Read the Record, Not the Claim
Fraud rarely presents as a lie. It presents as a claim that is internally consistent and externally contradicted, and almost every counter-fraud control reads only the claim. The contradiction is usually already on the public record.
How financial crime, corporate security, and fraud investigation teams use open-source intelligence to find the contradictions a claim cannot hide.
Series description
This analyst-led series shows how open-source intelligence finds fraud in the distance between what a subject declares and what their digital footprint shows. It is built for financial intelligence, corporate security, and civilian agency teams investigating fraud, waste, and abuse.
What does this series cover?
- How unexplained wealth surfaces in public data as spending that outruns declared income
- Building a confirmed digital footprint from minimal selectors across more than 300 sources
- Risk detectors that cut digital noise, including AI concept detection in images and video
- Network analysis that links separate investigations and surfaces associates no case file holds
- Where open-source intelligence sits in the sequence, before costly capabilities are committed
Register for the full analysis series
Register to access analyst-led case studies, repeatable workflows, and network analysis techniques for finding fraud in publicly available data.
Featured insight
The Association of Certified Fraud Examiners found that 84 percent of fraud perpetrators displayed at least one behavioral red flag before detection, with living beyond their means among the most common. A lifestyle that outruns a declared income is a public fact, which makes the most common warning sign in fraud something open-source intelligence can look for deliberately.
Why does it matter?
The National Audit Office puts fraud and error against UK public funds at £55 billion to £81 billion in 2023-24, of which £12 billion was detected. The Government Accountability Office estimates the US federal government loses $233 billion to $521 billion a year. Neither figure describes sophistication, and neither reflects on the teams doing the work: both describe claims that passed because nothing independent was held up against them.
How this stays proportionate
Intelligence should narrow scrutiny, not widen it. Fivecast is engineered for targeted, defensible investigation: source tagging on every finding, a fully automated audit trail, and AI that supports analyst judgment rather than replacing it. A signal is evidence for an analyst to assess. It is never an automated decision about a person, and it is never a conclusion on its own.
What will you access?
- Analyst-led case studies on finding fraud through footprint inconsistency
- Repeatable OSINT workflows for fraud detection and investigation
- Network analysis techniques that surface undisclosed associations and links between cases
- Guidance on identifying unexplained wealth in publicly available data
- Practical approaches to auditable, defensible open-source collection
Who is it for?
Built for financial intelligence and anti-financial crime teams, corporate security and insider risk functions, and civilian agencies investigating fraud, waste, and abuse. Most useful for investigators and analysts who need to test a declaration against an independent record rather than reassess the paperwork.
Frequently asked questions
What is fraud intelligence?
Fraud intelligence is the practice of testing what a subject declares against independent, publicly available records, so contradictions surface as indicators an investigator can assess.
How does open-source intelligence detect fraud?
It surfaces publicly available signals, including spending and activity inconsistent with a declared position, then links them across sources into a picture a single file would not show.
What is unexplained wealth?
Unexplained wealth is the accumulation of assets or spending habits that are not proportionate with a person’s typical income, visible in public sources through travel, luxury purchases, gambling, or large transactions.
Which teams use fraud intelligence?
Fraud investigation teams in government, financial services, and corporate security use it to detect and investigate fraud.
How does Fivecast keep findings defensible?
Fivecast applies source tagging and a fully automated audit trail, so findings stay accurate, consistent, and defensible. Findings support analyst judgment. They do not automate decisions about individuals.
