Reinvent the planning process to gain a competitive advantage
Amid uncertain times and the growing pressure for accurate insights, leading finance organizations are turning to data and analytics. With KPMG Intelligent Forecasting, finance leaders are creating a planning process that works for them—all based on predictive modeling and advanced analytics.
This provides visibility into what exactly drives the business and allows you to:
- Improve accuracy and confidence in budgets and forecasts
- Identify the true drivers of business value, both internal and external
- Set data-driven financial targets, based on a machine learning approach
- Determine and eliminate bias from forecasts
- Leverage predictive modeling and external signals
- Link decision-making with profitability
We utilize AI technologies in the KPMG Ignite platform to help make your forecast intelligent and actionable.
Improve your forecast accuracy
KPMG’s Sanjay Sehgal explores how AI is able to analyze external data and signals to provide organizations with more accurate earnings forecasts.
KPMG Intelligent Forecasting business value
Sample results we've seen
400-500 bps improvement in gross margin accuracy
through 40+ new external data and signals
quarterly forecasting process, through use of predictive models
How does Intelligent Forecasting work?
Combining deep industry knowledge with advanced technical capability and enablers, KPMG Intelligent Forecasting uses a data-driven and machine learning-powered approach to planning. Tailored to meet the needs of each company, KPMG Intelligent Forecasting is based on a core set of foundational components:
Advanced predictive techniques
Various types of time series and machine learning techniques are rapidly tested to help predict specific business unit(s) and P&L line(s) in scope.
Better data and signals
Incorporate better external data and signals from KPMG Signals Repository along with internal financial or operational drivers to add context and predictive power.
Create specific forecast by P&L line item, time interval, geographic region, product line, brand, customer, or channel.
Continuous learning and evolution
Incorporate structured feedback loops which enhance prediction accuracy over time.