KPMG Signals Repository

Listening and scoring platform delivering intelligent analytics services

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

The universe of data is growing at an exponential rate, and today’s AI, cognitive and predictive systems are hungry for more. The big challenge is finding the data and signals that drive optimal business decisions. You need to understand all of the data available to you—from your own internal and customer data to competitor and market data.

KPMG Signals Repository can help. Leveraging the latest decision science, Signals Repository continuously harvests a broad variety of signals from public and private sources to help organizations improve their decision-making. Structured and unstructured data is transformed into complex expressions, creating tens of thousands of signals when used by machine learning and other AI systems—which helps our clients significantly improve the accuracy in predictions.

 

Predict what happens next

Learn how KPMG Signals Repository can help organizations find the right data and signals to achieve unprecedented accuracy in predictions and business execution outcomes. 


The value of a signal

What is the Signals Repository? A collection of sources (data) that is harnessed to interpret the impact of internal and external factors (signals) on a company’s execution, to help organizations derive insights from the patterns (indications), and to accelerate and affect meaningful decision-making on a continuous basis.  Leveraging KPMG Signals Repository, it’s easy to “listen” to the tens of thousands of Signals around us and then use Machine Learning to make sense of it all.

Data


Data

  • Crime statistics
  • Portal login time stamp
  • Wi-Fi hotspots
  • Addresses for all bank branches and ATMs in a network, and total number of FDIC insured dollars per location

Signal


Signal

  • Exponential moving average by type, by neighborhood
  • Time series representing sessions behaviors of online users
  • Net growth in population in a neighborhood over time
  • Rate of change of Wi-Fi penetration by neighborhood and daypart
  • Ratio of branches and ATMs to population per ZIP+4

Indication


Indication

  • Financial vibrancy of the neighborhood
  • Changes in user engagement
  • Erosion / expansion of specific customer segments of a business
  • Likelihood of rally in apartment and business rental prices
  • Liquidity of population in a neighborhood

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