Waste, Fraud & Abuse: Technical Realities

Data silos in government systems represent a significant operational challenge. Vast amounts of critical information remain locked within isolated, incompatible, or outdated legacy platforms. This fragmentation prevents departments from accurately accounting for and sharing data, causing agencies to operate as separate entities rather than as a unified organization amid the inherent complexities of government administration and public safety.

President Donald J. Trump’s Executive Order 14243, signed March 20, 2025, titled “Stopping Waste, Fraud, and Abuse by Eliminating Information Silos,” directly addresses these barriers. The order requires agency heads to ensure that designated federal officials have full and prompt access to all unclassified agency records, data, software systems, and IT systems to identify and eliminate waste, fraud, and abuse. It mandates intra- and inter-agency sharing and consolidation of unclassified records, rescission of blocking guidance, and immediate access to data from federally funded state programs.

The scale of these technical failures is documented and substantial. In fiscal year 2025, federal agencies reported an estimated $186 billion in improper payments across 64 programs — an increase of $24 billion from the prior year. The Government Accountability Office estimates that annual federal fraud losses range from $233 billion to $521 billion, with cumulative improper payments since fiscal year 2003 exceeding $2.8 trillion. 

In public safety and corrections, the Bureau of Prisons remains on the GAO’s High-Risk List due to chronic understaffing that threatens inmate and staff safety. Its staffing assessment methods — including full-time equivalent (FTE) forecasting — rely on inconsistent, unvalidated data that create blind spots, conceal inefficiency, and drive hundreds of millions of dollars in excess overtime each year.

A core contributor to these persistent challenges across public safety and corrections systems nationwide—at the federal, state, and local levels—is the widespread reliance on incremental budgeting practices combined with oversimplified cost pooling and aggregation. Rather than using detailed, driver-based resource allocation tied to actual workload demands and facility-specific factors (such as inmate population acuity, security levels, shift coverage needs, or program requirements), these systems often start with prior-year baselines or broad historical data clusters. These are then adjusted with flat percentages, simplistic ratios (e.g., inmate-to-staff averages), or unvalidated guidelines that lump dissimilar activities and needs into oversized, uniform “buckets.” 

This method stands in direct opposition to more robust alternatives: real-time FTE optimization powered by multivariable calculus, machine learning, backpropagation, and mandatory human-in-the-loop oversight. These advanced methods deliver auditable, transparent analytics, generate jurisdictionally compliant staffing plans linked directly to validated workload drivers, and enable full data orchestration across isolated platforms—eliminating guesswork, black-box calculations, and excess overtime.

These outcomes are predictable technical results of governance systems lacking transparent data orchestration. Information silos block cross-agency sharing, permitting undetected overpayments and duplication. Opaque staffing algorithms function as black boxes without human validation, masking chronic understaffing as efficiency. Unvalidated data inputs produce garbage-in/garbage-out calculations that leave safety-critical posts vacant. Without real-time, auditable analytics, violence spikes and systemic risks remain obscured.

Peer-reviewed studies and government audits confirm the causal chain and its societal consequences. Understaffing drives inmate-on-inmate and inmate-on-staff assaults, increased use-of-force incidents, extended lockdowns, canceled rehabilitation programs, officer fatigue, elevated PTSD rates, higher recidivism, community crime, taxpayer burdens, and eroded institutional trust. These dynamics are not inevitable; they are the technical outputs of systems that obscure root causes and shift societal equilibria toward preventable harm.

The Constitutional Public Safety Staff Management System (CPSSMS), invented by William D. Colón (U.S. Patent Application Publication No. US20260050847A1, patent pending), is an evidence-based platform that advances the mission of good government by promoting fiscal prudence, operational transparency, data-driven accountability, and effective public safety that protects citizens and taxpayers while restoring public trust across all levels of government. Powered by multivariable calculus, machine learning, and backpropagation with mandatory human-in-the-loop oversight, CPSSMS delivers auditable, real-time FTE optimization for government entities at the national, state, and local levels while ensuring full constitutional compliance and jurisdictional alignment.

We resolve these issues through proven, transparent, and jurisdictionally compliant methodologies that form our Standards of Practice. These include evidence-based FTE measurement to eliminate guesswork and waste; human-in-the-loop AI for auditable and constitutionally sound outputs; full data orchestration with real-time transparency that supports Executive Order 14243; and staffing plans that reduce overtime, violence risk, and recidivism drivers. The result is safer facilities, lower taxpayer costs, restored public trust, and measurable improvements in public safety outcomes.

Additional information on these Standards of Practice, including contact details and full details regarding the patented Constitutional Public Safety Staff Management System, is available on the main page.

Analytical Solutions Consulting Inc. — Turning governance risks into measurable public safety and efficiency.