Appian Intelligent Decision Maker

Overview

Power your case and process management automation with ‘Appian Intelligent Decision Maker’, a solution with Machine Learning (ML) at its core. Appian Intelligent Decision Maker helps you process multiple data points, rules and conditions in real-time, and ultimately take appropriate decisions. This intelligent automation of your decision making process allows you to deliver superior end-user experience faster. The solution is empowered to solve multiple cases every minute, at a higher accuracy.

There is an existing claim process which involves multiple levels of approvals with human intervention is needed. There are few important steps, which are Pre-Authorization and Post Authorization of the claim, currently these steps are manually processed and taking longer than expected SLA's as number of transactions are high. To showcase the capability of automate decision making the Pre-Authorization step has been configured to get the decision outcome from the Logistic regression model trained in the AWS Sage maker. The Key features(Claim Amount, Coverage Amount, Comorbidities, Is Admission due to pre-existing disease? etc.)have been configured under the Decision configuration. Whenever Preauthorization step Approval is needed then the corresponding key features values will be sent to AWS Sage maker endpoint to get the result.

Key Features & Functionality

For any kind of business process/case management there involve sequence of steps and each step would be associated with key data points ,which will play the crucial role for taking the decision outcome . As and when the number of key datapoints are increased(>20), it is very difficult for end user to verify the each datapoint along with different permutations and combinations to meet the SLA as transactions are getting grown. Leveraging the Appian and ML capabilities to automate the decision making using Predictive model.

  • Seamless ingestion of the Appian configured Business process transactional data into the AWS for training the Predictive model
  • Turnaround time is less to configure the Business process and less effort required to train the Predictive model.
  • Higher accuracy/ Less cost involved ,any existing ML predictive model from leading Cloud provider can be leveraged.( AWS Sage maker)
  • Little training is enough for the model for prediction.
  • Efficient computation as the smaller number of system resources needed.
Anonymous