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

1. November 8, 2017
Wojciech Barczyński
Lead Software Developer
www.smacc.io
AUTOMATE
BOOKKEEPING
WITH AI

2. Wojciech Barczyński
• Lead Software Developer,
Head of Development Office - SMACC
• Before:
System Engineer – Lyke
• Before:
SAP Research (& Development)

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Outdated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. SMACC‘s AI Extractors offers extraction of 70
data fields and all invoiced items
Scalable Seamless
integration
High
Availability &
Secure
Modern,
RESTful API

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. PDF / scanned
document
OCR
Characters + Regions
+ Segments
Bidirectional GRU RNN
Labels
Bidirectional GRU RNN for extracting data

7. Automatic accounting using NLP &
Neural Networks

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. 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. 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. 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. Demo Installation at McKinsey & Company

13. • Integrations
• Refactoring platform for the growing number of customers
• Support new use cases, more more APIs
Next Steps

14. Thank you!
December 8, 2017
Wojciech Barczyński
wojciech.barczynski@smacc.io
www.smacc.io

15. Spark Finalist & 2017 German Accelerator