Why can't today's information systems – even though they are so powerful they can manage and process petabytes' worth of information – help us to make better decisions?
James Taylor, a thought leader in the automated decisions space, has a few ideas about what needs to be changed with today's information paradigm. To this end, he just published a new book: "Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics."
Taylor was also co-author, along with Neil Raden, of the groundbreaking work "Smart Enough Systems," published a couple of years back, which made the case for applying automation and rules engines against lower-level decisions, such as the countless ones made in customer contact centers everyday. Decision automation may be impractical for high-level strategic decisions (should we cut our rates for policyholders?), but well-suited for the thousands of decisions made at the ground level (is this policyholder eligible for a good driving discount?).
Taylor observes several challenges with many of today's systems:
- They stop and wait rather than acting: “Most information systems do not act on behalf of the organization or the users of the system,” Taylor writes. “All too often they wait until a human operator comes along to tell them what to do next.”
- They escalate rather than empower: “...they often don’t allow the day-to-day users of the systems to take action either. Instead they require managers or supervisors to log in and approve actions.”
- They report but don’t learn: “What these systems don’t do is learn from the data they contain; they don’t improve their behavior based on what happened in the past.”
- They have been built to last, not change: “To be robust and scalable, these systems have been built to last... [they are] hard to change and brittle when they are changed.”
By better automating decisions, insurers can make gains in agility and competitiveness, Taylor argues. “The use of business rules in decision management systems has given organizations the agility to respond rapidly to competition and market changes, to avoid business risks, and to take advantage of narrow windows of opportunity,” he points out.
In "Decision Management Systems," Taylor cites the example of Infinity Property & Casualty Corporation, a provider of nonstandard personal automobile insurance with an emphasis on higher-risk drivers, which implemented decision management systems to manage fraud. The company saw a documented return on investment of more than 400 percent following its implementation of automated decision-making, as well as speeding up referral time to its special investigative unit from 45-60 days down to 1-3 days.
As Taylor describes it: “Infinity’s decision management system combines predictive analytics with business rules to ensure the best possible outcome by defining and performing what-if simulations and adjusting the parameters for different situations. Business managers can also quickly modify rules, events and processes, and see their changes deployed immediately, giving them the flexibility to make adjustments as business needs change. As a result, claims adjusters and others with in-depth business knowledge can quickly and easily define how risk should be assessed and automate many routine decisions while retaining full control of the claims handling process.”
Fraud control is one area, but with many enterprise systems, such as policy administration, underwriting and call center systems, there are countless low-level decisions that can be effectively automated.
Joe McKendrick is an author, consultant, blogger and frequent INN contributor specializing in information technology.
Readers are encouraged to respond to Joe using the “Add Your Comments” box below. He can also be reached at email@example.com.
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