RPA에 관한 분석 과제

• 이메일 분류

  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.

• 은행 신규 대출

  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.

• 예지 정비

  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.

• 콜센터 라우팅

  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.

• 콜센터 인력 관리

  RPA는 통화량 이력을 보고 정보를 결합하며 DataRobot을 호출합니다. DataRobot 시계열은 예상 통화량을 예측하고 인력이 부족한 경우 관리자에게 경고합니다. 직원 일정을 예상 수요에 따라 구축할 수 있어 대기 시간이 단축되고 인력 충원 비용이 줄어듭니다.

• 공중 보건 및 안전

  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.

• 직원 이탈

  직원에 대한 이력 및 범주 데이터를 살펴보면 회사는 향후 직원 이탈을 예측하고 문제 해결에 필요한 조직 변경을 실행함으로써 이를 해결할 수 있습니다. 또한 추가 분석을 통해 최적의 비즈니스 성과를 보장하기 위해 해결해야 할 미래의 인재 격차를 파악할 수 있습니다.

• 의료 사기

  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.