Your data could be your most valuable asset, and participants in the workers’ compensation industry have loads available because they have been collecting and storing data for decades. Yet few analyze data to improve processes and outcomes or to take action in a timely way.
Analytics (data analysis) is crucial to all businesses today to gain insights into product and service quality and business profitability, and to measure value contributed. But processes need to be examined regarding how data is collected, analyzed and reported. Begin by examining these seven ways data can hurt or help.
1. Data silos
Data silos are common in workers’ compensation. Individual data sets are used within organizations and by their vendors to document claim activity. Without interoperability (the ability of a system to work with other systems without special effort on the part of the user) or data integration, the silos naturally fragment the data, making it difficult to gain full understanding of the claim and its multiple issues. A comprehensive view of a claim includes all its associated data.
2. Unstructured data
Unstructured documentation, in the form of notes, leaves valuable information on the table. Notes sections of systems contain important information that cannot be readily integrated into the business intelligence. The cure is to incorporate data elements such as drop-down lists to describe events, facts and actions taken. Such data elements provide claim knowledge and can be monitored and measured.
3. Errors and omissions
Manual data entry is tedious work and often results in skipped data fields and erroneous content. When users are unsure of what should be entered into a data field, they might make up the input or simply skip the task. Management has a responsibility to hold data entry people accountable for what they add to the system. It matters.
Errors and omissions can also occur when data is extracted by an OCR methodology. Optical character recognition is the recognition of printed or written text characters by a computer. Interpretation should be reviewed regularly for accuracy and to be sure the entire scope of content is being retrieved and added to the data set. Changing business needs may result in new data requirements.
4. Human factors
Other human factors also affect data quality. One is intimidation by IT (information technology). Usually this is not intended, but remember that people in IT are not claims adjusters or case managers. The things of interest and concern to them can be completely different, and they use different language to describe those things.
People in business units often have difficulty describing to IT what they need or want. When IT says a request will be difficult or time-consuming, the best response is to persist.
There needs to be timely appropriate reporting of critical information found in current data. The data can often reveal important facts that can be reported automatically and acted upon quickly to minimize damage. Systems should be used to continually monitor the data and report, thereby gaining workflow efficiencies. Time is of the essence.
6. Data fraud
Fraud finds its way into workers’ compensation in many ways, even into its data. The most common data fraud is found in billing—overbilling, misrepresenting diagnoses to justify procedures and duplicate billing are a few of the methods. Bill review companies endeavor to uncover these hoaxes.
Another, less obvious means of fraud is through confusion. A provider may use multiple tax IDs or NPIs (national provider numbers) to obscure the fact that a whole set of bills are coming from the same individual or group. The system will consider the multiple identities as different and not capture the culprit. Providers can achieve the same result by using different names and addresses on bills. Analysis of provider performance is made difficult or impossible when the provider cannot be accurately identified.
7. Data as a work-in-process tool
Data can be used as a work-in-process tool for decision support, workflow analysis, quality measurement and cost assessment, among other initiatives. Timely, actionable information can be applied to work flow and to services to optimize quality performance and cost control.
Accurate and efficient claims data management is critical to quality, outcome and cost management. When data accuracy and integrity is overlooked as an important management responsibility, it will hurt the organization.
Insurance executives can be excused for having ignored the potential of machine learning until today. Truth be told, the idea almost seems like something out of a 1980s sci-fi movie: Computers learn from mankind’s mistakes and adapt to become smarter, more efficient and more predictable than their human creators.
But this is no Isaac Asimov yarn; machine learning is a reality. And many organizations around the world are already taking full advantage of their machines to create new business models, reduce risk, dramatically improve efficiency and drive new competitive advantages. The big question is why insurers have been so slow to start collaborating with the machines.
Essentially, machine learning refers to a set of algorithms that use historical data to predict outcomes. Most of us use machine learning processes every day. Spam filters, for example, use historical data to decide whether emails should be delivered or quarantined. Banks use machine learning algorithms to monitor for fraud or irregular activity on credit cards. Netflix uses machine learning to serve recommendations to users based on their viewing history and recommendations.
In fact, organizations and academics have been working away at defining, designing and improving machine learning models and approaches for decades. The concept was originally floated back in the 1950s, but – with no access to digitized historical data and few commercial applications immediately evident – much of the development of machine learning was largely left to academics and technology geeks. For decades, few business leaders gave the idea much thought.
Machine learning brings with it a whole new vocabulary. Terms such as “feature engineering,” “dimensionality reduction,” “supervised and unsupervised learning,” to name a few. As with all new movements, an organization must be able to bridge the two worlds of data science and business to generate value.
Driven by data
Much has changed. Today, machine learning has become a hot topic in many business sectors, fueled, in large part, by the increasing availability of data and low-cost, scalable, cloud computing. For the past decade or so, businesses and organizations have been feverishly digitizing their data and records – building mountains of historical data on customers, transactions, products and channels. And now they are setting their minds toward putting it to good use.
The emergence of big data has also done much to propel machine learning up the business agenda. Indeed, the availability of masses of unstructured data – everything from weather readings through to social media posts – has not only provided new data for organizations to comb through, it has also allowed businesses to start asking different questions from different data sets to achieve differentiated insights.
The continuing drive for operational efficiency and improved cost management has also catalyzed renewed interest in machine learning. Organizations of all stripes are looking for opportunities to be more productive, more innovative and more efficient than their competitors. Many now wonder whether machine learning can do for information-intensive industries what automation did for manual-intensive ones.
A new playing field
For the insurance sector, we see machine learning as a game-changer. The reality is that most insurance organizations today are focused on three main objectives: improving compliance, improving cost structures and improving competitiveness. It is not difficult to envision how machine learning will form (at least part of) the answer to all three.
Improving compliance: Today’s machine learning algorithms, techniques and technologies can be used on much more than just hard data like facts and figures. They can also be used to analyze information in pictures, videos and voice conversations. Insurers could, for example, use machine learning algorithms to better monitor and understand interactions between customers and sales agents to improve their controls over the mis-selling of products.
Improving cost structures: With a significant portion of an insurer’s cost structure devoted to human resources, any shift toward automation should deliver significant cost savings. Our experience working with insurers suggests that – by using machines instead of humans – insurers could cut their claims processing time down from a number of months to a matter of minutes. What is more, machine learning is often more accurate than humans, meaning that insurers could also cut down the number of denials that result in appeals they may ultimately need to pay out.
Improving competitiveness: While reduced cost structures and improved efficiency can certainly lead to competitive advantage, there are many other ways that machine learning can give insurers the competitive edge. Many insurance customers, for example, may be willing to pay a premium for a product that guarantees frictionless claim payout without the hassle of having to make a call to the claims team. Others may find that they can enhance customer loyalty by simplifying re-enrollment processes and client on-boarding processes to just a handful of questions.
Overcoming cultural differences
It is surprising, therefore, that insurers are only now recognizing the value of machine learning. Insurance organizations are founded on data, and most have already digitized existing records. Insurance is also a resource-intensive business; legions of claims processors, adjustors and assessors are required to pore over the thousands – sometimes millions – of claims submitted in the course of a year. One would therefore expect the insurance sector to be leading the charge toward machine learning. But it is not.
One of the biggest reasons insurers have been slow to adopt machine learning clearly comes down to culture. Generally speaking, the insurance sector is not widely viewed as being “early adopters” of technologies and approaches, preferring instead to wait until technologies have become mature through adoption in other sectors. However, with everyone from governments through to bankers now using machine learning algorithms, this challenge is quickly falling away.
The risk-averse culture of most insurers also dampens the organization’s willingness to experiment and – if necessary – fail in its quest to uncover new approaches. The challenge is that machine learning is all about experimentation and learning from failure; sometimes organizations need to test dozens of algorithms before they find the most suitable one for their purposes. Until “controlled failure” is no longer seen as a career-limiting move, insurance organizations will shy away from testing new approaches.
Insurance organizations also suffer from a cultural challenge common in information-intensive sectors: data hoarding. Indeed, until recently, common wisdom within the business world suggested that those who held the information also held the power. Today, many organizations are starting to realize that it is actually those who share the information who have the most power. As a result, many organizations are now keenly focused on moving toward a “data-driven” culture that rewards information sharing and collaboration and discourages hoarding.
Starting small and growing up
The first thing insurers should realize is that this is not an arms race. The winners will probably not be the organizations with the most data, nor will they likely be the ones that spent the most money on technology. Rather, they will be the ones that took a measured and scientific approach to building their machine learning capabilities and capacities and – over time – found new ways to incorporate machine learning into ever-more aspects of their business.
Insurers may want to embrace the idea of starting small. Our experience and research suggest that – given the cultural and risk challenges facing the insurance sector – insurers will want to start by developing a “proof of concept” model that can safely be tested and adapted in a risk-free environment. Not only will this allow the organization time to improve and test its algorithms, it will also help the designers to better understand exactly what data is required to generate the desired outcome.
More importantly, perhaps, starting with pilots and “proof of concepts” will also provide management and staff with the time they need to get comfortable with the idea of sharing their work with machines. It will take executive-level support and sponsorship as well as keen focus on key change management requirements.
Take the next steps
Recognizing that machines excel at routine tasks and that algorithms learn over time, insurers will want to focus their early “proof of concept” efforts on those processes or assessments that are widely understood and add low value. The more decisions the machine makes and the more data it analyzes, the more prepared it will be to take on more complex tasks and decisions.
Only once the proof of concept has been thoroughly tested and potential applications are understood should business leaders start to think about developing the business case for industrialization (which, to succeed in the long term, must include appropriate frameworks for the governance, monitoring and management of the system).
While this may – on the surface – seem like just another IT implementation plan, the reality is that it machine learning should be championed not by IT but rather by the business itself. It is the business that must decide how and where machines will deliver the most value, and it is the business that owns the data and processes that machines will take over. Ultimately, the business must also be the one that champions machine learning.
All hail, machines!
At KPMG, we have worked with a number of insurers to develop their “proof of concept” machine learning strategies over the past year, and we can say with absolute certainty that the Battle of Machines in the insurance sector has already started. The only other certainty is that those that remain on the sidelines will likely suffer the most as their competitors find new ways to harness machines to drive increasing levels of efficiency and value.
The bottom line is that the machines have arrived. Insurance executives should be welcoming them with open arms.