Monthly Archives: November 2018

How to Use ODG Data to Improve Workers Comp Case Management

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Regardless of whether or not their organizations operate in states where the use of Official Disability Guidelines (ODG) has been adopted/mandated, risk managers can often leverage ODG data and the claim data from their risk management information systems (RMIS) to benchmark the medical and lost-time components of their workers compensation costs against national averages.

With its origins dating to 1995, ODG (www.mcg.com/odg) provides “unbiased, evidence-based guidelines” and analytical tools designed to “improve and benchmark return-to-work performance, facilitate quality care while limiting inappropriate utilization, assess claim risk for interventional triage, and set reserves based on industry data.”

The following are some ways risk managers can use ODG data in conjunction with their existing risk information tools to drive improvements in their workers compensation case management and achieve greater precision in loss reserve practices.

  1. Examine the data. ODG has a wealth of data that can be used to benchmark estimated incurred financials and return to work (RTW) best practices by job class, state, injury diagnoses, and numerous other confounding factors (e.g., obesity, diabetes, etc.). You can benchmark guidelines against both current and historical workers compensation claims to identify potential issues and opportunities for individual case management or program improvement. To evaluate trends, you need to capture and analyze detailed data on historical losses (a core capability of RMIS technology). Meanwhile, improving decision-making on open cases calls for the ability to track individual financial and treatment developments on a real-time basis. That is where your RMIS or claims administration platform combined with data streaming from your TPA or carrier can be keys to success.
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  2. Be specific. When looking at historical loss trends and comparing them broadly to ODG loss and recovery data, the sharper your focus, the faster you will be able uncover issues and make needed adjustments to improve individual outcomes or overall practices. Scrutinize data by individual location, job function, injury and even body part involved to get meaningful insights that yield specific action steps and measurable improvements.
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  3. Track open claims. Leverage the analytics from ODG to compare progress of specific cases against the statistical ODG guidelines. This will enable you to spot variances in recovery timelines and make reasonable adjustments to individual return-to-work plans.
  4. Set goals. You may want to start the benchmarking process with job functions or locations that have historically been the biggest drivers on total cost of risk. Conduct an analysis of historical claims against aggregated ODG data, identify significant variances in your practices versus ODG results, and target specific improvements in open cases. Monitor overall results on a quarterly basis to assess your progress and make any midstream adjustments to align your practices more closely to the ODG findings.
  5. Get help. ODG offers participants training through frequent webinars and other educational events. At the same time, RMIS providers can offer prescriptive guidance in automation that help clients optimize their workers compensation claims operations and return-to-work programs, including the adoption of the analytics available from ODG.

While there are many options available for employers to use predictive analytic benchmarks with workers compensation claims to drive improvements, ODG provides one of the most widely adopted measurements for tracking actual costs of injured employee cases and the success of return-to-work initiatives. When these resources are used in conjunction with a contemporary RMIS, risk managers can gain visibility into claims management issues, focus on improvements that accelerate recovery of injured employees, and start lowering the total cost of workers compensation risk.

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Using Adaptive Behavioral Analytics to Detect Fraud

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While fraud threats are nothing new for payments processors and financial institutions, the degree and magnitude of such incidents have escalated in recent years. A February 2018 Javelin study found that nearly 16.7 million consumers were victims of identity fraud in 2017—up 8% from the previous year.

Fraud prevention solutions must be flexible and sophisticated enough to not only counteract increasingly-savvy fraudsters, but also distinguish true fraud from false positives, which occur when genuine activity is mistakenly treated as fraud. According to CreditCards.com, four out of five blocked transactions are actually genuine, and these misunderstandings often result in customers being locked out of their accounts. In many ways, the aftermath of false positives can prove more damaging and costly than an actual instance of fraud, as institutions miss revenue generation opportunities while simultaneously hindering customer loyalty and trust.

As consumer payment technologies evolve, so too will the complexities of fraud detection and mitigation. Therefore, it is vital that risk management teams end their reliance on rigid, manually-programmed rule sets or static machine learning models and instead capitalize on the advanced capabilities offered by today’s more versatile tools. By modernizing their fraud strategies with adaptive behavioral analytics, payments processors and financial institutions can better mitigate risk and increase revenue.

How Does it Work?

Unlike the static machine learning of the past, adaptive behavioral analytics are extremely proficient at differentiating between actual fraud and activities that appear suspicious but are ultimately genuine. As a result, friction in financial services and e-commerce is significantly reduced and customers can maintain confidence in their preferred transaction method.

Adaptive behavioral analytics empowers machine learning through a set of sophisticated, automated, self-learning algorithms that review account activities and notify security teams of anomalies.

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These algorithms construct baseline behavioral profiles to reflect a customer’s activity type and frequency. In every interaction—regardless of if a payment occurs—information is gathered and evaluated on the type of device that is used, how it’s used, its location and the amount of the purchase. Combined, these behaviors create a customer portrait that becomes increasingly more accurate over time. Every subsequent interaction then can be measured against the behavioral portrait, within milliseconds, to determine if their activities are fraudulent or genuine.

For example, if a user logs into his or her account at an abnormal rate or suddenly begins adding priority shipping to high-priced orders, the system will detect the irregularity and block future activity. However, if a user simply purchases an expensive holiday gift or books travel arrangements—behaviors that coincide with seasonal activity—the system will recognize and differentiate the fraudulent from the legitimate accordingly.

Adaptive behavioral analytics also optimizes the speed and convenience of fraud detection by processing volumes of data and delivering critical intelligence accurately and immediately. Through this more comprehensive investigation, the software enhances the customer profile to better understand and recognize behavioral trends—a welcome sight for security teams that previously spent hours sifting through reports to locate red flags.

Where Can Adaptive Behavioral Analytics Help Most?

The ubiquity of mobile technology has created a consumer audience who prefers to conduct business through a smartphone, tablet or another device that eliminates a trip to a physical store or bank branch. In turn, these consumers demand leading-edge mobile technologies that are intuitive, convenient and offer a full range of services.

The combination of the U.S. adoption of the EMV standard in 2015 and the rise in e-commerce has escalated the volume and prominence of Card Not Present (CNP) fraud. Whether through online purchase portals or apps that access mobile wallets, the digital entry of account information raises the likelihood of a person’s information becoming compromised.

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With more transactions taking place, the volume of both true fraud activity and regular behaviors that appear suspicious will increase. However, adaptive behavioral analytics enables a more refined detection between the actual fraud and genuine activity.

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It is the best of both worlds: a much-needed, innovative line of defense that combats payments fraud and clears a path for more revenue-generating transactions.