KPMG Signals Repository
KPMG Signals Repository
Service

KPMG Signals Repository

Our listening and scoring platform delivering Intelligent Analytics services.

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. 

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 get the edge in their decision-making.

With KPMG Signals Repository, structured and unstructured data is transformed into complex expressions, creating tens of thousands of signals when used by machine learning and other AI systems, and helps our clients significantly improve the accuracy in predictions. Signals Repository is an accelerator for data scientists and next generation developers. By creating a ‘big data fabric’ of exogenous and endogenous data, organizations can find the right data and signals to enable their AI and machine learning technologies to achieve unprecedented accuracy in predictions and business execution outcomes.

 

Listening to all the signals to help turn data into value

To optimize any business activity, you need to understand all of the data available to you — from your own internal and customer data to competitor and market data. We work with organizations to identify and collect the right signals from the growing universe of data including structured and unstructured data.

 

KPMG Signals Repository

 

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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

 

Signals Repository service offerings

With the majority of Signals geo-tagged and the majority of sources captured over time, the KPMG Signals Repository is especially helpful when building predictive services that leverage temporal-locational understanding; this is made for improved Machine Learning and better outcomes. Our services are customized to handle different requirements around input, analysis and results so that the services meet the needs of the business

Retail site selection

Identify unique drivers of demand for each trading area, then use to select locations that maximize revenue 

Employee retention

Found local market dynamics that contribute to higher employee attrition plus drive intelligently recommended fixes

Customer growth

Pinpoint local competitive offerings that leach individual customer engagement and generate smart interventions

Market monitoring

Continuously monitor entire United States, market-by-market to detect and highlight unusual market performance


Shifted demand

Watch local events, e.g. school calendars and road construction, to spot and alert to conditions that will shift demand

Intelligent underwriting

Augment traditional property & casualty underwriting models to include customer-specific exogenous factors

Branch rationalization

Isolate trading area attributes that “pull through” demand for each asset type, then leverage to “outfit” each location

Intelligent forecasting

Complement top-down Revenue Forecasting services with a bottoms-up equivalent and blend the 2 to improve accuracy


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