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Powering Insurance Agents with AI

In the post-CoVID-19 era, a tremendous amount of focus, time, and energy has been invested in understanding the customer or policyholder based on the insurer's proprietary data and the data collected by many other research agencies. The customer is deeply analyzed and offered customized or personalization, as we call, solutions and offerings. However, the investment made by insurers has only been focused on 5% of the number of policies sold or approximately 11 Lacs policyholders, that are sold by direct sales, web aggregators, and online sales. This begs the question, why are insurers only investing in distribution channels that represent 5% of the number of policies sold?

Insights That Power Insurance

While the world around us is changing so rapidly, adapting to this change is no longer an option but a necessity to survive. The Indian Insurance Industry, too, as a whole, has proven its resilience in the current dynamic context but nevertheless continues to face various challenges to be dealt with as it navigates through this evolving landscape. As innovators, we at Aureus Analytics felt the urge to aid these giant strides with a little insight from the who's who of the industry. Thus, came about the metamorphosis of 'Aureus Insights' from a weekly blog to a periodically published yearbook in India that encapsulates this changing trend in the inaugural edition, the AI landscape, and a bird's eye view of what we can expect on the road ahead.   

Fighting Insurance Fraud the Smart Way

The insurance industry in India is expected to reach US $280 billion by 2020-2021. The life insurance industry is expected to increase by 14-15% annually during the next three to five years. With such rapid growth in the life insurance market, the number of fraud casess is also expected to increase. Recent reports suggest that fraud consume more than 8.5% of the revenue that the industry generates.

How Artificial Neural Networks Unlock Insights from Unstructured Data

One of the challenges faced by data scientists is dealing with unstructured data using traditional machine learning models. These models are trained on structured data that have input features with corresponding output labels. When using unstructured data, the data cannot be directly used as an input feature. One approach is to use Artificial Neural Networks (ANN) to unlock business insights from unstructured data.

Technology is Key to Surviving in the Modern Insurance Market

Digital is the key player in this time of the Covid-19 pandemic. Insurance companies are embracing digital channels now more than ever to reach out and connect with current and prospective customers. Covid-19 has changed the public’s outlook on insurance. Its value is perceived to be more critical as folks are considering the consequences of not being covered. Because of this, it’s essential now more than ever to provide consumers the ability to research products online without the assistance of agents, and at the convenience of their own home. Insurance companies must provide the tools and means to analyze various insurance products, provide price transparency, and a clean digital environment that is easy to navigate and execute.

Data-Driven Insurance: The Challenges Faced by Data Scientists

In my earlier blog, “Data-Driven Insurance: The New Normal in the Post-Pandemic World,” I concluded that in the future, the insurance industry would be data-driven. Not only data-driven, but we’ll see the use of emerging technologies like Artificial Intelligence, Image Recognition, and Natural Language Processing BOTs will be the buzz words in the corridors of the Insurance industry.

Data-Driven Insurance: The New Normal in the Post-Pandemic World

This is the age of data. This pandemic has forced us to find new ways to get our work done without putting ourselves in danger. Consumer buying behaviours have changed in order to adjust to this new normal in the post-pandemic world.  Before we talk about the changes in the business to navigate in the next two years, we should have a glimpse of the insurance business: What, How, and Why of the insurance business.

Understanding Agency Sentiment

In our previous blog article, “Using AI for Increasing Agent Productivity,” we discussed how many insurance companies can only analyze agent productivity based on the premiums written and the loss ratio of their network of independent agencies. In part 2 of our series of articles on “The Top 3 Emerging Trends for Agent/Advisor Analytics Using AI”,  we will focus on the benefits of understanding agency sentiment for insurance companies that utilize a network of independent agencies.

Using AI for Increasing Agent Productivity

Currently, many insurance carriers can only analyze agent productivity based on the premiums written and the loss ratio of their network of independent agencies. Looking only at past results doesn’t necessarily provide an accurate view of how an insurance carrier can increase agent productivity going forward. By using AI for increasing agency productivity, insurers can now predict the best course of action as opposed to waiting to review past results.

AI Lessons From a Mind Master and a Grandmaster

Chess and similar games have always been used to measure the “intelligence” of machines. Chess grandmasters have always seen an able sparring partner in a good chess engine running on a capable computer. The positional evaluation, which comes by intuition and is honed and sharpened by unforgiving hours of grueling practice, can be expressed as a set of mathematical models that fast computers can use to create gameplay.

How to Structure the Sentiment Analysis Process for Insurance Data

Sentiment reveals a lot about what customers think about an insurance brand, including how well customer representatives are resolving issues and how happy customers are with the underwriting process. This is where the sentiment analysis of structured and unstructured data can help insurers understand how their customers are feeling.

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