Predictive Analytics

predictive-analytics

We develop and utilize predictive analytics to sort through historical and real-time data; formulating models and analyses that project possible future outcomes. With the right data, model, and assumptions, businesses can avoid past mistakes, duplicate success, or change course to reach their goals.

Prescio uses predictive analytics to achieve the following:

  • Detecting fraud in financial transactions
  • Reduce risk to minimize exposure and loss in loan models
  • Decrease the number of equipment failures and reduce maintenance costs
  • Improving cross-selling opportunities through personalized offers and experiences

Our predictive models rely on the following statistical techniques:

  • Linear Regression Models
  • Discrete Choice Models
  • Logistic Regression
  • Multinomial logistic regression
  • Probit Regression
  • Time Series Model
  • Multivariate Adaptive Regression

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