This application area focuses on detecting, preventing, and managing fraud, waste, abuse, and corruption across government and quasi‑public programs, payments, and digital services. It encompasses benefits and claims fraud, procurement and supplier fraud, identity theft and account takeover, and broader financial crime affecting public funds. The core capability is to continuously monitor transactions, entities, and user behavior to flag anomalous patterns and prioritize high‑risk cases for investigation. It matters because traditional government fraud controls are largely manual, slow, and sample‑based, often catching issues only after funds are disbursed and hard to recover. By applying advanced analytics to large, heterogeneous datasets, organizations can shift from “pay and chase” to proactive prevention, reduce financial leakage, protect program integrity, and maintain public trust. At the same time, it helps governments respond to new threats such as AI‑enabled forgeries and at‑scale fraud campaigns by upgrading verification, oversight, and monitoring capabilities.

This AI solution uses AI to predict crime hotspots, detect benefits and grant fraud, and surface emerging risks across public-sector programs. By combining geospatial analytics, bias-aware predictive policing, and advanced anomaly detection on financial and case data, it helps agencies target interventions, allocate resources, and reduce losses while improving community safety and trust.

Urban Traffic and Safety Management focuses on using data-driven systems to monitor, optimize, and control vehicle and pedestrian movement across city streets and highways while reducing crashes and congestion. It integrates real-time feeds from signals, cameras, sensors, and historical crash and mobility data to continuously adjust traffic operations—such as signal timing, lane use, and routing—and to prioritize infrastructure investments and enforcement. This application matters because traditional traffic engineering relies on infrequent manual studies, static signal plans, and after-the-fact crash analysis, which cannot keep up with growing urban populations, constrained budgets, and safety mandates like Vision Zero. AI enables continuous, citywide visibility and faster detection of bottlenecks and high-risk patterns, helping public agencies improve travel times, reduce fatalities and serious injuries, cut emissions from idling traffic, and deploy limited staff and capital more efficiently.

AI Urban Congestion Intelligence uses real-time data from cameras, sensors, and connected infrastructure to detect, predict, and alleviate traffic congestion across city road networks. It dynamically optimizes signal timing, incident response, and routing to improve travel times, reduce emissions, and enhance road safety. This enables public agencies to maximize existing infrastructure capacity and deliver more reliable mobility without costly new construction.

Smart City Service Orchestration is the coordinated use of data and automation to plan, deliver, and continually improve urban public services across domains such as transportation, energy, public safety, and citizen support. Instead of siloed, paper-heavy, and reactive departments, cities use integrated data and decision systems to route requests, prioritize interventions, and tailor services to different resident groups, languages, and accessibility needs. This turns fragmented digital touchpoints and back-office workflows into a single, responsive service layer for the city. AI is applied to fuse sensor, administrative, and citizen interaction data, predict demand, recommend actions to officials, and personalize information and service flows for individuals. It powers policy simulations, dynamic resource allocation, and automated handling of routine cases, while keeping humans in the loop for oversight and sensitive decisions. The result is faster responses, more inclusive access, better use of scarce budgets and staff, and a more transparent, trustworthy relationship between residents and local government.

Police Technology Governance is the application area focused on systematically evaluating, regulating, and overseeing the use of surveillance, analytics, and digital tools in law enforcement. It combines legal, civil-rights, and policy analysis with data-driven insight into how policing technologies are acquired, deployed, and used in practice. The goal is to create clear, enforceable rules and oversight mechanisms that balance public safety objectives with privacy, equity, and constitutional protections. AI is applied to map and analyze patterns of technology adoption across agencies, surface risks (e.g., bias, over-surveillance, due-process issues), and generate evidence-based policy options. By mining procurement records, deployment data, usage logs, complaints, and case outcomes, these systems help policymakers, courts, and communities understand the real-world impacts of body-worn cameras, predictive tools, and other policing technologies. This supports the design of more precise regulations, accountability frameworks, and community oversight models. This application area matters because law enforcement agencies are rapidly adopting powerful technologies without consistent governance, exposing governments to legal liability, eroding public trust, and risking civil-rights violations. Structured governance supported by AI-driven analysis enables proactive risk management instead of reactive crisis response, and aligns technology deployments with democratic values and community expectations.

AI Public Safety Incident Response uses machine learning and real-time analytics to detect anomalies, flag potential crimes and fraud, and prioritize critical incidents across law enforcement and public agencies. It fuses data from 911 calls, sensors, case files, and external systems to guide faster, better-informed response and investigations. This improves community safety, reduces losses from crime and fraud, and helps agencies allocate limited resources more effectively and transparently.

This AI solution uses AI to support public‑sector investigations by detecting patterns of criminal activity, welfare fraud, and program abuse across diverse data sources. It prioritizes cases, flags high‑risk entities, and guides investigators with predictive insights, helping law enforcement and integrity bureaus focus resources where they are most likely to prevent crime, reduce fraud losses, and improve public safety.

Predictive policing is the use of data-driven models to forecast where and when crimes are likely to occur, and in some cases which individuals or groups are at higher risk of offending or victimization. By analyzing historical crime records, environmental factors, socioeconomic indicators, and real-time incident data, these systems generate risk scores, heatmaps, or priority lists that guide patrol routes, investigations, and preventive interventions. This application matters because police departments and public agencies operate under tight resource constraints while facing pressure to reduce crime, respond faster, and justify deployment decisions. Predictive policing promises more efficient use of officers and budgets, earlier intervention before crimes happen, and evidence-based planning for community programs. At the same time, it raises serious concerns about bias, transparency, legality, and public trust, driving parallel work on fairness assessment, bias detection, and governance frameworks for its responsible use.

This application area focuses on defining, structuring, and governing how public-sector organizations adopt and scale AI across services. It includes capability assessments, maturity models, strategic roadmaps, and quantified opportunity analyses that help governments move from isolated pilots to coordinated, citizen‑facing solutions. The emphasis is on aligning AI initiatives with policy goals, funding, data infrastructure, skills, and ethics requirements. It matters because many government agencies are stuck in experimentation, facing fragmented projects, unclear priorities, and high scrutiny around risk, fairness, and accountability. By using structured frameworks, data‑driven opportunity sizing, and governance models, public bodies can prioritize the highest‑value AI use cases, build the necessary capabilities, and put in place robust safeguards. This enables them to modernize public services, improve service quality and responsiveness, and do so in a way that is transparent, explainable, and compliant with public‑sector regulations and values.

Intelligent Policing Operations refers to the use of advanced analytics and automation to support core law enforcement workflows such as incident detection, patrol deployment, and criminal investigations. Instead of relying solely on manual CCTV monitoring, paper-heavy casework, and intuition-driven decisions, agencies use integrated data platforms and models to surface relevant evidence, spot patterns across siloed systems, and prioritize leads. The focus is on operational decision support, not replacing officers, with tooling that augments investigative work and field operations. This application area matters because policing is increasingly data-saturated while resources and budgets are constrained and public expectations for accountability are rising. By accelerating evidence triage, improving situational awareness, and enabling more data-driven deployment of officers, agencies can respond faster to incidents, close more cases, and reduce overtime, while maintaining robust audit trails for oversight. It also underpins workforce transformation—shifting officers’ time from administrative tasks to higher-value community and investigative work, and guiding reskilling and organizational change rather than ad‑hoc tech adoption.

Enterprise AI Governance is the coordinated design, deployment, and oversight of policies, processes, and tooling that ensure AI is used safely, consistently, and effectively across a government or large organization. It covers standards for model development and procurement, risk management (privacy, security, bias), lifecycle management, and accountability so that different agencies or departments don’t build and operate AI in isolated, incompatible ways. In the public sector, this application area matters because AI now underpins citizen-facing services, internal decision-making, and productivity tools. Without governance, agencies duplicate effort, expose citizens to inconsistent and potentially unfair outcomes, and increase regulatory, reputational, and cybersecurity risks. With robust AI governance, governments can scale the use of AI while maintaining trust, complying with law and ethics, and achieving better service quality and efficiency. AI is used both as an object and an enabler of governance: metadata and model registries track systems in use, automated risk assessments classify and flag higher-risk models, monitoring tools detect drift and anomalous behavior, and policy/workflow engines enforce guardrails (e.g., human-in-the-loop review, data access limits). These capabilities make it possible to operationalize AI principles at scale rather than relying on ad‑hoc, manual oversight in each agency.

Predictive Crime Hotspot Analysis focuses on forecasting where and when crimes are most likely to occur so public safety agencies can proactively deploy officers and resources. Using historical incident data, environmental and demographic factors, and real‑time signals, the models generate dynamic risk maps and prioritized patrol routes. This moves policing from a largely reactive model—responding after incidents occur—to a more preventive, data‑informed approach. This application matters because cities face rising demands on limited public safety budgets and personnel, alongside strong expectations for faster response times and safer communities. By highlighting emerging hotspots and patterns that humans might miss, these systems help agencies reduce response times, deter incidents through visible presence, and focus investigative resources where they will have the greatest impact. When implemented with clear governance and bias controls, it can improve community safety while making operations more efficient and accountable.

This application area focuses on how city and municipal governments design, implement, and operate the policies, processes, and structures that govern the use of AI across public services. Rather than building a single AI tool, it creates repeatable frameworks for project selection, risk assessment, procurement, ethics review, data management, and oversight of AI systems used in areas like transport, social services, permitting, and public safety. It often includes shared playbooks, national or regional coordination bodies, and standardized documentation and audit requirements. It matters because public-sector AI deployments carry heightened risks around rights, bias, transparency, and legal compliance, especially under regulations such as the EU AI Act. Cities typically lack in‑house expertise and risk fragmenting their efforts into ad‑hoc pilots heavily shaped by vendors. Municipal AI governance provides a structured way to experiment safely, build capacity, and align with regulation, while reducing duplication and dependency. It enables cities to modernize services with AI in a way that protects public trust and ensures accountability at scale.

Automated Video Threat Detection refers to systems that continuously analyze live or recorded video feeds from CCTV and other surveillance cameras to identify potential criminal, violent, or otherwise unsafe activities in real time. Instead of relying solely on human operators to watch thousands of camera streams, these systems automatically flag suspicious behaviors, objects, or situations—such as fights, weapons, intrusions into restricted areas, or abandoned bags—and generate alerts for security personnel. In the public sector, this application is used to enhance safety and security in public spaces, transportation hubs, government buildings, and critical infrastructure. By reducing the dependence on manual monitoring, it improves response times, expands effective coverage across large camera networks, and lowers the risk of missed incidents. AI models are trained on patterns of normal and abnormal behavior, enabling proactive intervention and more efficient use of limited security and law-enforcement resources.

This application area focuses on systematically building the skills, roles, processes, and governance structures that public‑sector organizations need to use AI safely and effectively. It encompasses assessing current capabilities, defining AI‑related job roles, designing training pathways, and establishing repeatable practices so that governments are not overly dependent on vendors or ad‑hoc pilots. The goal is to create a sustainable internal workforce and operating model that can plan, procure, deploy, and oversee AI solutions across agencies. This matters because many state governments face mounting pressure to adopt AI while lacking in‑house expertise and clear guidance. Without a coherent workforce and capacity strategy, they risk stalled initiatives, uneven adoption, ethical missteps, and poor return on investment. AI workforce enablement addresses these challenges by providing structured frameworks, standardized playbooks, and coordinated training that accelerate responsible AI uptake, reduce risk, and help governments derive consistent value from AI across their portfolios of programs and services.

Digital Government Service Automation focuses on streamlining public-sector services—such as permits, benefits, licenses, and citizen requests—by replacing paper-based and manual workflows with data-driven, automated processes. It covers end-to-end service journeys: intake of citizen requests, routing and case management, document handling, eligibility checks, and status notifications, all orchestrated across legacy systems and modern platforms. The goal is to improve service speed, accuracy, accessibility, and consistency while operating within strict regulatory, budgetary, and ethical constraints. AI is applied to classify and route requests, extract and validate data from forms, assist caseworkers with recommendations, and provide virtual assistants that offer 24/7 self-service to residents and businesses. Analytics and decision-support tools help leaders monitor performance, identify bottlenecks, and guide broader digital transformation. This application area matters because it directly impacts citizen experience, administrative burden, and trust in government, enabling agencies to do more with limited resources while maintaining strong governance and accountability.

Intelligent Traffic Management refers to systems that monitor, analyze, and control urban traffic flows in real time using integrated data from signals, sensors, cameras, and connected vehicles. Instead of operating traffic lights and road infrastructure on fixed schedules or manual interventions, these platforms continuously optimize signal timing, lane usage, incident response, and routing recommendations based on current and predicted conditions. This application matters because growing urbanization is driving chronic congestion, increased travel times, higher emissions, and more accidents, while building new roads is expensive, slow, and often politically difficult. By extracting more capacity and safety from existing infrastructure, intelligent traffic management helps governments reduce delays, improve road safety, and lower environmental impact. AI is used to forecast traffic patterns, detect incidents automatically, and dynamically adjust controls, enabling cities to achieve better mobility outcomes without massive capital projects.

This application area focuses on the design, assessment, and governance of algorithmic systems used in public services—particularly where decisions affect rights, benefits, and obligations (e.g., eligibility, risk scoring, and case management). It combines technical evaluation of models with structured involvement of affected stakeholders, caseworkers, regulators, and advocacy groups to ensure systems are transparent, explainable, and aligned with legal and ethical standards. It matters because automated decision tools in welfare, justice, and other public programs can amplify bias, erode due process, and damage public trust if deployed without robust oversight. By systematically auditing impacts, embedding participatory design, and implementing accountability mechanisms, this application helps governments deploy automation responsibly while preserving fairness, legality, and legitimacy in public-sector decision-making.

This application area focuses on automatically identifying potentially fraudulent or non-compliant tax returns and transactions submitted by individuals and businesses. Instead of relying solely on manual, random, or rules-based audits, models analyze large volumes of historical tax filings, payment records, and third‑party data to detect patterns indicative of underreporting, false claims, or other evasion tactics. It matters because tax fraud and evasion erode government revenue, strain public finances, and create unfairness between honest and dishonest taxpayers. By prioritizing high‑risk cases for review, these systems help tax authorities recover lost revenue, reduce the burden of unnecessary audits on compliant citizens, and allocate auditors’ time more effectively. In practice, AI is used to generate risk scores for each return, flag anomalous behavior, and continuously refine detection models as new fraud patterns emerge.

Digital Public Service Automation refers to the use of advanced analytics and automation to redesign how governments process cases, manage documents, and deliver services to citizens and businesses. Instead of handling applications, permits, benefits, and inquiries manually and case‑by‑case, administrations use intelligent systems to read and route documents, draft communications, support decisions, and personalize interactions at scale. This shifts public services from slow, paper‑heavy workflows to more responsive, data‑driven, and citizen‑centric operations. This application area also encompasses the governance layer required to operate these automated services responsibly: institutional frameworks, oversight mechanisms, and operating models that ensure transparency, fairness, and accountability. By embedding controls for bias, explainability, and human review into automated service workflows, governments can increase productivity and service quality without eroding trust. As a result, Digital Public Service Automation is becoming a core capability for modernizing the state, improving service access, and managing demand without proportional increases in headcount or cost.

This application area focuses on systems that help government leaders and civil servants make faster, more informed, and more transparent decisions on policy, budgeting, and service delivery. These solutions integrate data from multiple agencies, apply advanced analytics and simulations, and present evidence-based options, trade-offs, and impact forecasts in formats decision-makers can actually use. It matters because public-sector decisions are often made under time pressure, with fragmented information, and in politically sensitive contexts. By structuring complex problems, quantifying scenarios, and highlighting risks and distributional effects, decision support tools improve the quality, speed, and explainability of government choices—without replacing human judgment or accountability. AI techniques underpin forecasting, optimization, and scenario analysis, while interfaces and workflows are tailored to public-sector governance and oversight needs.

Public Service Delivery Copilots are digital assistants embedded into government workflows to help officials and frontline staff find information, draft content, and make consistent decisions faster. They sit on top of existing document repositories, case-management systems, and regulations, allowing staff to query complex policies in natural language, auto-generate responses and notices, and receive step-by-step guidance on processes such as permits, benefits, and citizen inquiries. This application matters because public agencies are burdened by legacy systems, high caseloads, and dense regulations that slow down service delivery and create inconsistency across departments and jurisdictions. By augmenting staff rather than replacing them, these copilots reduce delays, improve accuracy and transparency, and extend advanced digital capabilities to smaller municipalities that lack in-house technology teams. The result is more responsive, predictable, and equitable public service delivery for citizens and businesses. AI is used to interpret unstructured policy documents, understand citizen questions, reason over case data, and generate drafts of official communications and internal memos. Guardrails, role-based access, and workflow integrations ensure that human officials remain the ultimate decision-makers while benefiting from automated information retrieval, summarization, and suggested next actions.

Crime Linkage Analysis focuses on determining whether multiple criminal incidents are related through common offenders, groups, or patterns of behavior. Instead of viewing each incident in isolation, this application connects cases based on shared characteristics such as modus operandi, location, timing, and network relationships among suspects and victims. The goal is to surface linked crimes, reveal hidden structures like co‑offending networks or gangs, and prioritize investigations more effectively. AI enhances this area by learning similarity patterns between incidents and modeling social networks of offenders and victims. Techniques such as Siamese neural networks and social network analysis help automatically flag likely linked crimes, identify high‑risk groups, and expose influential actors within criminal networks. This enables law enforcement and public‑safety agencies to allocate investigative resources more efficiently, disrupt organized crime, and design targeted prevention and victim support strategies.

Law Enforcement Intelligence Analytics refers to the systematic collection, integration, and analysis of large volumes of criminal, operational, and open‑source data to support investigations and threat detection. It focuses on connecting fragmented data from phones, social media, criminal records, financial transactions, and cross‑border databases to identify suspects, criminal networks, and emerging threats more quickly and accurately than manual methods. This application area matters because traditional investigative workflows cannot keep pace with the scale, speed, and complexity of modern digital evidence and cross‑jurisdictional crime. By using advanced analytics to automate data triage, pattern recognition, and link analysis, agencies like Europol can accelerate investigations, improve cross‑border coordination, and surface hidden relationships that humans alone would likely miss, ultimately enhancing public safety and security outcomes.