Artificial Intelligence: The Potential And Implications For Finance Leaders

Timo Elliott

In early January, I presented a session for The Conference Board on the potential and implications of artificial intelligence for finance leaders.

After explaining the current research around artificial intelligence and machine learning, I covered four main areas where AI is being used for the financial function today:

  • Automating end-to-end processes: Increase efficiency and reduce costs. For example, machine learning can automate complex, repetitive decisions such as invoice matching; automatically recognize fields from invoices and expenses; automatically discover potential problems in invoices; and much more. All of this reduces manual interventions, leaving more time for strategic finance.
  • Detect and prevent: Detect and rank information out of Big Data. Use machine learning to automatically detect fraud in money transfers, employee expenses, and more.
  • Predict: Derive knowledge from historical information to increase the accuracy of predictive scenarios. Augment traditional financial analytics with more powerful data-matching, pattern recognition, etc., and discover the potential of predictive financial closes.
  • Proactive context-sensitive support: Digital assistants boost the productivity of financial experts using machine learning to improve context-sensitive, self-service access to financial data.

The recorded Webinar is available on The Conference Board website, and you can download a PDF version of the presentation from this link, or visit Screenshare to get the full PowerPoint version.

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This article originally appeared on Digital Business & Business Analytics.

Timo Elliott

About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence.