Potential Fraud or Waste
Description
Quickly develop predictive analytics based on historical values captured and assessed within the Data Analytics Service (DAS) Dashboard. The dashboards allow various purchase card managers to monitor purchase card spending of employees. The dashboards help monitor for fraud, waste, and abuse, and assist in providing oversight for compliance with purchase card laws and policies. We have now incorporated advanced data analytics within the dashboard. The Advanced data Analytics model generates an expected received date for items. It then flags items that have gone beyond the expected generated date by flagging items that were received late (possible waste) and/or items that have not been received yet (possible fraud). This has the potential to save the analyst time by investigating cases that have been flagged as opposed to investigating potentially an entire dataset, which is what they have been doing up until now. The model analyzes all purchase card transactions including ordered, delivery, and received dates as its inputs. It outputs expected received dates. The intended users of the product are purchase card managers.
Detailed example
Predictive analytics based on historical values captured and assessed within the Data Analytics Service (DAS) Dashboard.
AI / analytics pattern
Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
- Delivers high value to Customers, Partners, and Stakeholders by enhancing user efficiency and analytical capacity. - Customers receive quick responses to data analysis questions, - Customers can communicate requests to the Chatbot using natural langue format, - Provides quick and accurate answers to inquiries - Provides 24/7 support - Reduces support team emails - Provides support for ad hoc data analytics requests - Improves scalability to support customers.
Controls / human review
ATO: Not reported; PIA: Not published