Decision Automation

The following use case demonstrates how Decision can streamline business processes by drastically reducing the number of steps the digital worker has to go through.

An accounts worker deciding on whether a repair invoice needs to be paid or not must consider:

• Was the repair authorized?
• Does the vehicle exist?
• Do the details of the repair match our systems?
• Does the invoice have a matching purchase order?
  • If not, is the amount under a certain accepted level.

This type of administrative task is the ideal candidate for automation – and Decision can make it a reality.

The following use case demonstrates how Decision can streamline the process flow by drastically reducing the number of steps the digital worker has to go through.

An agent must consider the following (and more) when evaluating a new application:

• Is the person applying for the account a resident of an applicable country?
• Are they the appropriate age?
• Do they have an existing account?
• Are they a staff member?

Processing thousands of applications manually can become a significant time-sink – something Decision is primed to solve.

Decision automation utilizes machine learning (ML), which is a subset of artificial intelligence (AI), for automating the decision-making process, making it easier for organizations to expand their automation. It replicates human decision-making skill sets so employees can drive further value to the business.

For all decisions not covered by existing rules or training inputs, Decision’s ML model can be applied to business rules automation in four steps:

1. Define your model name, add input variables, and highlight the decision that needs to be made.
2. Create rules and provide decisions based on the variables so your model can train.
3. Calibrate your data to ensure the model is generating the correct outcomes.
4. Integrate and deploy your processes through Decision once they’re ready.

The limits of decision automation are your imagination. It can work across a multitude of industries and use cases, from financial services and banking to healthcare. It’s useful for routine decision-making processes that could be optimized, where quality and consistency are crucial.

Fully automated decision-making allows employees to focus on higher-value initiatives, further increasing productivity across business operations.

Here’s what Decision does:

• Reduces design complexity for process developers.
• Increases time-to-value with faster decision-making processes.
• Recognizes potential errors and makes decisions to repair or respond without human intervention.
• Aids in compliance by automating regulation adherence.
• Improves consistency by applying rules with 100% accuracy.

Automation works quickly and accurately, reduces errors caused by people, and improves process efficiency. Business decisions can be time-consuming, drawing employees from their other work. Automation serves to transform these processes using ML predictive models that serve relevant, useful data to users so they can make more informed decisions.

With all that, you get:

• Happier employees
• Faster processes
• Better accuracy
• Fewer risks
• Bigger time and cost savings
• Better resource allocation

Decision automation requires training your models on accurate data and ensuring your systems and people are connected.

Here are some of the challenges to look out for:

• Auditability: Ensure your decision automation includes good data security, retrieval, and storage.
• Collaboration: Some decision automation tools don’t require any human-in-the-loop, and this may make your people feel disconnected from your automated workflows. If this is a concern, look for software that allows greater visibility into the end-to-end decision-making process.
• Long-term decisions: Long-running decisions that include multiple steps can be complex, making these automations difficult to scale. Look for a decision automation platform that helps resolve these process complexities.

By the way, SS&C Blue Prism’s Decision answers these challenges head-on.