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Data is king in the enterprise, so it’s no wonder business analytics remains one of the top disruptive technologies today.
Analytics platforms give organizations insights so they can make better business predictions. Big data and analytics software is on a growth trajectory. The market is expected to grow at a five-year compound annual growth rate (CAGR) of 13.2 percent through 2022, according to research firm
Not only should organizations be taking stock of the data they are creating and capturing, but they should be applying “novel analytics” and developing unique data that can be monetized, advises the International Institute for Analytics in its report on Predictions and Priorities for 2019.
Drexel University’s LeBow College of Business recognizes organizations that do just that – demonstrate innovation in analytics. The Drexel LeBow Analytics 50 is a national recognition of industry analytics distinction where 50 companies are honored for their use of analytics to solve business challenges.
The judging panel is comprised of LeBow research faculty and industry practitioners. Nominations are judged by the complexity of the business challenge, the analytics solution implemented and the solution’s impact on the organization. Honorees are recognized at a biennial awards ceremony at Drexel University.
Here is a look at the analytics challenges and outcomes of two of the Drexel LeBow Analytics 50 winners for 2019.
CareFirst BlueCross BlueShield
The Federal Employee Program (FEP) actuarial team supported by CareFirst BlueCross BlueShield in Reston, Va., is tasked with pricing benefits modifications and new benefits for 36 BCBS plans around the country. This was becoming more of a challenge, however, because the FEP actuarial team had an out-of-date mainframe-based tool for pricing analysis that was not keeping up with their business agility and functionality needs.
The FEP Operations Center processes almost one million claims per day for 5.4 million subscribers and family members, says Len Rosenblum, senior director for FEP Products and Delivery. The team was increasingly finding that the tool was not adequate for complex pricing analysis.
The tool “had just run its course and didn’t have configurability to do what the actuaries were doing today,’’ such as the ability to change deductible amounts and quickly assess the impact based on real claims data, he says.
As a result, the actuarial team spent a lot of time working around the capabilities of the system pulling information together from multiple sources and then manually combining the data to perform the analysis required.
In early 2018, the FEP Operations Center rolled out a new Benefit Pricing System (BPS) that enables actuaries to analyze benefit category usage from prior years’ data and evaluate the impact of changes to the copay, coinsurance, deductibles and catastrophic maximum limits.
The system has three main components: data loading and summarization: the ability to integrate prior year’s claims data into Hadoop and summarize them by benefit category; data entry: users can enter cost share scenarios that apply copay, coinsurance, deductible and catastrophic maximums to each benefit category; and reporting and analytics: users can run reports directly against Hadoop that display selected metrics by multiple benefit category combinations applying the chosen cost share scenario.
On a monthly basis, the BPS system processes and summarizes more than 500 million claims from the previous two years. Then, as reports are run BPS simulates the claims adjudication process by applying business rules to the summarized claims on the fly to show key metrics for the specific scenarios and benefit category combinations selected.
Now, actuaries can change and add benefits categories more dynamically and in a more granular and flexible way than in the past, making it easier to do analysis on them, Rosenblum says.
For example, skilled nursing facilities is a relatively new benefit “and in the past there was no grouping, so we created a new grouping for that,’’ he says. Although the actuaries could manually collect the costs to understand how to price that benefit, “now they can look at skilled nursing facilities claims in greater detail and in combination with other claim characteristics.”
He points to time savings as a significant ROI for the team. “They’re getting better quality data and it is more [refined] to what they want to see,” he says. The actuaries can also more easily import data into Excel.
Looking ahead, Rosenblum says the team is planning to add more benefit categories and provide additional data to continue to enhance the actuaries’ ability to assess multiple changes, such as product, deductible, catastrophic maximum and Medicare, within one analysis in support of their pricing responsibilities.
FICO
The value of data and analytics is when they can provide insight and predictive foresight. While FICO is often associated with financial credit scores, the analytics company does a whole lot more.
In 2016, FICO launched its first driver safety score. Despite advancements in safety technology, incidents of driving while intoxicated or distracted account for 94% of serious accidents, according to the National Highway Traffic Safety Administration (NHTSA).
So FICO created a predictive analytic model called the FICO Safe Driving Score, which uses telematics-based driving data to predict the likelihood of future driving incidents.
“We are a data analytics company, helping businesses make better decisions through data. FICO provides value by applying our analytic skills and domain expertise to new business problems, distilling vast amount of data down into a meaningful and actionable score” explains Can Arkali, senior director of analytics.
The company says the score provides a consistent and objective measure of driver risk and safety based on driving behavior characteristics including acceleration, braking, cornering, speeding and cellphone distraction.
The application is geared at commercial fleets and aggregates anonymized data from a platform called MentorSM, developed by FICO’s partner, eDriving. Drivers are then ranked and given a risk score based on their driving behaviors.
The higher the score, the more likely a driver is to display safe driving behaviors in the future. “The FICO Safe Driving Score was developed off of several hundred thousand drivers and millions of trips,’’ says Arkali. “We are continually enhancing the model through independent data validations and monitoring trends in driving behavior data collected through Mentor®” on a weekly basis from thousands of drivers, he says.
For commercial fleets, the goal is to reduce preventable accidents and associated repair, downtime, liability costs and most importantly, ensure its drivers arrive home safely every night. The application is only used if the driver consents, FICO says. The Mentor platform provides a “playlist” of short, interactive training videos that are customized for each driver to promote safe driving behaviors to improve their driving risk, he says. Drivers who complete the training consistently receive the highest scores (or the lowest risk), according to FICO.
Because it’s challenging to predicting future collisions based on telematics-based driving data alone, Arkali says that “The next step is to obtain and leverage as much contextual information as possible” from external factors such as road and weather conditions, traffic flow, or even a measure of the driver’s mood, since that can strongly influence driving behavior.
The incorporation of this data will make the FICO Safe Driving Score a more complete solution for the commercial fleet market, he says. “In addition, continuous validations of the FICO Safe Driving Score against collisions will help introduce incremental changes and can make the model a valuable tool in both the commercial and personal insurance market.”
