Federal Technology Trends for Combating Waste, Fraud and Abuse
Published: January 03, 2019
With federal improper payments totaling nearly $151B in FY 2018, 4.6% of federal program outlays, federal agencies are using IT solutions as part of an overall program integrity framework to reduce waste, fraud and abuse (WFA) in programs.
Waste, fraud and abuse is prevalent in a variety of mission areas, such as health care, tax programs, benefits, nutrition programs, and contracting. Technology is only one component of broader enterprise risk management, program integrity and fraud risk management efforts. However, it plays a critical role in preventing, detecting and recovering improper payments in most federal programs.
Deltek’s analysis of IT contract spending data showed that agencies spent $738M from FY 2015 through FY 2017 on IT products and services to reduce waste, fraud and abuse. Analysis of professional services contract spending data showed $1.4B of agency spending on contractor services to lower improper payments for the same time period.
A multitude of technologies and services are in use today by federal agencies for managing and protecting program integrity. Deltek’s recent research indicates that federal agencies are investing in technology solutions such as data management, storage, analytics, and artificial intelligence to thwart efforts to defraud the federal government and limit erroneous payments. However, the diversity of program missions, beneficiaries, recipients, users, systems, processes, technologies and solutions used differs among agencies.
Although not an IT solution specifically, a critical component to combating WFA in federal programs is establishing a framework. Frameworks for increasing program integrity and combating WFA, such as ERM frameworks, internal controls, payment integrity processes, and fraud risk management frameworks provide governance structures, controls, and procedures to safeguard federal resources and funds. Frameworks can help identify the required combination of business process re-engineering, technology and data analytics needed to reduce improper payments and WFA .
Management and storage of data are critical to preventing, detecting and recovering improper payments and WFA . Data collection, quality, origin, derivation and trustworthiness are important aspects of data management; without them, data analytics and intelligence are difficult at best. Data management and storage applications include business intelligence and decision-making; data availability; characteristics of the available data (e.g. timeliness, source, quality, reliability, etc.); privacy and security aspects of the data (e.g. PII); availability of the data for intended uses; and data security and governance.
Data analytics is a key technology component to fighting WFA. Analytic technologies can automatically detect data aberrations, outliers, spikes and anomalies and flag them for further investigation. Additionally, data visualization, geospatial analytics and combining disparate data sets allow analysts to review data from different perspectives, as well as identify correlations, spot abnormalities and gain valuable insights. Predictive modeling allows agencies to assess what is happening or is likely to happen in the near-term.
Emerging technologies such as Artificial Intelligence (AI), machine learning, natural language processing, rule-based algorithms and predictive analytics have the power to help reduce improper payments and WFA significantly. Automated machine learning systems can spot patterns, anomalies and duplications in financial data, and provide critical insights. AI can automate certain processes and data analysis, resulting in more efficiencies and cost savings.
Contractors can expect agencies to continue to focus on reducing monetary loss and improper payments. Deltek expects investments in IT and professional services to help them do so, will remain strong. For more information on this subject, see Deltek’s recently published report, Technology Strategies for Federal Waste, Fraud and Abuse, 2019.