THE NEXT LEVEL: PEOS AND THE ISSUES OF EMPLOYING PREDICTIVE ANALYTICS

Predictive analytics is the process of using AI, data and algorithms that can learn from data to forecast future outcomes and trends to optimize preferred strategic progression in order to develop and improve performance. PEOs have a unique opportunity to maximize this advantage via real time payroll and historical risk data to advance assessment and predictive modeling in their operational ability to integrate, analyze and coordinate multiple streams of data. Examples of this data include client operational methodologies, facility/environmental conditions, employee job classifications, safety mandates, historical claims and employee work hours/shifts.

CLEAN AND CORRECT DATA

For a predictive analytics model to accurately learn and predict client behavior, the data set used to train the model needs to be as accurate as possible. Sanitizing data is the quintessential first step for PEOs when embarking on and solidifying predictive analytics project management. It is imperative that the transformation of raw data be driven into an applicable arrangement for analysis, removing errors, omissions and anomalies to ensure the reliability and veracity of the data. Operationally reinforcing the importance of quality data is paramount as large datasets can have combined negative effects, such as wasting project production time and increasing costs. Common data challenges, like missing, outdated or incorrect information, can result in an incomplete picture of a risk and flawed underwriting and pricing decisions. The data must be complete, validated and consistently cleaned to support accurate analysis.

RISK IDENTIFICATION CONSTRUCT

Risk identification is the process of determining potential risks faced by an organization. It is the proverbial laying of the cornerstone in building and maintaining a proactive risk management program. It involves systematically recognizing and cataloging potential threats that could adversely affect the PEO’s ability to achieve its risk management objectives. The very nature of predictive analytics is forward looking and historical data analyzation allows PEOs to identify hazards, assess the risks and create methodologies to control the risks. At its core, this data analyzation highlights a course of action in which the PEO could make rapid, informed decisions from the five main risk management actions: transference, toleration, manage, terminate or acceptance of the risk.

EMERGING WORKERS’ COMP. RISK PROFILES

As the rapid integration of technology continues to evolve, the nature of work and workplaces will be increasingly complex and vary greatly form their earlier incantations and greatly expanding their boundaries and reshaping traditional production patterns. Clients of PEOs will adopt new ways of production as markets expand, and the nature of work evolves. Technology has already changed and will continue to bring opportunity, paving the way to create new jobs, increase productivity, and deliver rapid performance and services. By gaining deeper insights into their clients’ preferences and behaviors, PEOs deploying predictive analytics modeling can hyper illuminate and bring data-driven solutions to customized pricing. Instead of relying on broad risk categories, PEOs can tailor premium discounts or increases, based on algorithmic data to individual clients based on their specific individual risk profiles. As traditional production and service patterns evolve at an ever-increasing rate, predictive analytics is a strategic imperative.

IMPROVE AND EXPEDITE WORKERS’ COMP. CLAIMS

Specific data driven predictive analysis can be used to help identify high-risk claims early, thereby enabling proactive intervention strategies to improve safety outcomes and reduce claims. The applied use of the aptly deployed predictive analytics algorithms will have the ability to transform the claims process. The properly applied use of predictive analytics can be used to expedite and enhance the workers’ comp. issues such as triaging of claims, identifying outlier claims and identifying and stratifying a claims’ beginning to end probability. This ability will enable the PEO to algorithmically determine the best place to allocate PEO assets.

DEEPER CLIENT RELATIONSHIPS

Another benefit from using deep data driven predictive analytics is that it provides a next level opportunity to solidify and personalize the PEO’s relationship with its client. By virtue of the process, client operational management data collection involves the collection, organization and utilization of that data to enhance the PEO’s risk management program. It should be noted that this also provides a framework to enhance the clients’ experiences with the PEO. By collecting this data, the PEO gains a detailed and integral understanding of individual client choices, whether they pertain to products, services or communication channels. Via this opportunity, it is possible for the PEO to provide a more robust, personalized, and relevant client experience, leading to more targeted digital outreach and increased client satisfaction. This in and of itself will lead to improved client loyalty by providing a personalized experience for the client. I mean after all, clients want to feel that the PEO really knows their business, business model, and its values and methodologies as a company.

CHALLENGES AND OPPORTUNITIES

As predictive models continue to evolve, they can and must be further refined with additional data points, becoming even more accurate in their forecasts. This allows organizations to stay ahead of emerging risks and adjust their strategies in real time. As the costs associated with workplace injuries rise, having a robust, data-driven risk management strategy will be critical to maintaining competitive advantage. It should be noted however, that taking a cautious, strategic, detailed and forward-looking considered approach to improving operational risk management efficiency is paramount for developing a foundational predictive analytics model that will be critical to the success or failure of the PEO’s predictive analytics-driven risk management program. If done properly and observing this precept, the end result will be PEO risk management program’s operating efficiently, responsively, and successfully. In the final analysis, by managing the PEO/client preferences and operational methodologies across all parts of an enterprise, it will be possible to deliver truly client tailored experiences, allowing PEOs to build stronger relationships, drive client loyalty and achieve optimum business performance.