Use Cases Across RPA

• Email Classification

  RPA AI tools streamline the whole process of email classification, reading incoming email and using DataRobot enterprise AI to classify the request. From there, DataRobot predicts the appropriate channel for follow-up and RPA can forward the email to that department. With a timely follow-up from the assigned department, the workers in the department can focus on higher value work and get more time back to answer customer phone calls. This ultimately leads to a higher customer satisfaction and more timely responses.

• Bank Loan Origination

  RPA uses intelligent optical character recognition (OCR) to extract data from paper-based applications and make it available digitally. It performs client outreach for missing information and then routes the completed application to DataRobot to assess the risk and provide an interest rate quote. After obtaining approval, the RPA robot will then notify the customer about the decision on the loan.

• Predictive Maintenance

  Predicting when equipment will fail is a huge cost saver for businesses. Ensuring that a company has the right replacement part available and the right maintenance person on hand to fix the problem ahead of time keeps operations running smoothly. RPA helps businesses to get ahead of the problem by gathering service logs and calling upon DataRobot to flag faulty equipment or outages. It then creates alerts for areas expected to suffer issues and repairs equipment before an expensive outage can occur.

• Call Center Routing

  How a customer navigates their way through a company’s phone tree can make or break their relationship with that company. RPA walks through a number of steps that make the process seamless. After pulling the customer profile information, RPA calls out to DataRobot, which predicts which department the customer needs, as well as the customer’s lifetime value and churn risk. It allows the best call center reps to deal with the highest value customers and allows callers to get routed more effectively.

• Call Center Staffing

  By looking at historical call volume, RPA combines information and calls out to DataRobot. DataRobot Time Series can forecast expected call volume and alerts managers if they are understaffed. Staffing schedules can be built on forecasted demand, resulting in reduced waiting times and lower staff costs.

• Public Health and Safety

  Enterprise AI can be developed with data from several public sources. Using historical data such as Medicare, the National Provider Identifier (NPI) Database, and the CDC, datasets can be combined to derive an aggregate of drugs prescribed by county and opioid deaths. By understanding how all drugs are prescribed at a county level, agencies can identify relationships between non-opioid based drugs and their effect on the opioid death rate nationally.

• Employee Attrition

  By looking at historical and categorical data on its employees, a company can predict future employee churn and offset it by hiring or by instituting the organizational changes necessary to fix the problem. Further analysis can also uncover future gaps in talent that will need to be addressed to ensure optimal business performance.

• Medical Fraud

  Since it is inefficient and time-consuming to investigate every claim of medical fraud, it pays to use enterprise AI to predict fraudulent activity. Solutions can become obsolete just as quickly as they are developed, and it can be hard for your team to keep up with every claim. RPA with machine learning can monitor the threat landscape and stay ahead of fraud in real-time.