Leveraging Technology for Cash Flow Forecasting

March 29-30th, 10am EST, 3pm BST

Accurate cash flow forecasts have always been essential to treasury.

The Covid-19 pandemic has brought cash flow forecasting front and centre as liquidity dries up and uncertainty rises dramatically through the different waves.

This course uses hands-on Excel methods to help you understand the mathematical techniques used to forecast cash flows, and worked case studies to demonstrate how to use them.

Who should attend

This course is designed for treasury and finance professionals who are responsible for or involved in cash flow forecasting and seek to learn the best techniques available in times of uncertainty.

Learning objectives

By the end of the course, you will be able to:

  • Make accurate cash flow forecasts to:
    o manage bank balances day by day
    o manage subsidiary funding month by month
    o manage group level cash needs
    o manage foreign exchange exposures
  • Use integration with APIs and file transfer
  • Statistically forecast cash flows from available historical and forecast data
  • Evaluate existing CFF models and processes and design new ones
  • Determine the best system solutions for your CFF needs, taking into consideration your current IT ecosystem

Key topics

  • Cash positioning
  • Subsidiary cash flow forecasting
  • Group level cash flow forecasting
  • Foreign exchange exposure forecasting
  • System integration for cash flow forecasting
  • Statistical methods for cash flow forecasting
  • Cash flow forecasting model and process design and evaluation
  • Systems used for cash flow forecasting

Additional benefits

  • Simply log in to the sessions from your desk
  • Email access to your tutor between sessions
  • Homework assignments and/or group exercises with tutor feedback

Course agenda

  • Start time: 3:00 pm End time: 5:00 pm

    1. Different types of cash flow forecasting

    • Cash positioning
    • Annual and long term cash flow forecast
    • Foreign exchange exposure forecast

    2. Data collection

    • Collecting data from disparate systems using APIs
    • Collecting data from disparate systems using middleware
    • Storing collected data in data warehouses
  • Start time: 3:00 pm End time: 5:00 pm

    3. Data analysis

    • Statistical methods for data analysis
    • Using machine learning for data analysis
    • Pattern analysis

    4. Data visualisation

    • BI and data visualisation tools
    • Data visualisation tool selection
    • Pre format and self service

    Case studies

    • Using statistical methods to forecast cashflows from sales projections (hands on with Excel)
    • Aggregating data from disparate sources
    • Implementing AI forecasting for subsidiary cashflows