Embracing OSINT: Lessons AML Can Learn from National Security Intelligence

9^th Sep 2026 By Dr Brent R. Crider - The Apollo Group & Chad Longo of Fivecast

For much of our careers, we have worked at the intersection of intelligence, security, technology, and financial crime. Those fields operate in different environments, but they increasingly face the same core challenge: how to find the signal that matters inside an overwhelming volume of information, and how to turn that signal into a decision before the opportunity to act has passed.

How do you identify a meaningful threat hidden within an overwhelming volume of information without mistaking collection for intelligence?

National security organizations learned long ago that collecting more information does not automatically produce better intelligence. Information only becomes valuable when it is evaluated, connected, contextualized, and delivered to a decision-maker in time to act.

Financial institutions should apply the same principle to their anti-money laundering (AML) programs. From a financial crime compliance perspective, the goal is not simply to gather more data, but to turn the right internal and external signals into timely, defensible risk judgments.

The lesson for AML is not that financial institutions should copy the intelligence community wholesale. It is that they should learn from where intelligence organizations have struggled with open sources: collecting too broadly, treating availability as reliability, allowing disconnected data streams to overwhelm judgment, and failing to convert information into timely decisions. AML programs can avoid those same mistakes by putting purpose, validation, governance, and analyst judgment around open-source collection from the start.

From Data Collection to Intelligence Production

Most banks are not short on data. They sit on years of customer records, transaction-monitoring alerts, sanctions-screening results, case files, regulatory filings, risk scores, and historical activity.

The challenge is that this information does not always help investigators understand the person or organization behind the activity. A customer may look unusual in a monitoring system, but the more important question is whether the activity makes sense in light of who that customer is, who they are connected to, and what may be happening outside the institution.

Traditional AML systems are built to recognize unusual financial activity. They can surface rapid movement of funds, high-risk geographic exposure, structuring patterns, or behavior that does not fit an expected customer profile. Those indicators are important, but they rarely provide a complete intelligence picture. In practice, this is where AML teams can lose sight of who is behind the activity, who they are connected to, and whether the apparent transaction pattern reflects a deeper risk story.

In national security, an analyst would not assess a threat from one stream of intelligence alone. Transaction data plays a similar role in AML: it is valuable, but incomplete. It can show what moved through the institution, yet often leaves unanswered the human, commercial, and network context that explains why the activity matters.

That is where open-source intelligence becomes essential.

The Intelligence Outside the Bank

Individuals and organizations increasingly leave extensive digital footprints across social media, news sources, corporate records, websites, online marketplaces, discussion forums, data leaks, and other publicly or commercially available sources. For financial crime teams, these sources can provide the context that traditional bank data was never designed to capture.

A customer’s digital footprint can help investigators understand the relationships and behaviors that make financial crime risk more visible. Public and commercially available information may reveal whether a customer’s business activity aligns with what they disclosed during onboarding, whether online behavior points to unexplained wealth or suspicious commercial activity, or whether the customer is connected to actors, networks, or schemes that would not appear in bank records alone. Traditional adverse-media screening may identify a published news article after a subject has already been publicly linked to wrongdoing, but social media and broader open-source intelligence can provide earlier, richer indicators. Used carefully, these sources help AML teams move beyond isolated transactions and develop a clearer picture of the risk story behind the customer.

For AML teams, technology can make that wider intelligence picture more manageable. Fivecast’s financial-crime capabilities help investigators search across fragmented online sources and use artificial intelligence to organize large volumes of text, imagery, and video. The value is not broader collection for its own sake; it is helping investigators uncover hidden relationships, assess behavioral patterns, and bring relevant external context into risk-based decisions.

This does not mean that every social-media post is reliable or that every online association proves misconduct. Intelligence professionals understand that information must be validated, corroborated, and assessed for credibility.

The answer to imperfect information is not to ignore it, but to apply disciplined tradecraft. Analysts need to understand where information came from, what it can reasonably support, what it does not prove, and how much weight it should carry in an AML decision. To make that discipline repeatable, financial-crime teams need a structured process for turning open-source information into intelligence.

Apply the Intelligence Cycle to AML

This is where the national security comparison becomes most useful for AML. Intelligence work is not defined by the amount of information collected, but by whether that information is directed toward a specific requirement, tested for credibility, analyzed in context, and translated into a judgment someone can act on. AML programs need the same discipline if open-source information is going to strengthen investigations rather than simply add more noise.

For AML teams, the intelligence cycle offers a practical operating model for turning open-source information into defensible risk insight. It begins by defining the question that needs to be answered, then collecting information tied to that requirement, analyzing the results in context, and communicating a judgment that decision-makers can act on.

Direction

Every investigation should begin with a clearly defined intelligence requirement.

Instead of asking an analyst to “research the customer,” the institution should define what it needs to know and why. That may include whether the customer’s apparent source of wealth is credible, whether the customer controls undisclosed companies, whether there is evidence of fraud, corruption, sanctions evasion, or other predicate activity, who the customer’s key associates are, and whether the customer’s online behavior supports the story presented during onboarding. The point is to move from open-ended searching to purposeful intelligence collection.

Clear questions produce more focused and defensible investigations. They also help AML leaders demonstrate that external-source research is governed, risk-based, and tied to a legitimate investigative purpose.

Collection

Collection should not be isolated to legacy sources or internal data. It should include a robust array of relevant content from diverse sources to provide a holistic risk profile.

The internal picture begins with what the institution already knows: the customer relationship, account behavior, prior alerts, counterparties, device information, and earlier investigative history.

External collection should then fill the gaps that internal records cannot answer. That may mean looking to online business activity, corporate records, litigation history, public filings, news coverage, social media, sanctions-related information, data leaks, or relevant deep- and dark-web sources when they are tied to a defined investigative purpose.

This is the environment in which OSINT becomes indispensable. In Fivecast’s view, OSINT is not simply a broader search across public information; it is the disciplined collection and analysis of publicly and commercially available sources that can reveal context traditional AML data often misses. That means extending beyond conventional adverse media to include online corporate records, watchlists, social media, news, commercial datasets, leaked data, and deep- and dark-web sources that may expose hidden relationships, inconsistent business activity, emerging risk behaviors, or links to wider criminal networks.

Processing and Analysis

Raw information must then be organized, tested, and interpreted before it becomes useful intelligence.

The investigator’s role is to turn that raw material into a coherent intelligence picture. Timelines, relationships, contradictions, behavioral indicators, geography, and possible predicate-crime connections all matter because they help explain the risk story behind the activity.

The central question is not simply whether negative information exists. It is what the available information means when considered alongside customer and transactional activity. For a financial crime compliance team, that interpretation is often the difference between documenting a finding and making a defensible risk decision or missing a risk entirely.

Dissemination

The final product should communicate and support a judgment, not merely provide a collection of search results.

Decision-makers need more than a summary of search results. They need a clear view of what was discovered, why it matters, how confident the analyst is, what remains uncertain, and what proportionate action the institution should consider.

That judgment may lead to enhanced monitoring, a revised customer risk rating, additional due diligence, account restrictions, escalation, exit, fraud intervention, regulatory reporting, or a referral to law enforcement. The right action depends on the strength of the intelligence and the institution’s responsibility to respond.

Intelligence Must Be Timely

National security intelligence loses value when it arrives after the decision has already been made. The same is true in financial crime.

A point-in-time review completed during onboarding may no longer reflect a customer’s risk six months later. Ownership can change, new relationships can emerge, business activity can shift, litigation can surface, and online behavior can begin to signal a risk that was not visible when the relationship began.

Fivecast helps institutions move from static, point-in-time reviews to continuous intelligence collection across relevant online sources, giving AML teams a better chance to spot material changes in risk before they become missed red flags, regulatory exposure, financial loss, or criminal activity the institution failed to identify in time.

This transition—from periodic review to ongoing intelligence—is one of the most important opportunities for AML modernization. The point is not surveillance for its own sake; it is helping institutions identify material changes in risk early enough to respond responsibly.

Analysts Remain Central

Technology should not replace the investigator.

Artificial intelligence can accelerate discovery and help analysts make sense of information that would be difficult to review manually. It can surface connections, organize complex material, translate content, and point investigators toward risk indicators that deserve human review.

But AI cannot assume institutional accountability. It cannot replace professional judgment, evaluate every nuance, or determine an institution’s risk appetite.

The strongest model is analyst-centered intelligence: technology expands what investigators can see, while trained professionals determine what the information means. That is the perspective Fivecast brings to financial crime compliance: AI should accelerate discovery and connection-building, while human expertise remains responsible for judgment, proportionality, and action.

From Compliance Function to Intelligence Capability

The future AML program will not be defined by the number of alerts it produces or the volume of investigations it completes.

It will be defined by whether it can identify meaningful risk, connect that risk to people and networks, explain its conclusions, and support action while the institution still has time to intervene.

National security organizations have spent decades developing processes for turning fragmented information into decision advantage. Financial institutions can apply many of the same lessons.

That begins with better questions and disciplined collection. AML teams should use multiple sources, validate what they find, look for relationships and intent, communicate clear judgments, and recognize that some of the most important information about a customer may exist outside the bank’s systems.

Financial crime is an intelligence problem. Institutions that treat it that way will be better positioned to identify threats before those threats become regulatory findings, financial losses, or reputational crises. For Fivecast FinCrime readers, the practical takeaway is straightforward: better AML outcomes will not come from collecting more information for its own sake, but from connecting the information that explains risk, testing it with discipline, and turning it into action while there is still time to intervene.

ABOUT THE APOLLO GROUP

Apollo Group offers a wide range of services including, but not limited to: procurement support, contract management, grant management, policy development, training and more. Our services are tailored to meet the unique needs of each client, and we pride ourselves on delivering high-quality results.

About Fivecast

Fivecast delivers intelligence solutions built for clarity, powered by AI and trusted to surface what matters. Engineered to solve complex intelligence challenges our platform cuts through digital noise to help those protecting nations, borders, businesses and communities uncover critical insights – before risk becomes reality.

Trusted by agencies and enterprises across national security, law enforcement, defense, corporate security and financial crime, Fivecast was born from collaboration between governments and research institutions. Headquartered in Australia with a global footprint, we support the world’s most critical missions.

Fivecast. Engineered for Intelligence.