Cognitive computing applications for risk management
Better decision making with artificial intelligence
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. Read on to learn more about the applications of cognitive computing for risk management.
Augment and empower business decisions
The idea of computers outsmarting and replacing humans has existed in movies and books for decades. Fortunately, that hasn’t happened on a wide scale yet. But what has happened is the recent emergence of artificial intelligence concepts—specifically cognitive computing. These concepts involve advanced technology platforms that can address complex situations that are characterised by ambiguity and uncertainty. Cognitive computing has begun to augment business decisions and power performance right alongside human thought process and traditional analytics. In fact, the domain of risk management lends itself particularly well to cognitive computing capabilities, as typical risk issues often include unlikely and/or ambiguous events.
Data that doesn’t fit in a spreadsheet
Artificial intelligence is increasingly becoming a routine part of our daily lives with the introduction of digital personal assistants, music and movie recommendation services, and cars that can see around corners. Just as smartphones, online shopping sites, and music apps learn and adapt based on our preferences, cognitive computing can be used to teach computers to recognize and identify risk.
Of course, computers have always been able to perform mechanical calculations faster than humans. The difference is that with cognitive analytics, computers have the ability to learn as well.
The use of artificial intelligence to manage risk is particularly helpful 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 (NLP), use advanced algorithms to analyse text in order to derive insights and sentiment from unstructured data. Given that a 2015 International Data Group study estimates that roughly 90 percent of data generated today is unstructured, implementing cognitive analytics can place businesses right on the cutting edge.
Computers get smarter
Look at fraud detection as an example. The old method of detecting fraud was to use computers to analyse a lot of structured data against rule sets. For example, fraud specialists would create a threshold for wire transfers at €10,000 so any transaction over that amount would be flagged by the computer for additional investigation. The problem is that this type of structured-data analysis often creates too many false positives, Hans says, which require hours of close scrutiny.
Where to apply cognitive analytics
These new capabilities are not limited to detecting risk. Cognitive analytics allow businesses to quickly tap unstructured information, personalize services, and reduce subjectivity in decision making. Among the arenas where this approach to data is useful are healthcare, retail, and even litigation, where computers are “trained” to discover specific information in millions of legal documents and perform any necessary global language translation.