4 Ways Data Analytics Can Reshape Risk
Insurance Experts' Forum, September 5, 2013
In a new report, IT security expert Steve Hunt makes the case for big data as a risk-reducer for banks and financial institutions. The lessons he provides can be instructive for the insurance industry as well.
In the report, published by GigaOm Pro and sponsored by Digital Reasoning, he points out that financial institutions now need as many surveillance and intelligence capabilities as western governments, to spot or prevent international terrorism and gangster activity. Additionally, the data analysis technology — now available at very low prices — also should be effective against lower-level fraud and insider trading.
He notes that analytic technologies are changing fast, including “the commoditization of cloud use and storage and massively parallel-processing computing.” Enterprises need to be proactive in understanding and adopting new analytics technologies.
Insurance companies also take on a great deal of measured risk, and would benefit from being proactive in being able to weed out instances of fraud and abuse. Hunt makes the following recommendations:
Be open to new tools and techniques: “Forward-looking businesses seeking to expand their geographical or vertical exposure need new analytical techniques and tools to maintain or reduce the risk to which they become exposed through increased activity in these potentially lucrative markets. These tools leverage the generational advances in data processing, memory, and algorithmic data processing that have occurred in the past few years with the rise of big data. The application of predictive analysis techniques, originally developed for nation-state and intelligence applications, to commercial transactions affords greater control of risk than ever. This lets banks and financial institutions participate in markets and opportunities that would have been deemed too risky by less sophisticated or nuanced capabilities.”
Expand the reach of data analysis to unstructured data: “A proactive approach includes the consideration of unstructured data. That includes enough data to do what we do today — examine transactions and transaction logs in near-real time and begin to ingest and examine other channels of supporting information, like email, chat sessions, blog posts, and social media activity.” For its part, the Financial Industry Regulatory Authority (FINRA) recently announced it would be conducting spot checks on social media, including blogs, Facebook, LinkedIn, Twitter and others.
Move beyond simple “compliance”: Proactively engaging in analysis of structured and unstructured data for potential risks can add value to the business, versus simply avoiding government penalties. “Many financial services firms are moving from a traditional risk and compliance strategy that focused on meeting the letter of the law to one in which the firm sets the objective to go well beyond the letter of law and focus on not only reducing their regulatory risk but also on taking the more important step of reducing its reputational risk,” says Hunt.
Be more attuned to insider abuse: This is a greater, and harder-to-spot threat than outside hackers and fraudsters, Hunt says. Here, big data needs can be employed to weed out suspicious patterns of behavior. “Detecting insider malfeasance requires evidence of transactions and interactions compared against a context of reasonable and normal behaviors that are inherently impossible to articulate through structured data alone,” he states.
(Disclosure: the author is an occasional contributor to GigaOm Pro.)
Joe McKendrick is an author, consultant, blogger and frequent INN contributor specializing in information technology.
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