The name’s Bot, AutoBot

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Predictive modelling is a widely used process to represent existing characteristics in a data set in order to forecast future behaviours and trends. Its application spans a range of uses, such as predicting the likelihood of customers defaulting on their accounts; measuring market reception to a new product; managing reactions to governmental policy changes or even allocating health insurance premiums using the probability of patrons living riskier, less healthy lives. 

The rapid evolution of the tools involved in predictive modelling, as well as validation and implementation techniques has resulted in models achieving higher accuracy, faster computing time, and process automation. 

This blog is the first in a series of articles describing a predictive modelling tool developed at Incline, which has been created to automate data validation, classing, variable selection, model build, as well as the model validation steps for a variety of modelling needs in real world applications. 

At a high level overview, Incline’s tool, the AutoBot, features the following capabilities: 

Despite there being other predictive modelling tools available, Incline’s AutoBot offers unique value such as it being built by analysts with first-hand experience, affording model builders a seamless experience. Advantages of using the AutoBot include:

Experienced analysts will be familiar with the tedious, iterative processes of variable classing, checking variable validity and correlation between variables. The AutoBot offers exemplary speed, equipping analysts with the ability to rebuild the model[1] multiple times an hour, and providing validation statistics within an hour of loading the dataset.

Analysts can therefore better utilize their time fine tuning and optimizing the model instead of expending precious hours on building and validation, resulting in a more refined model with improved performance.

Benefits to businesses using the AutoBot are manifold; the time saving aspect of the tool allows for optimized resource allocation, lowered costs associated with model building, and a faster response time to changes in the industry. The ease of implementation and short turnaround time make it easier for businesses to make changes to scorecards, refine, and perform champion-challenger tests.

The AutoBot comes complete with a comprehensive model building training package, covering:

·        Why predictive models?

·        Types of model available in general as well as in the tool

·        The theory of building a model

·        Building a model without AutoBot using R or Python

·        Understanding the validation statistics

Further detail on the AutoBot’s particular capabilities will follow in future articles.

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