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Artificial Intelligence -- Potential and Implications for Finance

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Presentation for The Conference Board, on the uses of Artificial Intelligence and Machine Learning for the Finance function. A recording of the session can be found here: https://www.conference-board.org/webcasts/ondemand/webcastdetail.cfm?webcastid=3882

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Artificial Intelligence -- Potential and Implications for Finance

  1. 1. Timo Elliott, Innovation Evangelist Artificial Intelligence: The Potential and Implications for Finance Leaders
  2. 2. 3 Artificial intelligence AI is a “sociotechnical construct” indicating machine capabilities which solve complex tasks that were recently only possible by humans (equally well or better) Technical disciplines that solve business problems through the extraction of knowledge from data. Deep learning Machine learning Data Science Big Data Advanced Analytics Predictive Analytics Data mining
  3. 3. 4 Why now? Big DataAlgorithms Hardware
  4. 4. 5 Algorithms are taking over Voice transcription Lip reading Image Descriptions
  5. 5. 6 What’s different with machine learning? Classical software must be programmed Humans learn from experience Machine learning learns from data
  6. 6. 7 Data Training Inference Apply model Services (such as invoice processing, profile matching) …and more Applications (such as cash application) Text Image Video Speech … and more Train model Prepare data Capture feedback How does machine learning work? From data to insights
  7. 7. 8 In the future, all processes will be “self-improving” Do your financial processes improve automatically over time?
  8. 8. 9 40% 70% 94%
  9. 9. 10 Machine learning everywhere Source; McKinsey & Company Machine learning has broad potential across industries and use cases
  10. 10. 11  Automating end-to-end processes Increase efficiency and reduce costs  Detect and prevent Detect and rank information out of Big Data  Predict Derive knowledge from historical information to increase the accuracy of predictive scenarios  Proactive context-sensitive support Digital assistants boost productivity of financial experts Machine learning for finance Changing the world of finance by adding intelligence to applications Automate PredictDetect Assist
  11. 11. 12 Big opportunities Source: http://gartner.com/SmarterWithGartner “In 2018, half a billion users will save two hours a day thanks to AI-powered tools.” Gartner
  12. 12. 13 Finance of the future Inverting the effort pyramid through automation Strategy and growth Keeping the lights on Compliance, efficiency, and business performance Strategy and growth Keeping the lights on Today 2020 Automation “Up to 70% of finance tasks are potentially automatable with next-generation technologies” (McKinsey 2016)
  13. 13. 14 Example: accounts receivable teams Joni Chen AR accountant Lump sum Missing info Discounts Exchange Customer call ??? Payments
  14. 14. 15 Intelligent Invoice Matching History Payments Invoices Matching proposals Intelligently learn matching criteria from your history and automatically clear payments. Machine Learning Improves days sales outstanding Allows shared services to scale as the business grows Empowers finance to focus on strategic tasks and service quality
  15. 15. 16 Automatic extraction of information from invoices
  16. 16. 17 Companies with 10,000 employees and more process 300,000+ invoices every year A subset of these needs to be corrected, because of formal or content errors Historical data is used to identify patterns for invoices that need corrections These patterns are used to warn the approver proactively Intelligent invoice correction Proactively improve invoice accuracy
  17. 17. 18 Automatic extraction of information from employee expenses
  18. 18. 19 Record to Report Procure to Pay Order to Cash  Eliminate manual FI/CO reconciliation efforts and make business self-service access to data easier and more intuitive  Improve forecast accuracy, accelerate and automate the financial close along with lower compliance and auditing costs  Significantly simplify and automate the interaction between buyers and suppliers through digitizing the exchange of information and achieve high invoice automation rates  Automate core Accounts Receivable processes like Credit Management, Dispute Management and Cash Application Plan to Forecast Using AI to help automate End-to-End processes Increase efficiency and reduce costs
  19. 19. 20  Automating end-to-end processes Increase efficiency and reduce costs  Detect and prevent Detect and rank information out of Big Data  Predict Derive knowledge from historical information to increase the accuracy of predictive scenarios  Proactive context-sensitive support Digital assistants boost productivity of financial experts Machine learning for finance Changing the world of finance by adding intelligence to applications Automate Predict Assist Detect
  20. 20. 21 Fraud investigators can detect unknown fraud patterns and reduce false positives leveraging their company’s investigative history – without expert knowledge in data science and algorithm tuning.  Detection of new fraud patterns  Reduction of false positives Predictive detection methods allow your business analysts and fraud investigators to  Automatically detect and rank attributes within classified data that positively correlate with fraudulent cases;  Incorporate them with existing detection methods into new fraud management strategies. Business integrity screening Detect and rank information that positively correlates with fraud Detect Fraud Reduce false positives Save money Fraud Management Team
  21. 21. 22 Assist Detect  Automating end-to-end processes Increase efficiency and reduce costs  Detect and prevent Detect and rank information out of Big Data  Predict Derive knowledge from historical information to increase the accuracy of predictive scenarios  Proactive context-sensitive support Digital assistants boost productivity of financial experts Machine learning for finance Changing the world of finance by adding intelligence to applications Automate Predict
  22. 22. 23 Augmenting financial analysis with artificial intelligence Bringing together actuals, forecast and simulation to uncover market trends before they happen Cleanse and match data from different sources Spot outliers, perform forecasts, and determine causality Share and operationalize data more intelligently Run what-if analysis and zero-in on key influencers for any business challenge
  23. 23. 24 Predictive accounting Accelerate accounting processes by machine learning powered forecasting Create a common view of all financial & operational data Predicts key accounting KPIs & how they impact key metrics Provides easily consumable reporting Anticipates the impact of currency fluctuations for the end of a period Optimize processes to close the financial books at the end of every fiscal period, and reduce costs, cycle times, and error rates. Data of former period end positions ML used to forecast costs, revenues & recurring costs Machine Learning
  24. 24. 25 Constantly-updated predictive key performance indicators Group Prediction
  25. 25. 26 Predictive accounting vision
  26. 26. 27 Detect Predict  Automating end-to-end processes Increase efficiency and reduce costs  Detect and prevent Detect and rank information out of Big Data  Predict Derive knowledge from historical information to increase the accuracy of predictive scenarios  Proactive context-sensitive support Digital assistants boost productivity of financial experts Machine learning for finance Changing the world of finance by adding intelligence to applications Automate Assist
  27. 27. 28 Digital enterprise assistants and self-service finance Business context awareness Understanding the business context, and pro-actively suggesting solutions using predictive functionality Conversational user interface Conversational interface that uses natural language processing functionality to create a human-like experience Cross applications Allows seamless transition across platforms; start a task on a mobile device and continue later, on a desktop or vice versa Self learning Using machine learning functionality to gain knowledge based on historic data, experience, and take action in response to new or unforeseen events “By 2020, the average person will have more conversations with bots than with their spouse” Gartner
  28. 28. 29 Your data is your most important asset  Investments in systems to ensure high-quality, consistent data will be even more amply rewarded  Invest in gathering leading “signal” data (e.g. social sentiment data, or foot-traffic statistics) to augmented “lagging” finance data It’s about people  Automation is a huge opportunity, but it’s also about “augmented intelligence” – displacing work, not replacing workers Watch out for side-effects  You’re delegating more decision-making to machines: make sure there’s still oversight and auditing New ways of working  Implementing AI in new ways requires new skills – your strategic partners can help! Implementing AI: requirements for success
  29. 29. Thank you. Contact information: Timo Elliott VP, Innovation Evangelist SAP timo.elliott@sap @timoelliott

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