Applying cognitive computing against massive data sets can help organizations process information more quickly and make smarter business decisions. And cognitive computing is increasingly being used in the domain of risk management, mining often ambiguous and uncertain data to find indicators of known and unknown risks. Learn more about the applications of cognitive computing for risk management below.
Companies and public sector organizations are using increasingly massive amounts of internal and external data to take a more preventative risk stance. However, with that increase in data volume, the effectiveness of traditional analysis methods is diminishing. Cognitive capabilities—including machine learning, natural language processing, and many other types of cognitive technology—provide a modern alternative to traditional analytics and are being applied to massive data sets to help find indicators of known and unknown risks.
Why is cognitive computing gaining traction? Computers have always been able to perform mechanical calculations faster than humans. But what distinguishes cognitive computing is its ability to learn as well. Computers haven’t always been great at what humans would call gray areas of thought and reasoning, but in the cognitive era, that is changing.
Cognitive computing is particularly effective when handling and evaluating unstructured data—the kind of information that doesn’t fit neatly into structured rows and columns. Cognitive technologies, such as natural language processing, semantic computing, and handwriting and image recognition, use advanced algorithms to analyze unstructured data to derive insights and sentiment. Given that a 2015 International Data Group study estimates that roughly 90 percent of data generated today is unstructured, use of cognitive computing can place businesses right on the cutting edge.
This is where cognitive computing and risk management converge. Cognitive computing can help companies detect and evaluate emerging strategic risks—threats that can jeopardize any or all of what executives care most about—before those risks cause potentially significant damage or lead to higher costs or investments. At the same time, cognitive computing can help companies identify other emerging trends, understand the risk/reward tradeoffs inherent in value creation, and improve funding decisions and resource allocation. Leaders who leverage cognitive capabilities can gain competitive advantage and use risk to power their organizations' performance.
Look at fraud detection as an example. The old method of detecting fraud was to use computers to analyze an organization's structured data against rule sets. For example, fraud specialists might create a threshold for wire transfers at USD$10,000 so any transaction over that amount would be flagged by the computer for additional investigation. One problem is that this type of structured-data analysis often creates too many false positives, which require hours of close scrutiny.
With cognitive computing, fraud detection models can become more robust and accurate. For example, a cognitive system might flag a transaction as potential fraud. But if a human determines it’s not fraud because of X, Y, and Z, the computer learns from those human insights. Next time it won’t flag a similar transaction. The computer gets smarter with each interaction. That’s a huge game changer.
Moreover, as cognitive fraud detection systems continue to learn, they can detect more complex fraud, an advantage that may have the biggest impact on risk management. By helping unearth emerging patterns that humans could never detect, cognitive technologies create new patterns to look for—a virtuous cycle that in theory never ends, which is a real advantage when fraudsters are continually evolving their fraud schemes.
Cognitive capabilities are not limited to detecting risk. On a broader scale, they enable businesses to quickly augment human intelligence and help humans perform tasks better. For example, by analyzing patterns in big data, small data, and "dark data," cognitive technologies can detect human behavior and suggest options for mass-personalizing products and services. Companies in the automotive, airline, health care, retail, wealth management, and even litigation are early adopters of these capabilities.
Analysts project that overall market revenue for cognitive solutions will exceed USD$60 billion by 2025, compared to the USD$1 billion in venture capital funding for cognitive technologies in 2014 and 2015, according to the International Data Corporation.
At this stage, cognitive technology is still an assistive technology to help suggest strategies and probabilities of outcomes. Human expertise is still important. Yet, together, humans and computers are learning to do things together that were just not possible previously. Over the past five years, Deloitte has invested significant time, capital, and talent in analytics capabilities. The advances we are making can help our clients introduce cognitive capabilities to their strategic planning and tactical execution processes, better align risk management's activities with management's priorities, and detect emerging risks before they jeopardize the organization's near- and long-term performance. And that’s only just the beginning.
If you’re interested in learning more, please contact one of our leaders.
Global Risk Analytics Leader
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Global Strategic & Reputation Risk Leader
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Pricipal, Deloitte Advisory US
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