Forecasting Financial Metrics using Machine Learning and Data Science
0 likes60 views
The document discusses the application of machine learning and data science in forecasting financial metrics at Bell Canada Enterprises (BCE), emphasizing innovative products and services offered by the company. It outlines the financial planning and analysis process, including the use of data science methods like driver-based modeling, AI modules for scenario planning, and continuous forecasting updates. The document also provides insights for finance executives on implementing data-driven approaches in financial forecasting.
Forecasting Financial Metrics using Machine Learning and Data Science
- 1. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org Forecasting Financial Metrics using Machine Learning and Data Science Vijay Dakshinamoorthy Senior Manager (Data Science), Finance Bell
- 2. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org Agenda 1. About BCE 2. Forecasting and FP&A 3. Data Science Methods 4. AI Modules 5. Evaluation 6. Insights for Executives
- 3. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org Bell Canada Enterprises (BCE) Canada’s Largest telecommunications Company 3 About BCE About BCE About the Financial Analytics COE About the Financial Analytics COE Delivers a wide range of innovative products and services to consumers, businesses and government customers. • Mobile data and voice plans for 4G LTE, 5G and 5G+ wireless networks • Fibe Internet / TV, Wireless Home Internet. Residential/business voice, cloud-based, mobile edge computing, Internet of Things (IoT) • Bell Media operates media brands such as CTV, RDS, Crave and iHeart radio. Leading investor in content creation, sports / entertainment and original TV and film • One of Canada’s major retailers with more than 8000 retail points of distribution across Canada. • Finance 2025 vision to better leverage data, tools, systems and processes to enable a more effective and efficient finance function • 59 team members across Data Intelligence (Data Engineering, Data Science, Service Management), Business Insights and Visualization, Data Strategy & Architecture, Program Office • Standardize and streamline processes, drive efficiencies and leverage Artificial Intelligence (AI) and machine learning (ML) as key enablers About BCE
- 4. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org Financial Forecasting at Bell • Business Units prepare their plans internally during August and September • The plans are submitted to corporate in October and reviewed by Senior Executives • In early December the plans are approved and detailed budgets are developed by Business Units and documented in the systems Business Unit Plans Business Unit Plans • An update to the in-year budget, also known as YEE (Year End Expectancy) • 3 forecasts are submitted to corporate in the months of April, July and October • Additional forecasts may be produced based on corporate’s needs Forecasts Forecasts • SAP P-50 – SAP instance • P58 – BI (Business Intelligence) • BPC (Business Planning and Consolidation) Systems Systems Forecasting and FP&A
- 5. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org Next Generation Financial Planning & Analysis (FP&A) • 12-24 months forward; routine processes automated Rolling Forecasts • Incorporate external drivers; collaborate with operations Driver-based Modeling • Annual event to continuous process; Scenario modeling Planning • Centralized data warehouse with integrated forecasting and planning processes Integrate processes, systems and data • Qualitative and quantitative measures beyond forecast accuracy Measuring Impact Forecasting and FP&A
- 6. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org Data Science Methods for Forecasting and Planning Data Science Methods
- 7. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org Forecasting Financial Metrics with Data Science at Bell Data Science Methods
- 8. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org Driver-based Models and Explainability Data Science Methods Driver-based modeling fits well with rate, volume forecasts and a P x Q calculation of final forecasts. Having components of forecasts also yields well for explainability.
- 9. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org AI Modules to support FP&A AI modules are re-usable components that can be applied across multiple business unit use cases. These modules use fairly sophisticated algorithms to detect patterns in data. • Scenario Planning – Sensitivity analysis – Forward Scenario modeling – Reverse Scenario modeling or Optimization • Root Cause Engine • Anomaly Detection • Commentary engine • Virtual reporting agent AI Modules
- 10. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org AI Modules at Bell AI Modules
- 11. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org Evaluating Forecasts Evaluation
- 12. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org So What for Finance Executives Insights for Executives
- 13. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org Where to Start? Insights for Executives
- 14. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org Tips for data-driven FP&A 1. Start small and iterate 2. Ensure sufficient historical time series and account for COVID periods 3. Balance accuracy with other benefits (timeliness, automation, granularity) 4. Incorporate external drivers (Driver based modeling and Scenario modeling) 5. Aim for explainability and transparency 6. Adopt a product management and agile data science mindset 7. Measure impact and refine Insights for Executives
- 15. ©2023 Institute of Business Forecasting | All Rights Reserved | www.ibf.org THANK YOU! Vijay.dakshinamoorth@bell.ca