Big Data is a big deal for Corporate America right now, yes—but that doesn't necessarily mean the idea is, well, a big bang. “A series of familiar ideas writ large” may be the better way to put it.
Take the property and casualty insurance market. Underwriting policies has always been a mix of art and science, says Claude Yoder, head of global analytics for insurance broker and risk adviser Marsh, but with the advent of Big Data, “it's added more of the science flavor.”
For example, it's no surprise that the taller a building is, the more likely it is to fall over. With today's extensive data and predictive models, however, insurance providers can now quantify exactly how much more likely that occurrence is, and put a price on “topple risk.”
The enhanced analytics “doesn't replace underwriting; it complements it,” Yoder says, which makes pricing “much more sophisticated” (read: harder to for customers to negotiate) as a result.
That idea of evolutionary improvement in the understanding of data, giving you more precise insights one iteration at a time, will be recurring theme as Big Data matures. Little surprise, then, that the early adopter of Big Data is the financial services sector, which has long been a heavy user of data and analytics.
“Banks and retailers have been monitoring transactions and flagging suspicious ones for decades,” says Norman Marks, a vice president for SAP and governance expert. “The issue now is that the explosion of data makes it difficult to mine all that in time with traditional tools.”
Big Data, however, also will not come to pass overnight. At $5.2 billion Frontier Communications, vice president of internal audit Neil Frieser has steadily seen more requests for analyses of large chunks of data. One request was a report that can verify the number of new customers each month (as opposed to existing ones who open new accounts); another was a way to monitor compliance with the company's strict new caps on customer service credits.
Frieser has been at it for more than two years, and still considers the effort nascent. “You don't just wave a magic wand,” he says. “It's a lot of smaller, lower-value things that we can do that over time should have a bigger impact.”
Yet, as slow and as incremental as the understanding of Big Data may be, the amount of Big Data cannot be ignored; compliance, audit, and risk executives do so at their peril. “You've got an explosion in the data, a dramatic reduction in cost of storing that data, and then you've got the tremendous improvement in quality of getting the data out, plus advances in predictive analytics, which allows you to take all that data and look in front of you,” Marks says. “The opportunity is fantastic.”
What You Know Already
So, where to begin?
For starters, define which risks concern you, or which policies you most want to monitor for compliance. Then look across both structured and unstructured data (including emails and social media), to select the items most likely to drive the risk or at least yield some insights about it. Given the volume of data, “You don't go searching the haystack until you know what you're looking for,” Marks says.
“Banks and retailers have been monitoring transactions and flagging suspicious ones for decades. The issue now is that the explosion of data makes it difficult to mine all that in time with traditional tools.”
—Norman Marks,
Vice President,
SAP
Marks advises first nosing around other parts of the company to see whether they already have any Big Data projects underway; the marketing and procurement departments are likely suspects. “Social media monitoring is mostly driven by marketing, but there are certainly risks that one could divine from the comments people make about the company, and its suppliers,” he says. “People in compliance can piggyback on systems that other departments are already using, whether it's marketing, finance, or supply chain.”
In some cases, executives discover that existing projects begun under some other rubrics fit quite nicely into the Big Data category. At Blue Cross and Blue Shield of North Carolina (BCBSNC), director of audit and risk management Richard Supinski says the continuous monitoring process he started two years ago is “absolutely” a Big Data project, and has shown impressive results.
For years, the $5.5 billion health insurance provider could only look at a sample of the millions of claims it processed each month, and compare that with the samples it had drawn from previous months. That sort of auditing was necessary to comply with National Association of Insurance Commissioners model rules, but it didn't give the business much actionable data. Supinski's goal was to get a window into the entire set of transactions each month, without adding new software, staff, or other types of cost.
Today, working with existing tools (SQL and a dashboard application called Excelsius), Supinski and his team of three put together a report within a week of the month's close that shows how many duplicate claim submissions the insurer received, which healthcare providers were among the top offenders, and which had shown the greatest increase in such mistakes compared to past months. With that more holistic data in hand, BCBSNC has been able to reach out to the providers and nip a number of errors in the bud.
ABOUT THIS SERIES
Compliance Week's exclusive four-part series on “Big Data” is examining the growing volume of information that companies are capturing and the tools they are building to harness mass volumes of diverse data at speeds once inconceivable. We'll look at ways it can be used to improve risk management, audit, and compliance, and the compliance officer's role in this landmark business transformation.
Part 1: Unlocking the Potential of Information, July 17
Part 2: Big Data and risk analysis, July 31
Part 3: Big Data and fraud investigation, Aug. 14
Part 4: The logjam on execution, Aug. 28
“Now that they know we're measuring it, they seem to be more concerned about how many [erroneous claims] they submit,” Supinski says. He notes that that duplicates as a percentage of all transactions have declined from about 14 percent of all claims to around 9 percent in the last two years.
Scaling Up
And as Supinski found, one analysis tends to beget another. Looking at duplicate submissions, for example, flagged a related problem: so-called corrected claims, which initially had problems and needed manual review, and then appeared as duplicates. The team now runs a separate report for those snafus, which are also declining. Supinski also runs reports for accounts payable transactions, to eliminate double payments to non-healthcare vendors; that exercise has led to reviewing T&E expenses separately.
Other requests for reports are streaming in, but with manual formatting still required for most, Supinski says he will soon need to automate more of the process to meet demand. That automation could take several forms, he says, including extending what he has or buying something entirely new.
One of the most appealing options to Supinski is Infogix, which could stream data directly into easily digested dashboards on a daily, rather than monthly, basis. Given the cost of such investments and the difficulty of pinpointing future cost-savings, however, “it may be a couple of years before we get there.”
Other organizations have taken the opposite approach. At Frontier, Frieser says he started with a software tool (ACL), looking for ways his department could partner more closely with operations; he then set out on something of a marketing campaign to persuade operational managers to make use of it.
“We haven't had an overwhelming stampede of people clamoring for us to write new reports for them,” Frieser admits, but he is building a “client list” within the company slowly. His department now runs two to five reports for each of six major areas of the company, including customer service and accounts payable.
This year he will also use the tools for internal audit purposes, aiming to assess 100 percent of datasets where possible and selecting smarter samples in other cases. Long term, Frieser wants to find cost savings that ultimately could help drive earnings per share.
Regardless of the tools in hand, however, compliance experts say that using Big Data wisely largely hinges on the ability to collaborate with other human beings, to tease out the right insights.
“We really depend on the business units to give us feedback on what we pull; to tell us what are really errors and what are false positives, so it's a continual collaboration effort” Supinski says. “You can't let it go on automatic.”
The big takeaway about Big Data: don't get discouraged. “We are only just starting to understand how Big Data can be used,” Marks says. “Like with many things, the use of Big Data by a company is only limited by availability of data, technology, and most importantly, imagination.”
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