Breakout Session AI and machine learning tooling

Transcription

1 Breakout Session AI and machine learning tooling

2 Presenters Peter Sergio Larsen Rasmus Kær Jørgensen Head of AI, Data Science & Data Engineering BSc. Human Nutrition & Biochemistry MSc. Biomechanics Data Scientist BSc. Engineering MSc. Computer Science

3 Agenda 1 Presenting and introducing AI, machine learning tooling in the context of the finance function Machine Learning Case Study 2 Full Implementation & Life Cycle Management 3 Live Example: Face Detection 3

4 Setting the Scene Before taking the deep-dive, we must build some context: - Challenges of the Finance Function - Can technology solve some of the challenges? - Introducing AI, machine learning and friends - Navigating in a world of buzzwords PwC s Digital Services 4

5 AI and machine learning tooling Context The Finance Function

6 Challenges of the Finance Function Regulations & Legislations Organization Technologies Imposed change Less risk, more compliant Organizational change More efficient Data-driven 6

7 Considerations How can we automate certain processes? Can technology help us deal with our challenges? What should we do to meet risk and compliance demands How can we decrease cost and errors? We might have a issue! 7

8 AI and machine learning tooling Introduction Into the matrix of AI and machine learning tooling

9 Definitions Machine Learning is a concept in which computers are not directly programmed, but are able to learn from algorithms and. Learning from < Big Data Machine Learning Deep Learning Deep Learning is a subset of Machine Learning, which concerns algorithms inspired by the structure and function of the brain called neural network.

10 From idea to deployment Part a) Classification of Transactions (example) Start by taking a deep-dive into a machine learning project By example, we want to show the process from business case to solution Part b) Implementation & Life Cycle Management Zoom out. A road map for AI and machine learning tooling By example, we wish to discuss how to put a solution into practice PwC s Digital Services 10

11 AI and machine learning tooling Case Study Classification of Transactions

12 Presenting the Problem What do we want to do? To go from yearly or quarterly reporting to immediate by automating bookkeeping and integrating the information into the enterprise. Why do we want to that? This will not only automate bookkeeping and build a good foundation, but also enable many other highly attractive options.

13 Show Me the Data What do we need to solve? To build the foundation needed for machine learning 1. Data from bank transactions It is difficult to be ambitious with very little information Date Amount Text / comments ,29 Internet ,04 B126H fees 2. Invoice & Appendix PDF document cannot be exported to spreadsheets We have a problem!

14 The Data Problem Consequently, we have do a bit of engineering How can we retrieve the from the invoices? Since the invoices contains information about the companies Can we then find some information about the companies to include in the set? + = Let us investigate

15 Scanning Invoices, Appendixes & Accessing Public Data Scanning Invoices & Appendixes There exists an array of tools for extracting information from invoices and appendixes Accessing Open Public Data Governments and public institutions are making and information freely available How does it look when everything is combined?

16 The Data Transformation Before: Bank Data BANK DATA Bank Bank Bank After: Bank Data Invoice & Appendix Data Open Public Data BANK DATA INVOICE DATA OPEN PUBLIC DATA Invoice Invoice Invoice Invoice Invoice Invoice Invoice Invoice Invoice Invoice Invoice OpenPublic OpenPublic OpenPublic OpenPublic OpenPublic OpenPublic OpenPublic OpenPublic OpenPublic OpenPublic OpenPublic OpenPublic Bank Bank Bank

17 Deep Learning Automatic Classification of Transactions Adjustment Error? Data Spending pattern Accounts Back into operations Company information Textual information New epoch Amount & numbers Classification

18 Implications Automated Bookkeeping Automatically assigning transactions to accounts Transfer Pricing Being able to monitor cost on a day-to-day / week-to-week basis, and therefore, decrease or avoid year-end adjustments Value Added Taxes Transaction-based VAT Live Financial Statement Being able to monitor financial activities on a day-to-day / week-to-week basis instead of quarterly or annually

19 AI and machine learning tooling AI, Machine Learning and Analytics Full Implementation & Life Cycle Management

20 Data Sourcing Data Sourcing Typical Data Sources We need different sources to build a accurate model Data often comes from all kind of systems CRM ERP Media Transactions Accounts We need to have the right technology that enables agility Modelling is an iterative phase where experimentation is required and different fields (X s) needs to be explored The computing speed is essential for fast modelling and processing

21 Data Pre-Processing Bad Quality Zoom & Understand I have never seen consistent in any enterprise Crucial to run quality and profiling Important to understand how to deal with bad quality before you start modelling Essential to take an iterative approach

22 Modelling Phases of Modelling Experimentation Feature engineering Training selection Test selection Cross validation selection Model testing and benchmarking Hyper parameter tuning Final model selection

23 What about deployment and maintenance? Deployment & Maintenance The Real Value Some of the most underestimated phases today in AI is the deployment phase Deployment of AI is essential if you want to affect the business and create real value for the business Deployment requires involvement from the business users since they will use the output from the model AI is an ongoing process. The system needs to be designed so it can learn and become better over time.

24 Tooling in PwC Tooling Tools we use in PwC Tooling and the right combination of technology are essential Bad tooling can harm the whole system Increasing the cost Creating an unnecessary complexity Not fitting the rest of organization or enterprise architecture Tooling needs to be transparent Avoid vendor lock-in Tools change fast

25 Organisations and Resources in PwC Generalist Specialist Centre of Excellence Generalist Specialist Centre of Excellence Description We need most employees in to be active players in understanding the value of and analytics and several employees to understand the new technology and how to use it. Specialist teams will work closely with the Centre of Excellence to leverage the paradigm of and analytics, scope solutions and jointly work on use cases and roadmap. Resources dedicated to deliver on the Data Analytic. They may be part of other teams but must be designated to deliver according to the joint strategy and use the same tool box. The Centre of Excellence must prepare a joint roadmap, facilitate the building of the necessary competences (technology and skills) and manage the process defined.

26 Next Steps 1. Try to identify 2-3 use-cases where AI/ML can add value 2. Take one use-case and break it down to understand the requirements 3. Identify skillset / resources and technology stack available in the company A. Do you have everything you need, then start with a small POC (start small scale fast) B. If you miss something, then focus on: i. Should you get it ii. Should you partner up with others who have it iii. Cost vs. Business case

27 AI and machine learning tooling Live Example: Face Detection

28 AI and machine learning tooling Live Example: Bad Data Quality

29 Standard Data Quality Problem Different date-time format often occurs in enterprises with more than one system. 1. ( CRM:', '3 May 1979') 1. (Stand:', ' ') 2. (ERP:', '5 April 09') 2. (Stand:', ' ') 3. (Account:', '21th of August 2016') 3. (Stand:', ' ') 4. (Customer:', 'Tue 10 Jul 2007') 4. (Stand:', ' ')

30 How can we help AI and machine learning tooling from a POC to a fully operational product/system Many companies are talking about AI, machine learning, science etc. All hot topic that have a big potential to transform many existing business processes by increasing efficiency and productivity How do you get from a POC to a full-blown enterprise-ready AI system? How do you measure the real business value of your AI model? How do you scope an AI POC, and what pre-requisites need to be in place? Should you buy a product or develop your own? For more information, please contact: Peter Sergio Larsen Director T: (+45) plz@pwc.dk Rasmus Kær Jørgensen Assistant Consultant T: (+45) rkr@pwc.dk