Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/4677
Title: | Making Federal Financial Data More Reliable With Emerging Tech |
Authors: | David I. Gill, Avram Ibrahim Chaudry Umer, Sonia Jolly Alicia M. Miller |
Keywords: | Natural Language Processing (NLP) Robotic Financial Data Procurement Process Automation |
Issue Date: | 6-May-2022 |
Publisher: | Acquisition Research Program |
Citation: | Published--Unlimited Distribution |
Series/Report no.: | Acquisition Management;SYM-AM-22-163 |
Abstract: | Federal agencies are stewards of billions in taxpayer funds. Given the scale of federal financial transactions, maintaining reliable, high-quality financial data can be challenging. The use of emerging technologies such as robotic process automation (RPA) and natural language processing can reduce manual work for agency employees and improve the consistency of financial data. These technologies are key to success on financial audits and maintaining public confidence in the reliability of procurement and nonprocurement financial information. |
Description: | SYM Presentation |
URI: | https://dair.nps.edu/handle/123456789/4677 |
Appears in Collections: | Annual Acquisition Research Symposium Proceedings & Presentations |
Files in This Item:
File | Description | Size | Format | |
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SYM-AM-22-163.pdf | Presentation | 439.29 kB | Adobe PDF | View/Open |
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