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.
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.
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.
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.
In order for regional insurance carriers to continue to grow, the acquisition of new customers and retaining existing customers is imperative. Because of this, identifying cross-sell and upsell opportunities has never been more critical. One of the significant challenges carriers face when growing their company is analyzing and managing the capabilities of their agent distribution channel.
I recently attended my first InsurTech Connect conference in Las Vegas a few weeks ago. Aureus has been attending and exhibiting at this conference, so I thought I knew what to expect. What surprised me the most about ITC 2019 was the sheer volume of people and the efficiency with which it was handled. I was impressed with how well organized it was. ITC's mobile app was a great way to encourage networking. The app enabled me to speak with over 150 attendees from insurance companies, agencies, brokers, and other technology providers. From these conversations, there were three topics that seemed to be on everyone’s mind this year.
The use of social media is growing at a steady rate, and with that, the adoption of predictive analytics is on the rise as well.
RPA is Changing Lives The digital marketplace is extremely challenging. Businesses must stay ahead of the game by being innovative and creative to continually provide automated processes that will improve the customer experience. Robotic Process Automation (RPA) and Digital Process Automation (DPA) are the front runners by being one of the fastest growing segments of business technology these days. Globally, the RPA market size was valued at USD 597.5 million in 2018 and is expected to register a compound annual growth rate of 31.1% between 2019 - 2025.
With the artificial intelligence (AI) technology that exists today, an AI-enabled single view of the customer provides insurers with an opportunity to improve the customer experience. In the previous blog article “The Importance of Having a Single View of Your Customer in 2019,” we discussed a few of ways this can be accomplished.
When William Shakespeare wrote “To be or not to be” for Prince Hamlet to speak and express his contemplation for embracing the universal truth; little did he know that he would be quoted in various different contexts for different types of effects. A coward soldier saying; “to flee or not to flee”; a conniving trader evaluating an unsuspecting customer; “to fleece or not to fleece”; the colonial masters strategizing their exit; “to free or not to free”. And as guessed by you; a data scientist upon stumbling on a couple of interesting variables; “to correlate or not to correlate”.
The first step in predictive modeling is defining the problem. Once done, historical data is identified, and the analytics team can now begin the actual work of model development. In this blog, we touch on the business factors that influence model development. If you find this interesting and want a deeper dive, you’ll have the opportunity to download our whitepaper that goes into more detail on this topic.