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How Predictive Models Improve Automated Decisions


Written by: Colleen McClintockPublished on: Jul 13, 2016No comments

Agility is a key focus and benefit in the discipline of decision management. Agility, in the decision management context, means being able to rapidly adjust and respond to business and market-driven changes. Decision management technologies allow you to separate the business logic from your systems and applications. Business analysts then manage and make changes to the business logic a separate environment. And they can deploy their changes with minimal IT involvement and without a full software development cycle. With decision management, changes can be implemented in a fraction of the time required to change traditional applications. This ability to address frequently changing and new requirements that impact key automated decisions makes your business more agile.

Being able to rapidly make and deploy changes is important. But how do you know what changes to make? Some changes, like those defined by regulations and contracts, are straightforward. If you implement the regulations or contract provisions accurately, the automated decision will produce the required results and therefore, make good decisions. However, many decisions don’t have such a direct and obvious solution.

When Agility Isn’t Enough

Frequently decisions depend on customer behavior, market dynamics, environmental influences or other external factors. As a result, these decisions involve some degree of uncertainty. For example, in a credit risk decision, you’re typically determining whether or not to approve a credit application and where to set the credit limit and interest rate. How do organizations determine the best decisions to help them gain customers while minimizing risk? The same applies to marketing decisions like making upsell and cross-sell offers. Which potential offer would the customer most likely accept?

Predictive Models Provide Data Insight

crystal ballThis is where predictive models help. Predictive models combine vast amounts of data and sophisticated analytic techniques to make predictions about the future. They help us reduce uncertainty and make better decisions. They do this by identifying patterns in historical data that lead to specific outcomes and detecting those same patterns in future transactions and customer interactions.

Predictive models guide many decisions that impact our daily lives. Your credit card issuer has likely contacted you on one or more occasions asking you to confirm recent transactions that were outside of your normal spending patterns. When you shop online, retailers suggest products you might want to purchase based on your past purchases or the items in your shopping cart. And you probably notice familiar ads displayed on websites you visit. These ads are directly related to sites you previously visited to encourage you to return and complete your purchase. All of these are based on predictive models that are used in the context of specific decisions.

How Predictive Models Are Built

Predictive modeling involves creating a model that mathematically represents the underlying associations between attributes in historical data. The attributes selected are those that influence results and can be used to create a prediction. For example, to predict the likelihood of a future sale, useful predictors might be the customer’s age, location, gender, and purchase history. Or to predict customer churn we might consider customer behavior data such as the number of complaints in the last 6 months, the number of support tickets over the last month, and the number of months the person has been a customer, as well as demographic data such as the customer’s age, location, and gender.

Assuming we have a sufficient amount of historical data available that includes the actual results (whether or not a customer actually purchased in the first example, or churned in the second) we can use this data to create a predictive model that maps the input data elements (predictors) to the output data element (target) to make a prediction about our future customers.

Typically data scientists build predictive models through an iterative process that involves:

  • Collecting and preparing the data (and addressing data quality issues)
  • Exploring and Analyzing the data to detect anomalies and outliers and identify meaningful trends and patterns
  • Building the model using machine learning algorithms and statistical techniques like regression analysis
  • Testing and validating the model to determine its accuracy
Data Science Process
By Farcaster at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=40129394

Once the model is built and validated it can be deployed and used in real-time to inform automated decisions.

Deploying Predictive Models in Automated Decisions

While predictive models can give us sound predictions and scores, we still need to decide how to act on them. Modern decision management platforms like SMARTS Decision Manager let you combine predictive models that inform your decisions with business rules that translate those decisions into concrete actions. SMARTS includes built-in predictive analytics capabilities and also lets you use models built using other analytics tools such as SAS, SPSS and R.

The use of predictive models is rapidly expanding and changing the way we do business. But it’s important to understand that predictions aren’t decisions! Real world business decisions often include more than one predictive model. For example, a fraud decision might include a predictive model that determines the likelihood that a transaction originated from an account that was taken over. It might also include a model that determines the likelihood that a transaction went into an account that was compromised. A loan origination decision will include credit scoring models and fraud scoring models. It may also include other models to predict the likelihood the customer will pay back early, or the likelihood they will purchase additional products and services (up-sell). Business rules are used to leverage the scores from these models in a decision that seeks to maximize return while minimizing risk.

In our next post, we’ll look at how modern decision management platforms, like SMARTS, help you evaluate alternative decision strategies. We’ll explore how you can use decision simulation to find the best course of action.


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