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SMACC - Automatic Bookkeeping with AI

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How SMACC automatize bookkeeping using AI. The original presentation, you will find on github: https://github.com/wojciech12/talk_smacc_automate_bookkeeping_with_ai/raw/master/SAP_Meetup_Wroclaw.pdf

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SMACC - Automatic Bookkeeping with AI

  1. 1. November 8, 2017 Wojciech Barczyński Lead Software Developer www.smacc.io AUTOMATE BOOKKEEPING WITH AI
  2. 2. Wojciech Barczyński • Lead Software Developer, Head of Development Office - SMACC • Before: System Engineer – Lyke • Before: SAP Research (& Development)
  3. 3. SMACC automates tasks of the finance department end-to-end applying AI Deep Learning application DATA EXTRACTION FINANCIAL REPORTS ACCOUNTING PAYMENTSWORK- FLOWS CON- TROLLIN G Automated Financial Management Data entry Manual accounting and missed tax filling deadlines Outdated numbers and gut feeling Messy reconciliation and closing Missed payment targets and IBAN typing Receipt stamps and signature folders
  4. 4. SMACC‘s AI Extractors offers extraction of 70 data fields and all invoiced items Scalable Seamless integration High Availability & Secure Modern, RESTful API
  5. 5. FLEXIBLE INPUT PLUG & PLAY IMPLEMENTATION CONTINUOUS IMPROVEMENTS FULL SCALABILITY GOOD GENERALIZATION Challenge: • > 300 000 documents in system • > 10 000 different layouts Template & Rule based approaches: • Limited generalization • Maintenance and updating is time consuming & error prone Why Deep Learning for Extracting Invoices?
  6. 6. PDF / scanned document OCR Characters + Regions + Segments Bidirectional GRU RNN Labels Bidirectional GRU RNN for extracting data
  7. 7. Automatic accounting using NLP & Neural Networks
  8. 8. Challenge: • Extract domain specific context from position descriptions • position descriptions are short, keywords sometimes help, often not Bag of words, n-grams (word2vec – exp.) Additional input: Client ID, customer name, tax rate etc. Client specific approach Feed to fully connected Neural Network NLP & Neural Networks for accounting
  9. 9. Source: AP automation study 2014 of Institute of Finance Operations, Kofax & SSON 2016, Techvalidate 2017, University of Mannheim, Thomson Financials, client case studies Processing time Processing cost Error rates 12 days 0.5 days 5 € 1 € 3 % 1 % w/o SMACC with SMACC w/o SMACC with SMACC w/o SMACC with SMACC - 95 % - 80 % - 65 % Automation with AI results in drastic improvements within finance departments
  10. 10. Market value SME finance software in Germany 3bn € Spending for manual processing of SMEs in Germany 43Bn € x14 Source: Deutsche Bank, PWC, destatis, own assumptions Automating the finance department is a huge market opportunity
  11. 11. Movinga case study: things are moving Starting point mid 2016 • ~ 1‘000 paper based invoices per month • Processing time of ~ 20 days • Error rates ... (high) • No transparency in payment process • Cost per invoice > EUR 10 • Accounting team of 8 FTE Smacc impact today • > 3‘000 invoices per month all processed with SMACC • Processing time < 12 hours • Error rate < 1% • Cost per invoice EUR 1 • Accounting team of 3 FTE COMPANY PROFILE • Leading online moving marketplace • Headquartered in Berlin • 250 employees • Active in 6 countries • Hyper growth • Troubledwatersandrestructuringoverlastmonths
  12. 12. Demo Installation at McKinsey & Company
  13. 13. • Integrations • Refactoring platform for the growing number of customers • Support new use cases, more more APIs Next Steps
  14. 14. Thank you! December 8, 2017 Wojciech Barczyński wojciech.barczynski@smacc.io www.smacc.io
  15. 15. Spark Finalist & 2017 German Accelerator

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