Public sector agencies are at a pivotal crossroads. Governments are embracing artificial intelligence (AI) not only to modernize core operations and improve citizen services. At the same time, the rise of AI is also reshaping the threat landscape. Criminals now deploy synthetic identities, deepfake-enhanced documentation and hyper-personalized social engineering campaigns are forcing agencies to rethink legacy risk controls that were never designed for this scale or sophistication. For example:
AI holds enormous promise, but only when grounded in trusted data and strong governance. Modernization isn’t about a single model; it’s about building a secure, end-to-end system that connects data, intelligence and workflows. This blog illustrates how to modernize fraud prevention with Databricks through a fictional agency called the Services Bureau.
Before exploring how this modernization works, it helps to understand how fraud investigations often happen today at the Services Bureau. Analysts must jump between multiple systems to gather the data needed for a single case. They export files from one system, download spreadsheets from another and receive additional information through email attachments or shared folders. They then combine those sources manually, running macros or rules to flag suspicious rows and performing deeper searches in other systems to validate the findings. The process is time consuming, fragmented and difficult to scale.
Now imagine a modern workflow where a single application visualizes 17 prioritized cases, each with supporting evidence and clear explanations tied to policies or fraud signals. AI surfaces the most urgent risks, while the analyst makes the final call. What once took weeks can now be done in a day, allowing them to move faster and with greater confidence.
Data and insights deliver the most value when embedded directly into daily workflows.
Using Databricks Apps powered by Lakebase, the Services Bureau brings governance, agents and dashboards into a single fraud operations application tailored to its mission.
A senior fraud analyst logs into the application and sees assigned cases. When opening a case, the analyst can review supporting documents stored in Unity Catalog volumes and third-party verification data.
Meanwhile, an embedded agent evaluates the case in the background and provides recommendations with supporting rationale.
If the analyst agrees, they can approve the case. If not, they can override the recommendation and escalate it for investigation. Human judgment remains central.
Executives use the same application to view dashboards and interact with Genie without logging into multiple tools. Leadership and analysts operate within a unified environment that connects governance, intelligence and action.
This is what operationalized AI looks like in practice. Insights are not isolated in analytics platforms. They are embedded into mission workflows where decisions are made.
Teams can process far more cases with the same workforce, all while reducing the likelihood that suspicious activity slips through the cracks. Investigators gain visibility into patterns across programs and leadership gains confidence that every flagged activity is being evaluated systematically and consistently.
The fictional Services Bureau processes grants, contracts, benefits, tax returns and patents, which requires strong governance. Thousands of applications stream in daily through external systems and land in Delta tables within the lakehouse. Machine learning models and business rules flag suspicious cases for fraud analysts across the country.
Within Unity Catalog, the agency manages its fraud investigation tables with attribute-based access control (ABAC). Sensitive columns such as Personally Identifiable Information (PII) are governed by tags that automatically enforce masking policies for specific user groups.
For example, junior fraud analysts can view case details needed for review but never see masked PII fields. Senior analysts and approved investigators can access additional context based on role and policy.
Governance extends beyond access controls. Full lineage is available at the table and column level. Analysts and compliance teams can see exactly where a data element originated and where it flows downstream. If a regulator asks where a field came from, the answer is available in seconds.
Once data is governed and accessible, the next challenge is prioritization. Executives need to understand risk trends. Fraud leaders must align operational decisions with policy guidance and emerging external threats.
The Services Bureau uses Agent Bricks, a multi-agent supervisor, to coordinate three capabilities:
Within the Databricks Platform, Agent Bricks is configured by defining its role and specifying which agents it can orchestrate. From there, executives can ask natural language questions such as: “As of December 1st, what should we prioritize next? Where are our top risk areas and how are we performing?”
Behind the scenes, Agent Bricks calls Genie to run SQL queries against live fraud tables. It invokes the knowledge agent to surface relevant policy citations with direct references to source documents, then retrieves external signals about emerging fraud schemes.
The supervisor synthesizes these inputs into a clear response with recommended actions and supporting reasoning.
This is not a generic LLM response. It is AI grounded in enterprise data, aligned to policy and enriched with real-time context. The agent recommends where the Fraud Investigation Unit should spend its time in the next 24–48 hours, armed with the context that they are currently in a “critical” backlog situation of nearly 53,000 cases.
For executives, this means actionable guidance delivered in plain language. And for operational teams, it means faster alignment around risk.
Feedback loops are built-in. Through labeling sessions, users can rate responses and provide guidance to refine outputs over time.
This approach brings AI into production as a coordinated system rather than a standalone model.
Equally important is AI governance. Every recommendation produced by the agent is grounded in traceable data sources, policy references and documented reasoning. Analysts remain in the loop and can review the supporting evidence before accepting or overriding the recommendation. This transparency helps agencies maintain trust in AI-assisted decisions while ensuring compliance with regulatory and oversight requirements.
Operational leaders also need visibility into workload distribution and performance metrics.
Within an executive dashboard built on AI/BI Genie, the Services Bureau tracks key performance indicators across its fraud program. The interface is interactive. Selecting an individual examiner automatically updates related charts to reveal workload, overdue cases and case mix.
Suppose leadership notices that senior examiners are carrying a disproportionate share of overdue cases. To investigate further, they can ask Genie directly: “What is the breakdown of cases by examiner level?”
Genie generates the SQL query against the gold fraud table, returns a structured table and produces a visualization automatically. The SQL remains visible for transparency and validation.
With this insight, leadership can rebalance workloads or accelerate training for junior examiners. Analysts and executives alike can move from question to evidence without waiting on technical teams.
AI/BI Genie transforms analytics from static reporting into conversational, transparent and actionable intelligence.
Modern public sector agencies cannot afford fragmented systems where data governance lives in one tool, analytics in another and operational workflows somewhere else entirely.
By unifying data, AI and governance within the Databricks platform, agencies can build secure foundations, coordinate intelligent agents and embed insights directly into mission-critical applications.
With models being built on trusted, context-aware data:
To learn how your agency can modernize fraud prevention and other mission critical programs, connect with our public sector team.
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。
