AI in Insurance: Empowering Actuaries and Underwriters

How AI in Insurance Revolutionizes Actuaries and Underwriters

In insurance practice, as with any other business, more data means we can make better decisions. However, crunching the massive facts and figures to extract information calls for skills beyond human ability. Many companies have turned to artificial intelligence to solve business problems. The move has led to impressive transformations across multiple industries, the most prominent being healthcare, insurance, marketing, automotive, and finance. In insurance underwriting, artificial intelligence and machine learning are already replacing traditional modeling with more precise systems.

What Does AI Mean in Insurance?

Artificial intelligence represents technological tools capable of performing tasks that would normally require human intellect. Computer engineers model human problem-solving and decision-making processes and create programs to perform these tasks. Deep learning and machine learning are subfields of AI that are used interchangeably.

Leaving no roles untouched, AI is redefining many aspects of underwriting practice, from customer-facing chatbots to back-office data processing. Every insurance executive needs to understand how these technologies work and their impact on business to navigate future disruptions. AI supports business in three aspects: process automation, business insights, and people engagement. Companies should prioritize the areas that bring the most returns and work on the rest incrementally if they want to maximize the benefits of AI.

Business Automation: Improving Underwriting with Artificial Intelligence

Process automation is one of the easiest to implement AI technologies. End-to-end automation streamlines mundane tasks that prove inefficient when done by humans. An example of process automation in life insurance is a self-service program that can read customer forms, evaluate the information and route it to the appropriate channel. Advanced automation programs can be used for background checks and other insurance operations, such as:

• Gathering financial data and processing it into reports, for example, quarterly claims ratios.
• Enable document sharing and tracking for a distributed workforce.
• Software that updates company records by extracting data from other sources like forms and emails.

Since computer programs can move more data between applications, insurers can improve turnaround times for claims processing, unlike before when employees had to do it manually. Increased speed translates to higher customer satisfaction rates, and eventually, more business. Data collection also becomes more accurate, helping the firm to comply with regulations.

Cognitive Engagement: Context AI Technology for Human Interactions

Cognitive engagement is showing steady growth as one of the main channels where customers interact with the firm. Machine learning made it possible for chatbots to process natural language, which makes it feel like users are engaging with humans on the other end. It enables insurance companies to offer 24-hour support on a broad range of queries. For example, the customer service team may answer some frequently asked questions and leave it to the chatbots to respond to clients. We foresee many underwriters adopting avatars with human-like features to create more realistic customer service experiences from AI.

The technology has not been without critics, some with largely valid concerns. It is hard for many of these apps to master the art of human interactions – like how to have unstructured conversations. Some bots may not know when to escalate issues to the next level. Picking up on this shortcoming, we have seen tech companies building cognitive systems that can route complex queries to human representatives. We’ve also changed to building document repositories from previously resolved cases. Deep learning technologies can match incoming queries with similar ones from the repository and provide quick solutions.

Business Insights: How Machine Learning Improves Insurance Underwriting

Machine learning plays a critical role in statistical analysis and data modeling, the mainstay of actuarial practice. Labeled the “development of thinking systems,” cognitive insight explains how machines can analyze and interpret massive data volumes. Since AI is data-centric, the results become more detailed and the models more precise. Historical policy data, for example, can predict future claims and inform the best possible pricing approach.

With great programming, technology could be able to play the role of an actuary. It could potentially advise on possible risks and their financial impacts based on evaluating statistical data. Newer versions of these technologies try to model the human thought process, helping them interpret audio and visual data. AI is already cutting the distance between insurers and customers by using chatbots to provide services quicker.

Mass customization is going to be a common feature in life and disability insurance. We’re slowly drifting from the era when policies used standardized prices for different categories of buyers. Machine learning technologies can compute individualized risk profiles from large data streams to provide more accurate pricing models. Automating the claims processes removes excessive human interventions, speeds up the processes, and raises customer satisfaction. Insurers will benefit from more accurate records and decreased fraudulent claims.

The pace at which AI systems evolve will have many impacts on the future of underwriting services. It will control how policies are drafted, how to navigate litigation risks and inform about better customer decisions. Since the data processed by AI tends to be more accurate, it will help executives make better underwriting decisions. For example, information on the risk exposure is more precise, helping them create reasonable pricing models.

How Will Context AI Transform the Insurance Industry In the Future?

Insurance companies need to exploit the capabilities offered by AI technologies. With machine learning, insurance underwriting services already have the upper hand. Some of the software programs need statistical experts, a skill which they already have in actuarial scientists. It will magnify their abilities by processing information from massive data in a short span.

As a business solely dependent on creating risk profiles of individuals, the insurance industry struggles to maintain fairness. The push to end scoring bias using AI has been a mixed bag of outcomes, with opponents arguing that it could increase cases of proxy discrimination. However, AI scoring is proving to be more objective compared to the human alternative. By capturing and feeding all the factors to a case, artificial intelligence calculates individualized outcomes.

Industry executives should be aware by now that machine learning capabilities in insurance underwriting services is no longer a futuristic idea. It is a present opportunity that, when executed right, will impact business in many aspects. Every insurer struggles with managing reasonable claim frequencies, cycle times, problem resolution rates, and client satisfaction metrics. AI presents the key to keeping all of these variables (and others) under control. To explore the immense benefits of technologies, get in touch with Pilotbird for a free demonstration.

Sources:

https://www.mckinsey.com/industries/financial-services/our-insights/insurance-2030-the-impact-of-ai-on-the-future-of-insurance

https://www.brookings.edu/research/how-artificial-intelligence-is-transforming-the-world/

https://www.technologyreview.com/2017/03/22/153072/customer-service-chatbots-are-about-to-become-frighteningly-realistic/

https://www.insurancethoughtleadership.com/how-ai-can-vanquish-bias/

https://towardsdatascience.com/how-are-insurance-companies-implementing-artificial-intelligence-ai-aaf845fce6a7