The Big Business of Big Data in Liquidity Management How Big Data Can Revolutionize Your Cash Forecast

Transcription

1 The Big Business of Big Data in Liquidity Management How Big Data Can Revolutionize Your Cash Forecast Mid Atlantic Association for Financial Professionals (MAAFP) Mark O

2 Big data?

3 What is Big Data? Process of using technology to turn large amounts of data into actionable insight WHAT HAPPENED? Descriptive analytics Diagnostic analytics WHY DID IT HAPPEN? WHAT WILL HAPPEN? Predictive analytics Prescriptive analytics WHAT SHOULD I DO?

4 Big Data is now beyond the trough of disillusionment Peak of Inflated Expectations Plateau of Productivity VISIBILITY Slope of Enlightenment Technology Trigger Trough of Disillusionment TIME

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6 Data Analyst Platforms BI Platforms Computer Vision Search Data Science Platforms Visualization Machine Learning Horizontal AI Speech & NLP Log Analytics Social Analytics Web/ Mobile/ Commerce Analytics

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8 Data & Analytics Cause & Effect Mobile Social Media Internet of Thing (IoT) Cloud Big Data Millennials as Agents of Charge Marketplaces & Sharing Economy Visualization & Gamification SOCIALISATION OF FINANCE CONSUMER EXPERIENCE OPERATIONAL EFFICIENCY NEW BUSINESS MODELS FS PRODUCT & SERVICE DISAGGREGATION

9 Big data? - Applications Source: Experience UX

10 pre-hurricane consumption prediction

11 Source: Kellogs.com

12 GPS based on (realtime) distributed data

13 Big data? - Applications Logistics prediction & optimization Source: UNIglobalunion.com

14 Big data? - Applications Data-infused production (association rule learning)

15 An organisation typically uses less than 1% of the collected data to make decisions Source: Unlocking the potential of the internet of things, 2015 McKinsey & Co

16 Cash flow forecasting remains a treasurer s priority Source: PwC The virtual reality of treasury 2017

17 Big data in finance & treasury

18 Identifying fraud through irregular patterns

19 Big data The Four V s VOLUME Scale of data VARIETY Different forms of data BIG DATA VELOCITY Analysis of streaming data VERACITY Uncertainty of data Source: IBM datahub

20 Cash forecasting

21 Cash forecasting VOLUME Scale of data Do you use a lot of data? (more than transactions) to predict cash forecasts?

22 Cash forecasting VARIETY Different forms of data How many data sources do you use to make a cash flow forecast? (ERP, CRM, Bank statements, trend analysis, manual data )

23 Cash forecasting VELOCITY Analysis of streaming data How often do you refresh your short-term/mid-term cash forecast? (Daily, Weekly, Monthly, Quarterly, I don t make a cash forecast)

24 Cash forecasting VERACITY Uncertainty of data How do you ensure no mistakes happen in your data capturing/consolidation?

25 Challenges in Cash forecasting

26 What does treasury look like today? Treasury today unintegrated systems & spreadsheets, siloed by group or region, with a lack of visibility Cash System & Bank Portals TMS FX system ERP Subledger system Finance/ Excel Excel Risk System TREASURY Cash Management Risk Management: FX & IR Hedges ACCOUNTING Monthly Reconciliation & Closings FP&A / PROCUREMENT Working Capital. Liquidity, FX & Price Volatility Hedges High IT Support

27 ERP $ ERP $ Multi - Entity Multi - Bank Complex ERP Multi - Currency ERP Cash Management in global & complex businesses is challenging

28 Challenges in Cash flow management & forecasting Admin & Analysis Number crunching & data analyses Time-consuming processes Few people involved & no corporate cash flow awareness ERP XLS Banks Follow-up Absence of targets Only yearly Action Less time left for actions Low accuracy, so more buffers

29 Forecast accuracy is a for 75% of treasury professionals

30 How do we tackle these challenges?

31 The key to an accurate and efficient cash forecast? A well-thought-out process! And Technology!

32 Effective Cash Flow Forecasting in 6 steps Understand your working capital drivers Understand your cash flow drivers Define your Cash forecasting Define your Cash forecasting Integrate and horizons 03 sources per time consolidate the horizon 04 data 05 Define your cash forecast logic / assumptions 06

33 Know WHY you are forecasting 00

34 Understand your working capital drivers 01

35 taking into account all historic invoices per individual customer 01

36 taking into account all historic payments per individual customer 01

37 taking into account your full supply chain 01

38 Understand your cash flow drivers - What s eating or feeding your cash 02

39 Understand your cash flow drivers - What s eating or feeding your cash 02

40 General Ledger vs MT940 rule-based allocation methodology MT940 rule-based General Ledger-based Not granular (Opex vs Capex) High maintenance due to an ever-growing set of rules Uses the existing Accounting entries & the strength of the Accounting capabilities of the ERP system Far less rich information Low maintenance: a Chart of Accounts mapping, which is easier to understand and maintain than a MT940 mapping structure

41 Define your Cash forecasting horizons 03 Liquidity Planning / Rolling Forecast Cash Flow Analysis Liquidity Forecast Cash position History Short term Mid term Long term Time horizon

42 Define your Cash forecasting sources per time horizon 04 Business data from systems (ERP / CRM / HRM / Budgeting systems...) (External) Market data Treasury data Banking data Manual data Calculations & Trend analysis on all of the above Order-to-Cash transactions Purchase-to-Pay transactions Forecast-to-Fulfil transactions Payroll Budgets... Credit ratings Payment behaviour Tax rules / restrictions Growth expectations Cost price indices... Loans, Deposits, Hedges... MT940, MT942 BAI2... Capex Projects i.e. Payment behavior, scenario analysis

43 Not all data users (or their needs) are the same TYPES OF DATA USERS HIGH TECHNICAL SKILL ANALYST What the Analyst need the data for Identify patterns Highlight key events Custom analytics Run reports I would like the ingredients and granular data. Consolidated data repository Key insights tool SCIENTIST What the Scientist needs the data for Summarize insights Solution development I d like aggregated variables and Insights that support strategy. Simplifies data Identifies strategic insights/solutions LOW TECHNICAL SKILL SUBJECT MATTER EXPERT What the Expert needs the data for Growth Marketing Product opportunities improvement Save money TACTICAL Product targeting I d like insights and reports relevant to my role. Identifies trends xxx EXECUTIVE What the Executive needs the data for Strategic insights Growth opportunities I want to measure how our decisions are impacting the business. Health of the business dashboard Headline to root cause analysis Tool to take actions quickly STRATEGIC

44 Let s bring it alive! Contact: Mark.OToole@cashforce.com