Infusing business decisions with machine learning

The data of your organization contains highly valuable information that you can use to help you make better decisions. You can integrate predictive models with decision models to gain insight into your data and make business decisions with greater accuracy.

Predictive analytics and machine learning provide valuable insights that can help businesses make more effective decisions. For example, machine learning algorithms can help identify at-risk customers, fraudulent claims, failing machines, competitive orders, and cost-effective suppliers. However, these insights must be acted upon to provide value to the organization. This can be achieved with the help of decision automation.

Integrating machine learning with decision modeling enhances business insights and predictive accuracy. These two approaches complement each other: rule-based decision modeling follows a deterministic approach, while machine learning applies a probabilistic approach to decision making.

Consider a loan application scenario. A decision model evaluates whether borrowers can repay a loan based on known inputs, such as income or credit score. In contrast, a machine learning model provides deeper insights, predicting the likely repayment amount and the probability of default.

Rule-based decision modeling and machine learning complement each other, offering the best of both worlds: predictive insights derived from historical data and prescriptive business decisions aligned with company policies.

Decision Intelligence Client Managed Software takes the complexity out of combining predictions and decisions, creating a sustainable, agile process that can be managed as business conditions change.

Remote machine learning models

You can use machine learning predictions from a variety of sources. Take advantage of Decision Intelligence's native integration with IBM Watson® Machine Learning to discover and import machine learning models directly into Decision Designer. Alternatively, you can connect to machine learning providers that are not natively supported by Decision Intelligence, such as Amazon SageMaker, Microsoft Azure Machine, Google Vertex AI, or custom services by using the IBM® Open Prediction Service API.

Diagram showing the machine learning integration workflow

Setting up the connection to a machine learning provider can easily be done from the Decision Designer interface. When a machine learning model is available in Decision Designer, you can prepare it for consumption with a step-by-step wizard and embed it into a decision model.

Local machine learning models

In Decision Designer, you can import ruleset and scorecard machine learning models in PMML (Predictive Model Markup Language) format and turn them into transparent business rules or decision tables. These rules can be easily understood and managed by business users. They are governed and executed directly in Decision Intelligence.