Anyone in the industry is familiar with the need to constantly scrutinize and update business processes, and that it’s a job that never stops. We’re still, in many ways, living in the wake of the great process re-engineering binge of the late 1980s and early 1990s, when companies tried to identify and tear down all the obstacles they’ve been throwing up in front of customers and employee productivity.

Now, algorithms may be picking up the work of process re-engineering. Machine-learning algorithms may already be having a huge impact on many key business processes, from customer service to managing finances. In a recent Harvard Business Review article, H. James Wilson, Allan Alter, and Prashant Shukla – all with Accenture Analytics – described the progress that’s being made in machine learning, and potential business benefits. They consider the rise of machine learning as significant as the automation and re-engineering of business processes some 20 to 30 years ago.

“Powerful machine-learning algorithms that adapt through experience and evolve in intelligence with exposure to data are driving changes in businesses that would have been impossible to imagine just five years ago,” they state, calling this latest wave “machine re-engineering.” The difference between this and the re-engineering movement of two-three decades back is that machine-learning algorithms allow for almost instantaneous re-engineering, versus the slower engagements that required human analysis.

Wilson, Alter and Prashant took a close look at 30 machine-learning pilot projects and saw positive results stemming from the employment of four key forms of machine learning:

• Natural-language processing was instrumental in boosting customer service, enterprise risk and compliance.

• Anomaly detection was key to enterprise risk and compliance and developing business capabilities.

• Predictive analytics played a role in enterprise risk and compliance, as well as business capabilities development and marketing.

• Visual sensing boosted marketing and sales.

“Nearly half of early movers reported improvements to top-line performance,” the authors report. “Most often, improvements came through automatically providing more timely predictive data to employees who interact with customers or sales prospects.” Plus, they add, “about a fifth of early movers reported significant gains in customer satisfaction and engagement. Here, we can thank machine-reengineered processes for smoothing customer-service interactions, reducing process steps, or increasing human interaction in customer service situations.” They provide an example: for its IVR system, a financial services group began using customers’ voices as passwords, cutting out four steps in the authentication process, and improving call routing by 50 percent.

In a presentation to the Casualty Actuarial Society a few years back, Christopher Cooksey outlined many potential machine-learning applications in the insurance space. Some areas of inquiry may include the following:

Underwriting or re-inspecting: “This kind of analysis can result in a list of policies to target.” • Determining customer profitability: “This kind of analysis defines the kind of business you write profitably. This needs to be combined with marketing/demographic data to identify areas rich in this kind of business.” • Quality of business: “Knowing who you write at a profit or loss, you can monitor new business as it comes in.” • Agent/broker relationship: “Use this analysis to inform your understanding of agent performance.”

Of course, this covers the underwriting side, and there’s many more parts of insurance companies that may benefit from machine re-engineering. The field is wide open, so stay tuned.

Register or login for access to this item and much more

All Digital Insurance content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access