformed several other sectors worldwide. One of the biggest contemporary trends in health insurance (and poised to last for the foreseeable future) is the harnessing of data analytics to ensure superlative risk management among other benefits.

Pharmacy managers, insurance companies, healthcare providers, and other industry stakeholders are already leveraging analytics to tackle issues of fast-growing healthcare costs. Insurance companies are already using them to identify high-risk patients and operate accordingly.

Why Data Analytics is Indispensable

From an operational standpoint, data analytics is indispensable for health insurance companies. They keep generating huge data volumes internally through sales and engagement but also get sizable chunks from varied external sources. When this data spreads out through multiple systems, it becomes an uphill task for insurers to effectively use and track the same.

This is why analytics-based solutions that can fuse and consolidate data from multiple touch points and sources have gained relevance in recent times. A centralised data gathering system with consistent analytics and actionable insights is thus one of the key trends in health insurance currently.

How Data Analytics Enables Better Risk Management

Data analytics is undoubtedly contributing towards enabling insurers to manage their risks better. Here are some aspects that should be highlighted in this regard.

  • Insurers can identify fraud swiftly before it takes place. Just to put it into context, healthcare fraud alone costs the U.S. a staggering $68-230 billion per year. This is 3-10% of the country’s healthcare spending estimated at $2.26 trillion.
  • Claims investigators are already deploying predictive analytics for evaluating unstructured data like posts on social media, while also identifying potentially fraudulent habits/behavior. They can thus flag specific claims for further review.
  • With machine learning being added to the tech stack for insurers, they can track customer behaviour over some time, while implementing new regulations when such fraudulent patterns arise. This will take the guesswork out of fraud prevention and detection as a result.
  • AI and data analytics can enable insurers to swiftly flag claims of a fraudulent nature. Hence, claims can be automatically approved or rejected, depending on several variables.
  • Machine learning and AI can work in tandem with analytics to help identify fraudulent billing while flagging providers/accounts that consistently provide incorrect data.
  • Data analytics can also help insurers gather and evaluate information from wearable and health-tracking devices. They can thus understand the risks and health statuses of people along with their habits before quoting premiums. They can also create customised and incentive-based programs that encourage healthy behavioural habits in return for lower premiums.
  • Predictive analytics may also help in fast-tracking claims processing. Insurance companies can easily analyse historical data like past claims and profiles to identify behavioural patterns. They can use predictive modelling to work out potential outcomes and use these insights to respond faster to claims and enhance customer satisfaction while reducing risks at an organisational level.

These benefits have gained even more traction in recent years if one considers how insurers lose a whopping $40 billion per annum on account of fraudulent claims as per Gartner reports. Many insurance companies also estimate 10-20% of claims to be fraudulent while identifying less than 20% of the same.

Suspicious and fraudulent behaviour and patterns can be identified with data analytics, with insurers creating diverse models to enable swift detection based on historical data and activities.

For instance, analytics has reportedly helped the Czech Republic’s Allianz Insurance to save a whopping US$4.5 million annually by lowering fraudulent claims paid by it. This is just one of the instances that testify to the need for insurance companies to rapidly adopt data analytics into their operational frameworks.

Analytics can be used to conduct risk evaluation in real-time which will help organisations respond swiftly in volatile scenarios. For example, in the case of auto insurance, an accurate assessment of risks posed by specific drivers will help insurers create more competitive premiums.

Cars connected to the internet will help them gather large volumes of data accordingly. Insurance firms can now predict the chances of drivers being involved in accidents by analysing driving habits and behavioural data. Some other advantages of data analytics include easier customer lifetime value (CLV) prediction and prospective claim forecasting.


How can data analytics contribute to more precise risk assessment in the health insurance industry?

Data analytics can enable more accurate risk assessments in the health insurance sector. It can help identify fraudulent behavioural patterns and flag the same for review before the actual payout.

What impact do 2024 trends in health insurance data analytics have on customer experiences and personalised offerings?

2024 trends in health insurance data analytics will have a positive impact on customer experiences. Insurers will be able to personalise their offerings for customers based on their behavioural data and also offer custom premiums with incentives for recommended and healthier actions.

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