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KPMG Intelligent Forecasting

Applying advanced analytics and artificial intelligence (AI) to power decision-making and improve profitability

Sanjay Sehgal

Sanjay Sehgal

Principal, Finance Transformation, KPMG US

+1 216-875-8113

Brett Benner

Brett Benner

Managing Director, Finance Transformation, KPMG US

+1 267-256-2959

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
 

50% more accurate


400-500 bps improvement in gross margin accuracy

95% sales accuracy


through 40+ new external data and signals

35% faster


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. 


 


Forecast customization

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.