Your policy administration system needs a new job description

The very notion of what has always been the core operating requirement of an insurer — policy administration — is changing. Today’s systems are not your father’s idea of policy administration. Data has been knocking on the policy administration system (PAS) door for some time. Now there is an explosion of data and a growing amount of smart data from artificial intelligence (AI) and machine learning (ML). As a result, the venerable PAS is getting a new job description.

Until the very recent past, carriers did not place the “openness” requirement on their policy admin systems. But the market trend away from a product-centered toward a customer-centered approach that mirrors consumer experiences in other industries has sent insurers back to their drawing boards, and in most cases, into earnest discussion with their PAS supplier. The re-design calls for faster and better data collections and offer management.

There is a big top-line payoff for insurers. It means less likelihood that you will lose the prospect to competitors if he/she is shopping around because you are taking less time to collect data, less time to make an underwriting decision, and making a better product offer.

A new job description for PAS

New capabilities needed are the ability to accept more internal and external third-party data and the ability to do great things with it — such as make more personalized offer recommendations. The growing importance of these capabilities is writing a new evaluation criteria for insurers looking to purchase a PAS that is future-ready. PAS shoppers now need to  focus on how open the PAS architecture is to receive data, and what is the PAS ability to use that data to manage an offer life cycle and reduce underwriting process time.

Accepting data — in all its forms

It all begins with data collection. There is a lot of it and it comes in many, many forms — standard data, photos, social media posts, telematics data, structured data, geolocational data, geospatial data, weather data, flood zone data, hazard data, and personal financial data.

You will need to assess how a PAS platform can extract, import and synthesize these various forms without any limitations — without customization of the platform — in order to incorporate any data element into any core process.

A quicker, smarter data gathering process

The old way of data gathering was a serial process of multiple forms. The new way involves more sources and much more streamlined and dynamic interactions.

Instead of asking your customer or agent to do data entry, the process becomes more or less data validation, which is a lot faster, more efficient and — for the most part — more reliable. Critically, it is also less burden on the customer and turns what can feel like an inquisition into the mutually beneficial interaction it should be.

If additional data is needed, you can always collect it because “smart” is built into your process. Instead of hearing from the underwriter, “Based on your responses, please go back and ask these five more questions…,” you can infuse the form with the logic that the underwriter uses to ask those five questions and ask them at the same time.

The result is dynamic interaction with your customers and agents using a smarter process that knows you’re going to need this information two steps down the road, and right now you have an opportunity to collect it.

Furthermore, the interaction can be a guided conversation leveraging the smarts of a chatbot, which can be used in all of your data collection and presentation.

Start your recommendation engine

Then comes the offer. Today, insurers are mostly reactive: The customer tells us what they want and we offer what is asked for.  With the new model, driven by a newer generation recommendation engine, we can offer something much closer to the need based on an assessment of the prospect’s or customer’s profile, which considers such things as life stage, lifestyle, asset profile, and liability profile.

The opportunity for a simpler and faster upsell and/or cross sell is obvious. We can make an informed package offering that says “By consolidating your portfolio our offer is cheaper and better than alternatives.” We can also make a supplemental insurance offer that matches an identified lifestyle or life stage need.

By leveraging data analytics and augmenting human generated and maintained rules with ML and AI, the recommendation engine/recommendation service responds dynamically and helps create an efficient, event-driven and efficient offer life cycle.

Making an offer that can’t be refused

Tied inextricably to making an attractive offer using your well-informed recommendation engine, is the function of offer configuration itself. This lies at the heart of your PAS.

The successful and rapid interplay of data, AI/Ml, product recommendations, and the ability to easily configure and support an unlimited number of products, are hallmarks of a new generation of policy administration platform.  Your next-gen PAS has much more access to internal and external data and can write rules for real-time, event-based interactions.

Going forward, expect to see offer life cycle management become a discrete and sought after component of the PAS.  And you should expect it to provide a more flexible purchase flow and present and issue your offer in any channel efficiently.

Rowshi Pejooh (rpejooh@eisgroup.com) is executive vice president, Product Management at EIS Group. These opinions are the author’s own.

See also:

The 3 stages of the P&C insurance software system selection process