Teamwork

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By: Ted Ledbetter

As of April of 2024, I’ve been at Finys for 12 years. When I first came here, there were only about 20 people in the company. I was 19. I wasn’t even old enough to drink … legally. I still had two years left of college to finish. I joined shortly after a friend of mine who also worked at Finys recommended I apply since he enjoyed the work. When I came in for my interview with Kurt Diederich, the president and CEO, they had just moved into the office we’re in now. It was a Saturday. Kurt was painting the break room walls. The conversation went something like, “When can you start?”

We’ve certainly grown over the past 12 years, but we still have a small-company vibe. We have a very flat organization. Kurt’s here every day. His door is always open. In that way — and by his example — the company is really good at cultivating leaders. It takes a little time, but all of us who get to leadership roles are expected to mentor others who are coming up. In fact, we don’t even use the term “manager” in the company. It’s more a matter of earning leadership roles by contributing in our own ways, then allowing the members of the teams we lead to contribute in their own ways and to demonstrate their aptitudes and capabilities.

I started in QA, which is where most of us started back in the day. I did that for three months before moving on to development. The person who was the project lead for Virginia Farm Bureau (VFB), our largest client at the time, left the company. Another person was assigned to be the VFB project lead. That didn’t work out. Then I was appointed the VFB project lead after almost a year on the job. It was very much a sink-or-swim type thing, but that’s just how we do things here. The work each of us does actually matters, and the work we do is very quickly put to use. Code I wrote when I first started is still being used by thousands of agents in Virginia every day. We’re given all the help we need, even as we’re given free rein to do things the way we want them done.

Believe it or not, the questions we ask most frequently here are like this: What are all the things we can do to make our clients’ lives easier by using Finys? What can we do to make their days go smoother? What can we do to make their things move faster so they can have an easier day? I think there are many companies where you might spend a week putting something in a backend tool that never gets used. For us, because of the way we go about things and communicate with our clients, those things get used every day, all day.

This also is a company that protects and cares about its people. You don’t get that if you’re working for a billion-dollar company, for a CEO who flies around in his jet plane. Kurt is in the office every single day, more than anyone else. So, he cares the most. He doesn’t drive a Bentley. He’s not spending all the money out on his own private plane or helicopter or whatever, even though we joke that he lands the helicopter on the roof sometimes when the air conditioner kicks in. But he doesn’t.

We also like to say our clients aren’t our clients. They’re our partners. I know partner gets a lot of lip service. But our relationship with VFB isn’t a vendor-client relationship. We’re partners, and we’ve been working with them now for 14 years. I’ve been with them almost all 12 of my years here. We get along and interact with VFB employees just as well as we do any coworker in our office. I can call up anyone over there and talk to them the same way. It’s not a situation in which we’re compelled to do things one way because we’re the vendor. It doesn’t work that way.

By the same token, Kurt and the company have done a really good job of selecting our clients. Not just our employees but our clients. We work with some really good people. That makes our jobs easier. It makes their jobs easier. Everyone’s friendly and we work together so well. If that were different, so many of those relationships might’ve ended long ago. But because we have this partnership with everyone, it makes life so much easier.

Look Back to Look Ahead

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As artificial intelligence (AI) continues to establish a foothold in insurance, it’s important to remember two things: (1) AI can be invaluable in automating routine tasks to reduce the need for manual intervention. (2) The predictive value of AI lies in hindsight; that is, its ability to analyze data, to identify patterns, and to predict outcomes is retrospective.

Predictive AI combines machine learning, AI, and statistical models to identify relationships between variables and to make predictions. To cite a few examples, AI can:

  • Assess and manage risk more effectively by analyzing data from various sources, including social media, credit reports, and medical records.
  • Predict the likelihood of claims, enabling insurers to offer more accurate risk assessments and personalized premiums.
  • Automate claims processing, reducing the time and effort required to settle claims and improving accuracy.
  • Predict customer churn: By analyzing customer behavior and demographics, AI can identify patterns that indicate whether a customer is likely to switch to a competitor, allowing insurers to target them retention efforts.
  • Forecast sales: AI can analyze historical sales data, seasonality, and market trends to allow insurers to prioritize the sales or products or lines accordingly.
  • Detect anomalies: AI can identify unusual patterns in data, such as unusual policyholder behavior or rating abnormalities, to allow for swift detection and resolution.
  • Optimize processes: AI can analyze process data to identify bottlenecks and inefficiencies, enabling insurers to optimize workflows, reduce costs, and increase productivity.

Generative AI, on the other hand, creates new content, such as images, text, and other media, by learning from, aggregating, and adapting existing data patterns. While generative AI is valuable in creative fields and novel problem-solving, its predictive value is limited to generating new content rather than making predictions about future outcomes.

What’s In It For Insurance?

AI is flexing its muscles in the insurance industry, by improving capabilities like customer service, claims processing, underwriting, and fraud detection. Its ability to analyze large datasets and to process information in specifically programmed ways enables insurance companies to enhance customer service by providing personalized policies and improving communication. AI-powered chatbots and virtual assistants can provide 24/7 customer support, answer common questions, and help customers with policy-related inquiries. AI can streamline or eliminate manual, time-consuming tasks; improve the accuracy of rating and underwriting; and bind policies faster. It can aid in policy comparisons, ensuring policyholders get the most suitable products for their needs. And it can help detect and prevent fraud by analyzing patterns and identifying suspicious behavior.

As AI continues to evolve, we can expect it to contribute more significantly to improvements in the insurance industry. Since it relies on data (past) to enable its predictive capabilities (future), it will always look back to look ahead.

In an industry that measures its success retrospectively (based on claims experience), that’s not a bad way to go.

Follow the Money

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Because we’re as modest as we are, we don’t like to brag. (But we will if we have to. 😉) We bring that up here because we were doing some research on the percentage of annual revenue some companies put back into their products. What we found suggested that in industries with long innovation cycles — and in which products are more standardized — companies may allocate five percent or less of their annual revenue for their products. Along with not bragging, we’re not inclined to say we’re the most innovative company on the planet. But we invest 22 percent of our annual revenue into our product.

Some of that may be attributable to the fact that we’re dedicated to serving U.S.-based property/casualty insurance companies only, which spares us some expenses other companies might incur. As examples:

  • We don’t have to produce materials or product versions that are multi-lingual or multi-currency.
  • We don’t have to build interfaces for foreign, third-party data sources.
  • We don’t have to address varying cultural nuances and business practices.
  • We can focus our efforts, allocate resources, and tailor our marketing, sales, and product-development efforts to one market.
  • We can simplify our compliance with laws, regulations, and even data formats.
  • We can establish and maintain deeper relationships with other U.S.-based companies, industry associations, and government agencies.
  • We can provide customer support tailored to U.S. time zones and business hours.
  • We can better target marketing messages and campaigns to U.S. businesses.
  • We can prioritize the product features and enhancements we develop to ensure they’re most relevant to the U.S. market.
  • We can enjoy relative economic stability as opposed to being exposed to the economic volatility of other global regions.
Money Isn’t Everything

That statement is certainly true. But money can be very influential. In our case, being able to put almost a quarter of our annual revenue back into our product-development efforts gives us discernible value over some of our competitors. And the corollary is that we’re investing that revenue for the benefit of our customers, who can be sure our software is as good as it can be, for the market we choose to serve, year after year.

No. Money isn’t everything. But if you follow it, you can learn some pretty interesting things.