SAS Viya

1. Connect to transaction data from various sources.

2. Use SAS Viya Copilot to identify potential fraud indicators.

3. Build machine learning models to predict fraudulent transactions.

4. Deploy the model to flag suspicious transactions in real-time.

5. Monitor model performance and update as needed.

1. Collect sensor data from manufacturing equipment.

2. Use SAS Viya to identify patterns indicative of equipment failure.

3. Build predictive models to forecast maintenance needs.

4. Schedule maintenance proactively to prevent breakdowns.

5. Optimize maintenance schedules based on model predictions.

1. Gather customer data from CRM and billing systems.

2. Use SAS Viya to identify factors contributing to customer churn.

3. Build machine learning models to predict customer churn.

4. Implement targeted interventions to retain at-risk customers.

5. Measure the effectiveness of retention efforts.

1. Connect to policyholder data and claims history.

2. Use SAS Viya to identify risk factors.

3. Build models to predict potential claims and losses.

4. Adjust premiums and policy terms based on risk assessments.

5. Monitor portfolio risk and update models as needed.

1. Integrate data from inventory management, sales, and logistics systems.

2. Use SAS Viya to forecast demand and optimize inventory levels.

3. Build models to predict potential supply chain disruptions.

4. Optimize logistics routes and inventory placement.

5. Monitor supply chain performance and adjust models as needed.