In 2020, the consequences of the pandemic-generated recession have dealt a blow clean to the bones of industry. From financial markets to insurance, to retail, to oil markets, the entire scope of industry has been foundationally shifted by the impact of the 2020 recession crisis that the pandemic catalyzed.
Predictive analytics paired with the business models of these industries all throughout that period, testing the outer limits of these business models and their endurance in unprecedented circumstances.
A litmus test of the disruption and surprise onset recession can be observed by watching the hit the insurance industry took. The insurance industry is a good point of reference to watch because the entire scope of the industry requires insurance policies at some point. It is a common denominator between industries that we can focus on to observe the impact on industry now and in the forecasted near-future.
CNBC reported on August 28 that millions of Americans, as a direct result of the pandemic recession, have lost their health insurance coverage. As of August 28, at least 12 million Americans had lost their health insurance and were seeking options for getting new coverage. CNBC cited a study by the Economic Policy Institute as its source.
The study noted that the sudden drop from health insurance policies was a result of those who were insured through their employer. The 2020 recession has seen a record rise in American unemployment.
Recession and the need for intuitive insights
This leads to Americans who are seeking to navigate insurance policies they can afford to purchase independent of an employer. The need for a changing insurance policy demand places the strain on intuitive processing for plans and options. This can be achieved most efficiently through the power of predictive analytics tools.
Predictive Analytics in 2020 Markets
Early this year, forward-looking industry watchdogs pinned predictive analytics as to the “reshaping” factor of the 2020 insurance industry. Predictive analytics came on board in theory to give insights from the huge influx of data collection that exists on pricing and risk selection, customer cancellation risks, and fraud risks. The industry outlook reports also noted the use of predictive analytics would have for streamlining the claim process, pinpointing outlier claims, anticipating trends, and so on. Insurance companies put their hopes in the power of predictive analytics to propel them ahead of a market that is moving faster by default of the digitization of insurance claim documents.
The insurance industry was then hit with the unprecedented circumstances of 2020’s global pandemic crisis on the insurance market. The impact was extreme and complex. There was a significant loss due to the high volume of claims that were called in because of the crisis pandemic circumstances.
Risk Management Magazine noted a significant rise in D&O Liability, workers' compensation, professional liability, and trade credit lines of insurance. The D&O Liability claims were noted in major hospitality corporations. Likewise, changes in some fields of insurance, such as trade credit, were not “stratospheric” but did rise at an incline of 10 percent. An incline that, with a workforce taxed by the reduction of employees present, or by those laid off due to financial losses, would create serious disruptions for the business model of many insurance companies
The demand for predictive analytics systems
Predictive analytics is only as good as the degree to which they are leveraged. Determining the predictive analytics systems that were used across the industry at the time the recession was sprung upon them can help us see what capacity the scope of these industries had to triage analytics reports and apply them to business procedures.
The predictive analytics systems available to these companies were efficient, but only at the functional level. Watson analytics brought in the IBM suite of tailored tools that fast-tracked the common thread of efficiency found in general predictive analytics tools. The Watson tool leverages the full potential of predictive analytics, bringing them to the level of the large-scale insurance operation that sees the highest volume of claims.
Watson Tools for Leveraging Predictive Analytics
Continuing with the example of insurance companies, they teach their teams to use Watson IBM tools to speed the process of mass insurance claim influxes. These influxes can be the direct cause of a large-scale crisis such as the COVID-19 pandemic for health insurance, or of natural disasters such as the West Coast fires. Whatever the cause, and whatever the policy, insurance claims are constant and compile. A mass-scale firm requires the tools that can legitimately circulate claims at the scale of the business operation.
IBM Watson promotes the fast-tracking of insurance claims, from scanning the claim to see if it is legitimate, and then processing that claim through the proper policy channels.
New Watson tools, such as the Watson Explorer tool, break down the claim process to analyze structured and unstructured data at once. Watson Explorer employs data analysis to understand interactions, rules, and the processing logic of this data. It then applies the interactions, rules, and processing logic to policies.
Watson first analyzes both unstructured and structured data, and then compares it to the policy information that fits the case scenario. It then inputs documents and makes intuitive recommendations. These recommendations help the insurance employee to determine the eligibility of the claim in question, as well as what percentage of the claim the insurance company should pay.
Employees use the logic of the Watson data analysis to make informed decisions that yield rapid results. The process has been proven to improve the speed of insurance claims processing by 25 percent.
Watson Studio takes the intuitive functions of Watson Explorer a step further by allowing the user to build, run, and manage AI models. Through these models, the user can optimize decisions across a cloud. This comes with an allowance to make AI models operational across the IBM Cloud Pak. This can be quite helpful for major scale companies with more complex process management systems to work with. For the Fortune 500 to Fortune 100 scale insurance company, there is a vastly larger input of both structured and unstructured data to sort through. The Watson Studio allows the user to not only predict outcomes and insights that can be actionable for insurance policies but also further explore and refine the data to hone results.
Finally, Watson Assistant is a Watson conversation AI platform. It works to bring virtual assistance at a speedy and straightforward pace to your customers. This is useful because it takes the burden off of employees and delays interruptions that might interrupt productivity. Watson can maintain customer interactions without sacrificing customer experience and efficacy because it leverages predictive analytics to provide clear, actionable directives from its knowledge base. It knows when to ask the customer for more information, when to produce the answer from its knowledge base, and when to direct to an employee.
Looking beyond 2020
The effects of the 2020 recession-era will stretch on into the future for a while yet. Predictive analytics, if leveraged to their full potential, have the power to stimulate a market correction through the auditing of prime business procedures, the triaging of priorities, the advance of deep insights, and the execution of goal sets. With Watson IBM tools leading the way as the razor-edge of predictive analytics systems, industry firms should consider making the transition to stay in step with the market advances.
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