How AI and ML are Transforming the Insurance Industry

To offer competitive rates, the insurance industry needs predictability. This is why insurers are increasingly turning to AI and machine learning to more easily predict the future and the risks that the future may hold for their clients.

For example, AI can help insurance companies gain enough information about changing weather patterns to keep premiums low, by analyzing historical weather data and future weather predictions.

Another example is the price of behavior policies. Instead of tailoring a policy based on information provided by a customer in the app, policies can be tailored using personalized data from devices like vehicles and fitness trackers. This leads to personalized pricing, meaning safer drivers and those leading healthier lifestyles will be offered reduced premiums for car insurance and health or life insurance, respectively.

Essentially, AI allows insurance companies to rely on near real-time event-based predictions using large data sets, rather than a statistical display of past performance.

It can also help financial companies comply with the ever-changing regulations to which they are subject. Machine learning algorithms can quickly read and learn from regulatory documents to detect the correlation between certain actions and compliance, which in turn enables anomaly detection. Machine learning algorithms can also be used in the prevention of fraudulent claims by identifying features that distinguish them from legitimate claims.

Why AI and ML will transform the insurance industry

As the industry prepares to enter 2022 and the new decade, operators are investing in the capabilities that will help them thrive in an increasingly fast-paced, data-driven marketplace. The main one of those capacities? Artificial intelligence (AI) and machine learning (ML).

Read: Cost to develop on demand a Financial App

Highlights from the insurance research report include:

The majority of respondents (62%) work at carriers that are implementing, testing, and planning AI and ML projects and are already seeing benefits from their investments.

The majority of respondents (75%) believe that AI and ML can provide operators with a competitive advantage.

Users face four key challenges around AI and ML:

Financial: the cost of implementation, uncertainty around ROI, and competing priorities.

Staffing: The growing challenges of attracting and retaining data scientists when this skill set is in high demand in nearly every data-driven industry.

Data — especially the operational complexities of managing data volume, security and quality as operators move from single-source to multi-source solutions.

Compliance: Increased regulatory scrutiny and the challenges of discerning between new, legitimate data sources and sources that are actually proxies for sensitive or prohibited data.

Today, AI Applications and ML Applications capabilities can provide an incremental boost. Ultimately, operators that can successfully put AI and ML to work will be better positioned to achieve ultimate competitive advantage. The survey results can help operators mitigate gaps in their capabilities and shed light on investments that can drive their AI and ML initiatives.

In conclusion

Throughout this blog, we have discussed the personal and commercial insurance sectors, highlighting potential use cases for ML and AI. Having discussed dynamic pricing, claims management, fraud detection, crash management, recommendation engines, and automated underwriting, we can conclude that;

1) ML & AI is critical to establishing a dynamic pricing strategy and capability that enables insurance companies to remain competitive in an increasingly competitive marketplace.

2) ML & AI can streamline business processes and introduce cost and time savings in the claims management domain.

3) The underwriting process can become highly automated and more transparent, with the effective use of ML and AI to drive enterprise risk management practices.

4) Aligning technology choices and implementation strategies across pricing, underwriting, and customer acquisition can shorten the feedback loop for rich customer segmentation. This can drive further revenue growth for companies if implemented effectively.

5) Customer demands, especially in the personal lines industry, require companies to alter their digital experiences. In doing so, intelligent document ingestion and RPA can be used to effectively streamline the claims handling process.



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