Robotic Process Automation
We’re all familiar with work that suffers from the three R’s – Routine, Repetitive, and Rules-driven actions. Robotic Process Automation (RPA) solutions aim to automate mundane back office work. By replacing full-time equivalent (FTE) hours with machine-based learning, departments like HR, financial services, and call centers can focus on more meaningful and strategic goals.
Learn how organizations use automated machine learning with robotic process automation software.Request a Demo
Robotic Process Automation (RPA) + AI
RPA lays the groundwork for enterprise AI and more intelligent applications. If a company has an RPA solution in place, it generally means that they have an automation and data-first mentality and are probably ready to invest in more complex machine learning models to solve their business problems. It also means that their data is ready to be put in a format that can be used for enterprise AI models. Once an organization has seen the levels of productivity that are possible with RPA and AI, many are eager to embrace more complex use cases and digital strategies.
Gain Efficiency and Accuracy
- Let bots handle repetitive and mundane tasks or scale human tasks
- Automatically collect, structure, and label data that is required for enterprise AI to have utility
Enhance the Rules-Based Process
- Provide intelligence to robotic processes that can evolve and improve as more data gets examined and trained
- Increase worker performance, reduce operational risks, and improve response times
End-to-End AI-Driven Process Automation
- End-to-end process orchestration, process application, and modeling
- Humans, robots, and systems working together to make smarter decisions outside of the defined rules
AI Use Cases Across RPA
Robotic process automation companies are able to gather data needed to run predictive models in DataRobot.
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.
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.
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.
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.
DataRobot’s platform makes my work exciting, my job fun, and the results more accurate and timely – it’s almost like magic!
I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.