Pinpointing the Total Cost of Risk

How analytics are helping Family Dollar identify and reduce liabilities.

April 26, 2013 Photo
Staring at the image on my laptop at 7:00 a.m. on Oct. 27, 2012, I tried to take in the magnitude of the risk and opportunity I was seeing as I prepared to lead Family Dollar’s next hurricane conference call.

On the screen was a map of the Eastern Seaboard that extended from Florida to Maine and west to the Mississippi River. Overlaying the map was NOAA’s latest hurricane strike zone projection. Hurricane Sandy was centered 335 miles southeast of Charleston, S.C., having just become a Category 1 hurricane with sustained winds of 75 mph that extended 175 miles from the eye. Tropical force winds extended even further, reaching 485 miles from Sandy’s center.

At that moment, Sandy was moving at a speed of 11 mph, and meteorologists were predicting it to strike near Atlantic City, N.J., in less than 48 hours with sustained winds approaching 90 mph. The storm was then supposed to move inland, first to Pennsylvania then New York over the next two-to-three days.

My focus, however, was on the 771 red and blue dots covering the map on my screen, which represented Family Dollar store locations directly in Sandy’s cross hairs. The blue dots highlighted our Tier 1 coastal store locations; the red indicated our inland stores. The situation, although dire, presented some opportunities, too.

The risks were obvious. There was a high potential for losses associated with damage to our stores and merchandise, as well as the massive power outages that would certainly interfere with their ability to remain open. But there also was the opportunity to utilize the data on my screen. All of those red and blue dots represented stores that would have customers in great need of food and materials to assist in weathering and recovering from the storm. By using this analytical information, we could offset loss costs through sales of supplies. Here are some ways the data was used:

  • Analyzing weather data
  • Identifying and tracking store inventory levels
  • Monitoring and capturing regional power grid data
  • Mapping the strike-zone to manage and support stores
  • Identifying high-risk areas to manage team member and customer safety better
  • Providing trauma crisis counselors for our team members in need
  • Determining logistics for product deployment to stores
  • Identifying expected loss estimates in order to stage and deploy adjusters, personnel, and equipment

Although focused on property risk, this real-world Sandy scenario reflects one dimension of an effective risk management analytics program. Significant challenges also are posed in the areas of workers’ compensation and general liability risk analytics. So the question is this: What analytical tools are available in your organization to mitigate and maximize a risk?

Analytics – The Scorecard

Risk management’s interaction with the C-Suite generally ends with the CFO and, as with many things financial, comes down to the impact property and casualty losses have on the corporate bottom line. The ability of a risk management program to identify, manage, and reduce the liabilities to the bottom line is critical. A key diagnostic and performance management tool is scorecard analytics.

An effective corporate risk management analytics scorecard is a monthly snapshot of key loss performance indicators (KPIs) and is validated by the ability to monitor and manage the metrics directly impacting loss development projection results reported in an independent actuarial analysis.

The actuarial analysis is a picture of the total ultimate casualty loss costs, which typically are limited to workers’ compensation, general liability, and auto liability, although it also can incorporate property loss experience and be used to book the corporate bottom line balance sheet liabilities. In the case of publicly held companies, the Securities and Exchange Commission (SEC) requires that these liabilities be stated quarterly in a 10-Q filing and annually in a 10-K.

To be a meaningful and actionable tool for a risk management department, the actuarial analysis should be completed quarterly. Simply conducting this analysis annually will not be frequent enough to alert risk management teams to adverse developments in time to aggressively determine root causes of negative trends and manage a timely correction so that the enterprise does not incur unexpected balance sheet liabilities.

Additionally, the actuarial report is a direct, objective, and independent reflection of the effectiveness of corporate safety and claims management programs over time.

Primary and key scorecard analytics that can be implemented in any program, even without sophisticated risk management information tools that will better manage loss outcomes and, ultimately, the actuarial impact to the corporate bottom line can be focused on four key categories: 1) Total Cost of Risk; 2) Workers’ Compensation; 3) General Liability; and 4). Property.

TCOR and Loss Analytics Scorecarding

Basic risk management analytics start with the measurement of the total cost of risk (TCOR). TCOR may be basic or highly complex, depending on how far a risk management program has evolved or matured.

Risk management programs will vary from company to company and are greatly influenced by corporate size, degree of risk, and loss experience. Therefore, a risk management program may take a very basic risk-transfer approach by simply purchasing insurance. Or it may take an increasingly sophisticated, integrated risk management approach that incorporates aggressive claims management programs with loss avoidance programs that are managed through robust analytics, while retaining risk and purchasing insurance as a safety net. The ultimate state of a highly developed and sophisticated risk management program is evidenced in an enterprise risk management (ERM) program, a financially driven risk management strategy that identifies and manages risk within all critical departments of the enterprise utilizing the combined expertise of the risk management and internal audit departments.

The majority of companies have risk transfer or integrated risk programs; for these organizations,  a basic approach to TCOR analytics will suffice.

For the basic approach, TCOR is divided up into three categories: 1) Enterprise-wide casualty premium costs (WC, GL, AL, umbrella, aviation, property, D&O, EPL); 2) Administrative costs (TPA, broker, special programs such as spill response, safety, return to work); and 3) Claims costs (WC, GL, AL, property). By focusing on these three components of TCOR, corporate risk management departments can be very successful in driving year-over-year TCOR reduction.

The largest component of TCOR will generally be claims costs followed by insurance premium costs and then administrative costs. As a result, risk analytics are mostly focused on WC, GL, and property loss analytics.

Analytics at Family Dollar

TCOR must be a KPI of risk management program success. An effective risk management program will result in a reduction in TCOR and can be measured in a flat, quarter-over-quarter and/or year-over-year reduction in expense. However, unless the company is a zero-growth company year over year, the value of this type of measurement is questionable. Most companies are dynamic; they are either growing or downsizing. As a result, the degree of risk changes accordingly. The truly meaningful scorecard measurement of TCOR is founded on a rate basis that is not influenced by business growth.

For example, in a high-growth company driven by growth in sales, TCOR can be measured at a rate of cost of risk per $1,000 of net sales. As a result, the KPI would demonstrate risk management program success if the TCOR declined by five percent year over year, demonstrating cost reduction of $.50 in that TCOR declined from $10 per $1,000 of sales last year to $9.50 current year.

In addition to TCOR, there are three critical analytical components used to support corporate intelligence while driving and managing adverse impact to the corporate bottom line: 1) Financial; 2) Scorecard; and 3) Diagnostic.   

Financial Analytics are those analytics identified to report: 1). statutorily required information, 2). internal corporate accounting information for dollars managed by the risk management department and that impact the corporate balance sheet such as loss and administrative expenses, financial  forecasting, budgeting, Sarbanes-Oxley compliance, 3). analytics and internal financial data that need to be shared with external partners to generate actuarial studies, generate accurate claims and loss performance data such as loss runs.

Risk Management Financial Analytics are fairly routine and standard in all organizations but will vary in detail and sophistication based on the size and maturity of the enterprise. The true actionable analytics are the scorecard and the diagnostic analytics.

Scorecard Analytics are customized and often revised for monitoring and managing program performance. Scorecards are generated on a regular schedule (weekly, quarterly, monthly annually) and may be shared with business operations partners to report performance and drive behavior.

It is essential that scorecard analytics are developed through close collaboration between Risk Management and the corporation’s third party claims administrator (TPA), to ensure that claims performance is measured and managed in a number of ways.

Ultimately the scorecard provides a monthly KPI roll in a one page monthly executive summary.  These scorecard metrics are a combination of metrics that reflect industry standards, can be benchmarked against the TPA’s book of business and are metrics specific to corporate risk management initiatives.

Workers’ compensation analytics, at the minimum, should be broken down into four areas of measurable metrics:  1). Total Claim Metrics, 2).Indemnity Metrics, 3). Medical Metrics, 4). Expense Metrics.

Total Claim Metrics. Claims frequency by claim type (Incident Only v. Medical Only v. Indemnity) – measures frequency and severity and effectiveness of safety programs, ratio of medical only claims to indemnity claims – measures control of claim severity i.e. benchmark goal: no more than 20 percent indemnity to 80 percent medical-only, year to year or month to month claims closing rates while also measuring reopening rates – measures claim resolution; benchmark goal: 100 percent closure rate or better with zero reopening resulting in a zero rate of claims pending growth, average paid claims; claims close aggressively without increasing average claims cost, aged pending; goal: claims should be open no more than a predetermined number of days, number of days claims remain open on average; goal: reduce the average number of days, Indemnity expense v. medical expense v. allocated loss adjustment expense (ALAE); goal: reduction of indemnity and medical expense while not increasing ALAE or subrogation recoveries.

A successful program results when total claims scorecard metrics demonstrate reduction or no growth in the average claims costs while reducing claim frequency and total pending.

Indemnity Metrics. Average number of lost time days per claim, average daily TTD expense, TTD paid per claim, Total TTD days lost, Average paid TTD per claims. These scorecard metrics measure the ROI of a successful return to work program.

Average indemnity reserve per claim, average indemnity paid per claim, average number of indemnity reserve changes, average indemnity settlement. These scorecard metrics measure reserve adequacy and effectiveness of program reserve strategy.

Medical Metrics. Claims medically triaged, cases referred to telephonic case management (TCM), open telephonic case management (TCM) inventory, average length cases are open in TCM, average TCM expense, cases referred for UR (medical utilization review), procedures reviewed and percent of those cases approved, negotiated or denied, medical savings per claim, bill review savings (out of network & in network), average pharmacy expense per claim, pharmacy expense, percentage of cases with MSAs, average MSA cost.

These types of medical scorecard metrics measure the effectiveness of a workers’ compensation managed care program.

Expense Metrics. Some examples of scorecard expense metrics include: average legal expense per claim, average bill review expense per claim, independent investigative expense, SIU expense.

In the areas of GL, AL and Litigation scorecard metrics there is much overlap with the WC metrics already discussed, however, some metrics specific to GL, AL and litigation:

Of particular importance in the GL/AL category is early claims resolution in an effort to avoid attorney representation on claims and claims transitioning to suit. Key metrics to evaluate and manage resolution rests in metrics focused on average number of days claims remain open, claims conversion rates (converting from non-suit to suit), Resolution ratios and average claim payment. Scorecard metrics should drive improvement in these KPI’s.

Key litigation scorecard metrics should be focused to drive a reduction of litigation rates, a reduction of legal expense as well as a reduction of ultimate settlement costs. Litigation rates, suit resolution rates, pending rates should be measured month over month and year over year; average lawsuit life, rates and success of alternative dispute resolution (ADR) are some examples of litigation KPI’s.

Diagnostic Analytics are analytics that are ever changing and used to drill down on a specific area or issue identified as a result of adverse and even positive trending evidenced in the scorecard analytics. These analytics can be both regularly scheduled reporting for a period of time or “one off” in nature and are proactive and reactive; driven by identifying trends and seeking root causes.

Diagnostic analytics are also developed to measure performance of strategic initiatives. Diagnostic Analytics are the trait of a risk and claims management program that is well developed, is aggressive, inquisitive and is in, or is approaching an ERM level of claims and risk management.

Diagnostic Analytics Workers’ Compensation Case Study

In 2007, Family Dollar’s monthly scorecard report showed that the total percent of medical spend on a workers’ compensation claim had risen to an all-time high. It was time to conduct a diagnostic deep dive to identify the cost drivers. Upon review we were surprised to learn that 21 percent of the total medical costs were comprised of pharmaceutical expenses. The industry benchmark in 2007 for pharmaceutical expense as a portion of total medical expense was 16 percent.

As a result, Family Dollar developed and implemented a program to aggressively manage pharmaceutical utilization and address abuse in workers’ compensation claims. Today the percent of Family Dollar’s pharmaceutical costs as a percent of the total medical spend is 11 percent while the industry benchmark has risen to 19 percent. This has also driven a positive impact on the total medical costs which have dropped to 37 percent of the total claim cost compared with the current industry benchmark of 60 percent.

The diagnostic approach has also ensured that our employees are getting proper health care, avoiding drug dependency, reduce expenses, and Family Dollar is now beating the industry benchmark by 42 percent in pharmaceutical expense and 38 percent in total medical expense. We continue to measure this result by way of the monthly scorecard.

Diagnostic Analytics General Liability Case Study:

In 2010, as a result of tracking accident rates and ranking by accident type and severity, Family Dollar initiated a strategy to address the top five most frequent accidents. Of the top five, the most frequent occurrence was slips and falls. Not a big surprise, as benchmarking in the retail industry clearly demonstrates that the number one customer accident in retail, both in the areas of frequency and severity, are a result of customers slipping and falling on the premises.

However, through a diagnostic approach using “deeper dive” analytics, we determined the specific causes of the slip and fall accidents. A significant number of these slips and falls occurred as a result of spilled products, such as detergents. Upon further investigation, we found that cleaning up these spills was a challenge in a store environment that has a limited number of team members. Cleaning up a liquid detergent spill with the traditional mop/bucket approach made the spill larger and soapier, requiring a significant amount of time to clean up while often leaving behind a soap residue. Therefore, the process took a team member 20 minutes to clean up, taking valuable time away from serving their customers.

These analytics demonstrated there needed to be a more efficient program to better the outcome. We worked with an external supplier specializing in spill response stations for stores, which included safety cones and an all-purpose spill cleanup chemical that immediately absorbs all spills and is simply swept up, taking less than two minutes to use. 

As a result, our stores are safer for our customers, our slip and fall loss rates and costs have declined, and implementation of the program resulted in a significant actuarial loss credit resulting in a spill response program ROI in excess of 250 percent.

Some Final Analytical Thoughts

It is easy to get caught up in the analytics for analytic's sake game, or the paralysis of analysis. A risk management department can get so enamored by analytics, generating report after report, looking at the same thing in different ways, or creating analytics so complex that that reports are not helpful. Yes, analytics and scorecards can be overdone.

A strong analytics program is useless unless they easily communicate information in such a manner that it is followed by action. The resulting action must include goals, be actionable, measurable, and manageable.

A RMIS system is a truly a significant asset, but not absolutely essential in the early development of an analytics program, as long as your TPA has a robust claims system and an adept individual who will drive the development and ongoing development of an analytics program. This talent can be provided by the TPA or can be a member of the corporate risk management staff, or both.

Family Dollar’s risk analytics program has been extremely successful due to the fact that Family Dollar has a manager of risk analytics, our TPA, Sedgwick, has an extremely robust claims system, and our TPA provides a designated analyst to Family Dollar.

Family Dollar’s analytics and scorecard program has been instrumental in significant reduction actuarial ultimate loss projections and reduction in corporate TCOR. As a result, the ROI of Family Dollar’s risk management program has validated purchasing an RMIS system for continued improvement and supporting program evolution to the ERM stage.

About The Authors
David Smith

David Smith is divisional vice president of risk management for Family Dollar Stores, Inc. He has been a CLM Fellow since 2011. 

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