Data & Analytics

10:03 AM
John Anderson and Carl Sherzer, IBM
John Anderson and Carl Sherzer, IBM

Unlocking the Potential of Big Data for Underwriting

In the new underwriting paradigm, the differentiators are those who are pushing hard to get the information they have to the underwriting process.

Big data and analytics are the sentinel change agents in an insurance environment that is being impacted by radical shifts in how underwriters assess risk and how consumers buy insurance. In fact, the combination of the two is precipitating a big shift in the traditional underwriting role, while leveling the competitive playing field.

The systematic integration of these emerging variables is enabling insurers to address the following challenges:

1. Manage the collection of data

2. Integrate data insights into the underwriting process

3. Utilize data to drive innovation in the underwriting process

4. Fund underwriting improvements

In the past, we would hear from insurers who would question the value return from big data. However, the answers are becoming more apparent and we are excited about the implications.

A new IBM study reports that a steadily growing number of organizations – more than two-thirds – across all sectors are applying big data and analytics to support revenue-generation strategies. We are observing leading insurance carriers who are integrating data within their underwriting operations – using it to drive improvements in sales, marketing, underwriting precision and to leverage more from the claim adjustment process.

One carrier is profiling submissions and then applying business rules that guide the underwriter on prioritization through; 'fit to appetite', opportunity for success (predicted hit ratio), and contribution to enterprise growth objectives. Another carrier is using content management and analytics to push examples of successful deals to the underwriter (match to current submission) thereby increasing the consistency of their quotes and optimizing the hit ratio. On the claims side, we are seeing that trends in claim activity are more quickly recognized, which are then available to push as 'informational alerts' to underwriters when the risk they are working on may be similar.

[How SPARTA automated underwriting controls]

Carriers are also delivering insights derived from their data to improve operational performance in other ways, as well. For example, underwriters are provided real time personal metrics, reminded of key sales points in making proposals, and given the ability to incorporate predictive risk scores to balance the risk profile of their book.

One of the most compelling reasons for getting the data into the hands of the underwriter has come from delivering greater consistency in the pricing. With a more precise and predicatively priced model the underwriter has a tool to see where in the range of pricing a risk resides. They can determine how this affects the profile of the overall book and are better equipped to know the "walk-away" or "get aggressive" price points.

Delivering data insight as it is needed is also creating a solution for those companies who are less experienced in particular lines of business. Data and analytics incorporates risk profiling to understand the current submission then applies enterprise insight on the associated risks, and can push that knowledge to the underwriter. Additionally, the carrier is able to identify available expertise within the company (for consult on line), provide expert underwriting guidance through smart interrogatories, and access to examples of sustainable solutions (best practice underwriting from similar risk examples).

We are also observing tools that can offer real time price trends, micro segmentation, exposure concentrations, reinsurance alternatives and predictive alerts. The sophisticated underwriter is learning how to channel this new "guidance", not as a way to avoid risk, but as a path to craft profitable solutions. Further, the intellectual capital developed over the years through traditional underwriting methods is being "mined" such that the empirical experience of the expert underwriter is not lost in the new underwriting model.

[MetLife gets the big data band together]

We identified common challenges that insurers are dealing with when it comes to integrating data with the underwriting process. Some insurers are combating these challenges with the creative approaches described above, while many others are still struggling with their strategy and how to integrate the information within their process.

One thing is clear: In the new underwriting paradigm, the differentiators are those who are pushing hard to get the information they have to the underwriting process and tactical application of their operational execution. In this way, they are beginning to provide the necessary investment return.

About the authors: John C. Anderson and Carl Sherzer are Senior Management Consultants in IBM Global Business Services' Insurance Strategy and Transformation Practice. They can be reached at and

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Nathan Golia
Nathan Golia,
User Rank: Author
1/11/2014 | 3:02:27 PM
re: Unlocking the Potential of Big Data for Underwriting
Thanks John. Interesting that you talk about mixing segments Gă÷ some people I've talked to have said that all this data and the personalization it leads to could actually lead to too-customized selection and the concept of the pool could fall through. I much prefer the other option, where new data sources help drive prices down for drivers that had been considered more risky in the past.
User Rank: Apprentice
1/11/2014 | 3:01:18 PM
re: Unlocking the Potential of Big Data for Underwriting
Nice article Carl and John! Well done! I hope you both are doing well!!!
- Bruce F. Broussard, Jr.
User Rank: Moderator
1/8/2014 | 3:23:26 PM
re: Unlocking the Potential of Big Data for Underwriting
Nathan, your question is intriguing and creates
multiple discussion opportunities. From my perspective, the use of big data to
develop precision pricing can now introduces a re-think on segmentation.
Could this drive more market to non-standard? Hmmmm, yes G㢠I see that, but
maybe there is another possible direction?

Precision pricing will become a part of all segments (standard and non-standard) which will drive down the price to exposure rates. Meanwhile,
competition for premium dollar may increase as carriers are able to calculate
and mix segments (even the previously considered non-standard) with greater
accuracy. The previously eschewed non-standard becomes accepted into the basic portfolio.

Of course competitive markets may attempt to continue the 'cherry pick' for the most predictable but notionally, can they survive at an ever decreasing margin or will the servicing and administration swamp their profit model?

Several additional interesting concepts:
1) Can a much higher level of predictability create an opportunity for
securitization of books of business...
2) As more competitors avail themselves of these analytic opportunities,
will it hasten commoditization of the industry, which in turn fosters
services as differentiated advantages...

3) In another direction, with risk now managed to a more
precise price point, will combining it with other financial services may
finally take holdGă¬

...but I start to talk crazy, sometimes...
Nathan Golia
Nathan Golia,
User Rank: Author
1/7/2014 | 5:16:29 PM
re: Unlocking the Potential of Big Data for Underwriting
Thank you gentlemen. Underwriting clearly benefits from big data initiatives. I think better pricing can go a long way in helping insurers attract the kind of risks they want. I wonder how that would affect the non-standard market, which stands to gain some more customers if some previously "standard" risks didn't hold up to greater scrutiny or if their customers turned out to be better risks than anticipated under old models.
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