Financial crime detection – technology and collaboration in modern compliance
Financial crime detection is no longer about ticking compliance boxes. The challenge has shifted from reporting suspicious transactions to proactively identifying risk before it becomes loss.
Across the financial sector, institutions are under pressure from regulators, shareholders, and customers to strengthen how they detect money laundering, sanctions evasion, insider fraud, and cyber-enabled financial crimes. The solution now lies in technology, collaboration, and data-driven intelligence — not manual oversight.
Defining financial crime detection
Financial crime detection refers to the technologies, processes, and controls that uncover illegal financial activity within regulated entities. This includes:
- Money laundering and terrorist financing
- Fraud and embezzlement
- Sanctions evasion
- Insider trading
- Corruption and bribery-linked transactions
Detection frameworks rely on integrated data monitoring across payments, securities, lending, and digital asset systems to identify abnormal or high-risk behavior.
The growing complexity of financial crime
The Financial Action Task Force (FATF) and the European Banking Authority (EBA) have both warned that new payment technologies and cross-border data fragmentation make detection harder.
Criminal organizations now use layered structures across multiple jurisdictions, leveraging digital wallets, shell companies, and even legitimate business fronts to move funds.
According to the Europol Financial and Economic Crime Threat Assessment 2024, over 70% of large-scale financial crimes involve at least one cross-border data exchange failure — meaning regulators and banks lack visibility into the full transaction chain.
The conclusion is clear: no single institution can detect financial crime in isolation anymore.
Criminal organizations now use layered structures across multiple jurisdictions, leveraging digital wallets, shell companies, and even legitimate business fronts to move funds.
According to the Europol Financial and Economic Crime Threat Assessment 2024, over 70% of large-scale financial crimes involve at least one cross-border data exchange failure — meaning regulators and banks lack visibility into the full transaction chain.
The conclusion is clear: no single institution can detect financial crime in isolation anymore.
Technology’s expanding role in detection
Financial crime detection has moved beyond static rule-based monitoring. Modern systems now combine advanced analytics, artificial intelligence, and privacy-preserving computation.
Key technologies include:
Key technologies include:
- Machine learning (ML): continuously adapts to evolving criminal behavior by identifying subtle transaction anomalies.
- Natural language processing (NLP): scans text-based data such as payment references, invoices, or internal communications for red flags.
- Network analysis: maps connections between entities to reveal hidden relationships in complex ownership structures.
- Federated analytics: allows multiple institutions to collaborate on shared risk models without centralizing data.
The goal is not just to flag suspicious transactions, but to understand behavioral intent and interconnected risks.
The regulatory environment
Under the EU’s Sixth Anti-Money Laundering Directive (AMLD6), financial institutions must strengthen their detection frameworks and report suspicious activities to national Financial Intelligence Units (FIUs).
The Financial Crimes Enforcement Network (FinCEN) in the United States, and the UK Financial Conduct Authority (FCA), have both emphasized the need for risk-based and data-driven detection — moving away from periodic reviews to continuous monitoring.
In Europe, the creation of the European Anti-Money Laundering Authority (AMLA) aims to harmonize data access and coordination between national regulators, further highlighting the role of digital infrastructure in financial crime detection.
The Financial Crimes Enforcement Network (FinCEN) in the United States, and the UK Financial Conduct Authority (FCA), have both emphasized the need for risk-based and data-driven detection — moving away from periodic reviews to continuous monitoring.
In Europe, the creation of the European Anti-Money Laundering Authority (AMLA) aims to harmonize data access and coordination between national regulators, further highlighting the role of digital infrastructure in financial crime detection.
Challenges in detection
Even with advanced tools, several structural problems persist:- Data silos: fragmented data across subsidiaries, jurisdictions, and third parties.
- False positives: over-alerting due to rigid rules-based systems.
- Lack of standardization: inconsistent definitions of suspicious activity across regulators.
- Privacy constraints: institutions must share intelligence without violating GDPR or banking secrecy laws.
According to Deloitte’s 2024 AML Benchmark Report, over 40% of institutions still depend on outdated rule sets, creating compliance inefficiencies and missed detection opportunities.
“The strongest financial crime detection programs are not the ones that collect the most data — they’re the ones that connect it meaningfully and securely. Privacy-preserving intelligence sharing is the next competitive edge.”
- William Morris, Lead Enterprise Account Executive - UK
This shift reflects a growing understanding that compliance and privacy must evolve together, not in opposition.
The convergence of AML, CTF, and fraud detection
Modern detection frameworks merge Anti-Money Laundering (AML), Counter-Terrorist Financing (CTF), and fraud detection into a single analytical layer. Criminal typologies rarely fit one category; for example, trade-based laundering may involve both fraud and terrorist financing elements.
Integrated systems allow compliance teams to detect multi-vector risk using unified data pipelines and shared analytical models — an approach now recommended by both FATF and the EBA Guidelines on Financial Crime Risk.
Integrated systems allow compliance teams to detect multi-vector risk using unified data pipelines and shared analytical models — an approach now recommended by both FATF and the EBA Guidelines on Financial Crime Risk.
Predictive analytics and privacy
Predictive analytics is the next stage in financial crime detection. By combining machine learning with network intelligence, institutions can anticipate potential criminal activity before it happens.
However, predictive accuracy depends on access to shared intelligence — and that introduces privacy and regulatory risk. Sharing transaction or identity data across organizations remains restricted by law.
This is where privacy-preserving computation, such as Multi-Party Computation (MPC), changes the landscape. It enables multiple data holders to collaborate on pattern detection without ever exposing or transferring the underlying information.
However, predictive accuracy depends on access to shared intelligence — and that introduces privacy and regulatory risk. Sharing transaction or identity data across organizations remains restricted by law.
This is where privacy-preserving computation, such as Multi-Party Computation (MPC), changes the landscape. It enables multiple data holders to collaborate on pattern detection without ever exposing or transferring the underlying information.
Partisia’s perspective
Effective financial crime detection relies on collaboration — but collaboration cannot come at the cost of privacy. Partisia’s privacy-preserving data collaboration technology uses Multi-Party Computation (MPC) to make this possible.
With MPC, banks, regulators, and investigative bodies can securely compare transaction data, identify shared risk indicators, and run fraud-detection models across borders — all without revealing sensitive customer information.
This model aligns directly with the goals of AMLD6, FATF, and DORA by enabling financial intelligence sharing that is secure, transparent, and compliant.
Partisia helps institutions move from reactive detection to predictive intelligence — detecting risk early, maintaining privacy, and proving compliance under the world’s most demanding regulatory frameworks.
With MPC, banks, regulators, and investigative bodies can securely compare transaction data, identify shared risk indicators, and run fraud-detection models across borders — all without revealing sensitive customer information.
This model aligns directly with the goals of AMLD6, FATF, and DORA by enabling financial intelligence sharing that is secure, transparent, and compliant.
Partisia helps institutions move from reactive detection to predictive intelligence — detecting risk early, maintaining privacy, and proving compliance under the world’s most demanding regulatory frameworks.
