By MARTY ELLINGSWORTH, president of ISO Innovative Analytics, a business unit of ISO
It's an ongoing reality. As always, good underwriting is important to success, and growing the bottom line is paramount to sustainable competitive advantage in the property/casualty insurance marketplace. The vagaries of "hard" or "soft" underwriting cycles have never been more dangerous. The fabled "grow" and "shrink" strategies of cash-flow underwriting in carrier operations have been obliterated by a competitive analytical strategy that focuses on managing controllable risks while using investment opportunities to increase profits.
The most successful insurers make investments with profits from underwriting results--not from the borrowed time between collecting premiums from underpriced policies and their eventual claims payouts. Successful companies are analytically driven and use risk-based pricing as an efficient risk management and portfolio optimization process. They steer away from unintended triple-digit loss ratios while balancing opportunities and trends offered in the investment world. They also leverage line-of-business analytics upward into customer analytics and then into operational and portfolio analytics. These insurers continually strive to get a more refined understanding at each level of operational complexity.
As each component by line of business is segmented and resegmented for more accurate costing, the territories, cars, drivers, buildings, losses and financial histories--and even service and sales channels--should be evaluated and then analyzed together.
The goal is to be smarter in the marketplace and quicker to react to change as well as retention of business and likelihood of future cross-sell and up-sell campaigns. As analyses turn into actionable insights, they will then be used in operational and financial models that balance risk and reward to optimize expected returns on the use of capital. They will also have implications regarding the need for surplus to address future uncertainties.
The confidence gained from accurate knowledge about the cost of goods they sell is a crucial risk mitigation element and the key to reducing variations in business results for insurers. Business insight allows pricing flexibility to react to customer demands, competition, regulatory changes and macroeconomic realities. And pricing all risks offered by the sales channels accurately helps minimize risk.
A key driver in insurer financial results is the confidence born from assigning each risk a proper price. The highest possible return for shareholder value comes from making a legitimate profit on each customer segment. This is a repeatable phenomenon and essential to insurer growth and stability.
Insurers should begin by examining the risk itself, the people who own and operate it, and the surrounding community. Every component of the risk needs to be analyzed, cross-validated and reanalyzed, so that each risk can be accounted for as an ingredient in the overall book of business as it contributes to the carrier's portfolio of risk.
ESPECIALLY IN DOWN MARKET
This practice continues to be effective even in down markets. While traditional companies use conventional risk management tactics such as cutting costs, shrinking their books and waiting for the macro market to turn, analytically enabled companies are navigating a rough economic sea and actively positioning themselves to accelerate at the inevitable market turn by both tuning their portfolios for growth and looking to their competitors for weaknesses to exploit or for opportunities to make an acquisition.
More accurate understanding of risks at a granular level enables such proactive strategies and creates a more solid foundation for profitability in a flat market.
Deeper insight from analytics can also lead to hyperacquisition strategies where underperforming carriers may be gobbled up for their customer base.
In every underwriting cycle throughout history, certain companies survived and thrived in both the economic swell and trough if they heeded good underwriting discipline. Performance enhancements (investment gain schemes) are always opportunistic in the long run and should only be executed as part of a portfolio strategy in which the underlying book mix does not destroy value when favorable conditions wane.
Nor is it a viable business strategy merely to hold on and hope to ride out each storm until things get better. The "do nothing" strategy is doomed. The number of insurers using advanced analytics for proactive strategies is sufficient to ensure that the absorption of weak carriers by strong competitors is well under way.
SEGMENTATION, SEGMENTATION, SEGMENTATION
The best and most effective course of action to drive superior results comes from a high degree of certainty that what is predicted will actually occur. In the traditional sense, "location, location, location" is the common reprise for the financial potential and desirability of property investments and their perpetual risk.
In the new world of predictive modeling, executives reduce the risk in their portfolios through "segmentation, segmentation, segmentation"--the recursive and persistent focusing on reducing the uncertainty about cost of goods and operations.
A productive segmentation strategy uses more granular levels of analysis and exploits newer and deeper levels of valuable information coming from within the existing information silos of carriers, or from stores of third-party scoring solutions--and even from new third-party data attributes such as pictographic and multimedia material, or geospatial, climatologic and atmospheric information.
Insurers can now better leverage new data attributes in conjunction with smaller geographies to develop estimated loss costs for noncatastrophe exposures. Predictive models can take information about what is on the ground in these areas and build more refined and more accurate estimates of losses.
In the attached graphic, Table 1 shows the level of pricing sophistication available from using newer territories at a countrywide level of impact, while Figure 1 visually illustrates the potential impact of advanced segmentation.
The exhibits show how a change in the modeling approach for the level of analysis can dramatically improve the opportunity to create new segmentation within an existing framework of analysis.By linking and modeling data at a more granular level of analysis, new and vast amounts of customer data available today can be coupled with geospatial and predictive analytic tools to create highly granular local geographical segments.
The effect is to exploit the variety of important information buried in large stockpiles of customer information and to reduce the uncertainty of estimating loss costs by territorial definitions.This same playbook for progress can be applied to every level of data aggregation in the analytic value chain, often challenging traditional risk-assessment methodologies.
These statistically accurate predictions can further segment loss costs in the territories used by today's most prominent carriers. Forward-thinking companies that adopt this new variation on an old theme around territory will have a distinct advantage in the insurance marketplace to surpass the competition.
As has always been true in property/casualty insurance, and as the last 30 years have proven in earnest, the strongest businesses grow healthier by employing ever more intelligent data acquisition and predictive analytic strategies across the enterprise.
There is no room for complacency. In today's insurance marketplace, as rapid innovations in analytic technologies for better data, better models and better decision support continue to revolutionize the concept of what it takes to survive and thrive. In fact, the deeper business perspectives drawn from advances in data availability, data quality and modeling sophistication can produce leapfrog effects in competitive growth and sustainability,
Exploiting more granular levels of analysis and leveraging both the main effects plus the higher order interactions between old and new variables combined will wring the most insights possible from modeling efforts.As new data sources and categories of risk emerge, property/casualty insurers must adopt new strategies to stay ahead or watch as innovative competitors leave them behind.
January 1, 2010
Copyright 2010© LRP Publications