Fraud now accounts for 45% of crime. What does this mean for financial crime investigations?
Fraud now accounts for as much as 45% of all crime in England and Wales, highlighting a significant shift in the way financial crime operates.
Much of today’s fraud ecosystem emerges in open online environments before financial transactions take place. As a result, financial crime investigations increasingly need to consider both financial data and open-source intelligence to identify networks, relationships and early indicators of risk.
What does the growth of fraud reveal about modern financial crime?
Recent figures show that fraud now accounts for almost half of recorded crime in England and Wales. More than 444,000 cases were reported in 2025, representing the highest annual total recorded by the National Fraud Database.
Many of these cases involved identity fraud, facility takeover and the use of breached personal information to access accounts without authorisation.
However, this trend points to a wider shift in the structure of financial crime. Fraud is no longer solely a problem of illicit financial transactions. Increasingly, it involves the identities, networks and digital infrastructure that enable those transactions.
Financial institutions generally detect fraud through transaction monitoring systems. Yet by the time suspicious financial activity appears, the criminal activity behind it may have been operating for weeks or months.
Therefore, understanding where fraud originates is becoming just as important as identifying the resulting transaction.
Where fraud emerges: the open digital environment
Many modern fraud schemes emerge in environments that investigators can identify long before funds begin to move.
Examples include:
- Fake investment websites and identity theft portals
- Social media advertisements promoting scams
- Money mule recruitment through online channels
- Messaging applications used to coordinate fraud operations
These environments can provide valuable early investigative signals. For example, open-source information may reveal:
- Online relationships between individuals involved in scams
- Digital infrastructure supporting fraudulent websites
- Behavioural signals that conflict with declared financial activity
- Networks coordinating the recruitment of money mules
This is particularly important when criminals use short-lived digital infrastructure. Churn-and-burn websites can appear, process large volumes of transactions and disappear before traditional monitoring processes have time to respond.
As a result, fraud signals can appear outside the financial system before they become visible within it.
Why are traditional financial crime controls under pressure?
Most financial crime frameworks focus on detecting suspicious transactions. Core controls often include:
- Sanctions screening
- Transaction monitoring
- Customer education and due diligence
- Adverse media checks
These controls remain essential. However, they are largely reactive because they identify suspicious activity once financial movement has already occurred.
At the same time, financial crime continues to evolve rapidly. Three structural pressures are changing what investigators face.
Speed
Instant payments allow criminals to move money across accounts, platforms and jurisdictions rapidly.
The wider laundering process is changing too. As explored in how criminals are digitising the laundering chain, physical and digital financial crime increasingly form part of the same connected journey.
Scale
Fraud networks can industrialise identity theft and account takeover by using large volumes of stolen data.
Furthermore, technologies such as AI are changing how easily criminals can scale their operations. The rise of fraud-as-a-service and agentic AI is making increasingly sophisticated fraud capabilities accessible to a wider group of offenders.
Complexity
Criminal organisations increasingly use online networks, layered identities and digital infrastructure to obscure illicit activity.
Consequently, investigators need a wider intelligence picture to understand how financial crime networks operate and identify opportunities to disrupt them.
The investigative gap
Many financial crime investigations rely heavily on internal financial data, including transaction trends, account activity and customer information.
Although these datasets are essential, they provide only a partial view of risk. They show what happens inside the financial system but often provide limited visibility into the wider networks generating that activity.
Open-source intelligence can help fill this gap. Publicly available information may reveal:
- Hidden relationships between individuals and companies
- Connections between online identities and financial activity
- Digital infrastructure enabling fraudulent schemes
- Networks coordinating scams or money mule recruitment
By analysing these signals together, investigators can move beyond individual alerts and start examining the wider ecosystems that enable financial crime.
However, this relies on teams being able to connect information effectively. Turning disjointed data into actionable fraud intelligence explores how joined-up data can help investigators uncover relationships and patterns that remain hidden across disconnected systems.
Intelligence-led financial crime investigations
Financial crime investigations are increasingly adopting an intelligence-led approach. Instead of relying on a single source, investigators can bring multiple types of information together to understand the wider context of a case.
Typical investigative inputs include:
- Transaction monitoring data
- Customer due diligence information
- Analysis of suspicious networks
- Analysis of suspicious behaviour
- Open-source intelligence
Rather than focusing solely on individual suspicious transactions, investigators can analyse the relationships and patterns connecting them across different environments.
This matters because financial crime rarely involves isolated individuals. Instead, criminal activity often relies on interconnected networks that exploit digital infrastructure, stolen identities and financial systems.
As fraud continues to learn and evolve, investigative approaches need to evolve with it.
The role of open-source intelligence in financial crime investigations
Open-source intelligence refers to the collection and analysis of publicly available information for investigative purposes.
Sources can include:
- Corporate registries
- Public records
- News media
- Domain and infrastructure data
- Social media platforms
When investigators use OSINT effectively, it can provide context that internal financial data alone cannot always reveal. For example, it may help uncover:
- Undisclosed business relationships
- Links between individuals and sanctioned entities
- Online activity connected to fraudulent investment schemes
- Networks involved in recruiting money mules
However, OSINT does not replace traditional financial crime controls. Instead, it adds another layer of intelligence that can support more informed investigative decisions.
Our guide to OSINT in financial crime investigations explores how teams can incorporate publicly available intelligence into modern investigative workflows.
Why does fraud change the investigation model?
Fraud networks operate across multiple environments. Digital services, identity infrastructure, financial systems and international networks can all form part of the same criminal operation.
Banks and financial institutions sit at a critical point within that ecosystem. They have visibility into transactions and financial behaviour, but they rarely see the entire network.
Therefore, investigations that rely solely on internal financial data risk identifying criminal activity only after the damage has occurred.
Combining OSINT and financial intelligence gives investigators additional context and can help organisations identify relationships, networks and emerging risks earlier.
Practical consequences for financial institutions
Financial institutions looking to strengthen financial crime investigations should consider several areas.
Expand intelligence sources
Investigations should incorporate both financial information and relevant publicly available intelligence. Together, these sources can help teams uncover hidden relationships and behavioural signals.
Improve investigative workflows
Investigators need structured processes for analysing multiple intelligence sources efficiently.
Connected workflows also reduce the risk of losing context as information moves between discovery, analysis and case management.
Strengthen analytical capability
Understanding networks and digital infrastructure requires specialist investigative skills. Therefore, organisations need to develop and maintain these capabilities as criminal strategies evolve.
Integrate intelligence tools
Modern investigative platforms can help teams bring together and analyse different sources of intelligence.
For example, OSINT Investigator helps investigators gather and analyse open-source intelligence, while the Financial Investigation Toolkit supports teams analysing complex financial data and identifying relationships within it.
Bringing these capabilities together can help investigators move from individual pieces of information towards a more complete intelligence picture.
Conclusion
Fraud accounting for 45% of crime in England and Wales signals a wider change in the structure of financial crime. Criminal networks now operate across digital environments, identity infrastructure and financial systems simultaneously.
Financial institutions remain a crucial line of defence. However, detecting and investigating modern fraud increasingly requires teams to understand the wider ecosystems that enable it.
Open-source intelligence can provide that additional context. When investigators analyse financial data alongside external intelligence, they can move beyond individual reactive signals and start uncovering the relationships, infrastructure and networks behind them.
Ultimately, as fraud continues to evolve, financial crime investigations need to become more connected, intelligence-led and capable of identifying criminal activity beyond the transaction itself.
