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Many people are uncertain about predictive analytics in information systems, confusing them with predictive reports in business intelligence, and are unclear on the value of predictive analytics. Executable predictive analytic models can be embedded in systems for a number of beneficial outcomes.

  • Know your customers—and act on the insight.
    Predictive analytics provides insight on future customer behavior that can help you identify the best action to take on every customer or transaction. And analytics guides other strategic action, such as placing accounts at collection agencies that will maximize collected dollars, or detecting fraud, abuse and error in healthcare claims.
  • Work smarter and faster by replacing guesswork with science.
    Analytics answers complex questions and processes transactions with empirical precision and at incredible speeds, often during "live" transactions. Decisions that used to take hours or days can be reduced to minutes or milliseconds—from "instant" credit offers to insurance underwriting to approval for new phone service to real-time fraud detection.
  • Reduce costs with a clear gauge of risk and uncertainty.
    With analytic insight, businesses can more accurately measure business risks and reduce losses. This includes losses due to fraud, since predictive analytics can detect the subtlest abnormal patterns in application, purchase, claims, transaction or network data.
  • Add consistency to business decisions, improving compliance and customer service.
    Predictive analytics operates consistently and dependably, relying on mathematical technique. This is critical to the risk control and consistent treatment required by regulators in banking, insurance and other industries. Consistent, unbiased decisions meet the fairness test better than decisions based on human subjectivity.
  • Become more competitive.
    The end result of faster, smarter and more consistent decisions is a more agile business that can respond quickly to market conditions, improve customer service and profitably grow into new markets.

Adding predictive analytics to automated decision services is critical to improving the quality of decision-making in your systems. Critical, in other words, to making them smart enough.

JT

Author, with Neil Raden, of Smart (Enough) Systems.

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