Beyond the Buzz: Can Generative AI & Synthetic Data Transform Financial Crime Detection?

The financial services sector is approaching a pivotal inflection point. While generative AI has dominated headlines and captivated public imagination, its real-world adoption in compliance and financial crime prevention remains limited. At the same time, synthetic data once confined to academic discourse is now gaining traction as a practical solution to one of the industry's most entrenched challenges: building effective AML and fraud detection systems amid strict data constraints.

Recent reports from Deutsche Bank  and the UK’s Financial Conduct Authority (FCA) highlight a shared conclusion: the convergence of generative AI and synthetic data can enable a paradigm shift from static, rule-based monitoring to dynamic, intelligence-led compliance. Together, they offer a transformative pathway for institutions seeking to modernise financial crime detection while maintaining robust data protection and operational integrity.

Although both technologies are still evolving, their intersection presents a compelling opportunity for banks, fintechs, and regulators alike to move beyond the limitations of legacy systems and into a new era of smarter, safer compliance.

Innovation Bottleneck: Data Access & Model Constraints

Despite the promise of AI, many AML frameworks remain outdated—heavily reliant on rules-based logic, structured inputs, and deterministic thresholds. The result? Excessive false positives, investigation fatigue, and limited adaptability to sophisticated, multi-channel criminal behaviour.

Even when institutions attempt to adopt machine learning or AI tools, progress often stalls due to a fundamental constraint: the lack of usable, shareable, and unbiased data. Regulatory concerns, data privacy legislation, and competitive sensitivities frequently inhibit the kind of collaboration and data sharing that AI systems need to thrive.

Enter Synthetic Data: A Privacy-Enhancing Game Changer

Synthetic data offers a way forward. As outlined by the FCA’s Synthetic Data Expert Group  (SDEG), synthetic data can be engineered to reflect the statistical properties of real-world financial data without exposing personally identifiable information (PII). Crucially, it enables the simulation of high-risk, low-frequency events such as coordinated mule networks or complex layering schemes, providing a safe environment for model training, scenario testing, and fraud simulation.

This privacy-enhancing technique allows firms to innovate within compliance boundaries—bridging the gap between regulatory expectations and the need for agility in a rapidly evolving threat landscape.

Real-World Financial Crime Use Cases

• Creating Synthetic 'Reject' Profiles in Credit & AML Scoring to Mitigate Data Bias

  One of the longstanding challenges in credit risk and AML modelling is the data bias introduced by a lack of visibility on rejected applicants or transactions. Traditional models are typically trained on accepted or confirmed cases, leading to skewed risk assessments. This creates a risk of unfair credit decisions and missed financial crime indicators.

  
  

  Synthetic data offers a powerful remedy by generating realistic profiles for rejected applicants and previously unseen transactions. This allows institutions to build more representative, inclusive, and resilient models.​

  
  

  A case in point is the Reject-aware Multi-Task Network (RMT-Net), which was developed to model ‘missing-not-at-random’ data in credit scoring. By accounting for the relationship between rejection decisions and subsequent default risk, the model significantly improved predictive accuracy and helped mitigate systemic bias in credit modelling.

  
  

• Enabling Cross-Bank Fraud Collaboration Without Violating GDPR

  As financial crime becomes increasingly cross-border and multi-institutional, collaboration between banks is more important than ever. Yet privacy legislation particularly the General Data Protection Regulation  (GDPR) makes data sharing complex and often legally prohibitive.

  
  

  Synthetic data presents a compliant solution, enabling banks to share anonymised, realistic datasets that maintain the statistical fidelity of real transactions. By using transformer models to generate these datasets, banks can co-develop fraud detection tools without exposing live customer data.

  
  

  This has proved especially valuable in testing cross-border payment fraud models, where synthetic data enabled institutions to simulate and analyse complex typologies across multiple jurisdictions, thereby improving the accuracy and coverage of detection algorithms.

  
  

• Generating Datasets for TechSprints: Testing Real-Time APP Fraud Scenarios

  Authorised Push Payment (APP) fraud where victims are manipulated into transferring money to fraudsters has surged in both frequency and sophistication. Building effective detection tools for APP fraud requires realistic datasets that represent how these scams play out in the real world. However, privacy concerns often limit access to such data.

  
  

  The FCA addressed this issue during its 2022 APP Fraud TechSprint tby deploying synthetic datasets tailored to simulate APP fraud scenarios. These included synthetic transaction trails, communication metadata, and customer behaviours. The result: participants were able to safely test and refine detection solutions under near-real conditions—without compromising data privacy.

  
  

  This initiative illustrates how synthetic data can unlock safe experimentation, foster innovation, and accelerate the development of fraud solutions fit for the modern digital economy.

Generative AI: The Force Multiplier for Financial Crime Prevention

Generative AI, particularly large language models (LLMs), has emerged as a powerful tool in financial crime investigations. When trained on synthetic narratives and typologies, these models can enhance various aspects of investigative processes such as:

• Summarising SARs & Case Files to Assist Investigators in Triaging Alerts

Investigation teams are frequently overwhelmed by the volume of Suspicious Activity Reports (SARs) and case documentation. LLMs can automate the summarisation process, extracting key insights and presenting concise summaries to support triage and escalation decisions.

A joint study by SAS and the Association of Certified Fraud Examiners (ACFE) showed how LLMs could reduce time-to-triage by processing large intelligence documents and highlighting relevant risk indicators. When paired with Retrieval-Augmented Generation (RAG) methods, these systems can also reference internal policies or historical cases—ensuring contextual accuracy and enterprise security.

• Identifying Hidden Linkages Across KYC, Transaction & Open-Source Datasets

Financial crime rarely occurs in isolation. Detecting suspicious activity often involves connecting seemingly unrelated entities, transactions, and behaviours.

LLMs can analyse customer due diligence records, transaction logs, and open-source intelligence to uncover non-obvious relationships or behavioural anomalies. Oracle's deployment of AI agents in financial crime detection is a leading example of using NLP and generative models to identify fraudulent patterns in massive, multidimensional datasets.

• Modeling Typologies by Learning from Synthetic Patterns to Spot Similar Risks in Production Environments

By training generative models on synthetic data that reflects known fraud patterns, institutions can create typology-aware models capable of identifying similar risks in production environments. This approach supports early detection of evolving threats—such as newly emerging scams or fraud vectors not yet captured by historical data.

As noted in recent research by ADaSci, these systems not only improve detection but also help compliance teams adapt faster to the changing tactics used by sophisticated threat actors.

Risks & Realism: Governance Is Key

Whilst the potential of generative AI and synthetic data is vast, their implementation must be carefully managed. Without strong governance, these technologies can introduce new risks. Generative AI, for instance, may 'hallucinate' whereby it produces plausible but inaccurate information. Synthetic data, if not properly validated, can misrepresent real-world patterns and lead to flawed models or compliance breaches.

To ensure safe and effective adoption, financial institutions should embed both technologies within a robust governance framework that includes:

• Rigorous Testing & Documentation

Reliable outcomes depend on rigorous, repeatable testing. Institutions should apply comprehensive validation procedures across a range of scenarios, documenting model performance, assumptions, and limitations. This transparency not only supports audit readiness but helps maintain stakeholder confidence in the system’s reliability. The International Monetary Fund (IMF) has underscored the importance of rigorous testing in its guidance on AI in financial services, warning that insufficient validation can lead to systemic risks and operational blind spots.

• Legal Review of Data Generation & Use

Compliance does not end with technical safeguards. A thorough legal review is essential to ensure that data generation and AI use align with current data protection laws particularly around privacy, consent, and cross-border processing.​ The Roosevelt Institute has highlighted the legal grey areas in AI deployment within financial services. Institutions that fail to consult legal and regulatory experts early risk reputational damage and regulatory sanctions even when synthetic data is used.discusses the necessity for legal oversight in the deployment of generative AI within financial services, noting that without proper legal review, institutions may face significant compliance challenges. ​

• Model Risk Assessments & Explainability Standards

As AI becomes more embedded in decision-making processes, ensuring models are interpretable and explainable is no longer optional. Regular risk assessments must evaluate not only accuracy but also fairness, bias, and operational impact. The Office of the Superintendent of Financial Institutions (OSFI) in Canada advocates for clear explainability standards, especially where AI models influence high-stakes outcomes like transaction blocking, customer onboarding, or SAR filings. A robust model risk framework should guide lifecycle management from training and deployment to retraining and retirement.

Strategic Decisions: Build, Buy, or Partner?

For financial institutions seeking to implement synthetic data and generative AI into their financial crime compliance frameworks, a key decision lies in how to approach capability development. Broadly, this comes down to three strategic options: building in-house, buying off-the-shelf solutions, or forming partnerships. Each route offers distinct advantages and challenges, and the right choice often depends on an organisation’s size, maturity, risk appetite, and strategic goals.

• Building in-house allows for maximum control, customisation, and integration with existing systems. Institutions that build their own synthetic data engines or AI-driven compliance tools can tailor them to specific risk profiles, operational nuances, and regulatory obligations. However, this path requires significant investment in time, talent, infrastructure, and ongoing model governance. It also entails managing all regulatory, legal, and technical risks internally. This approach may be best suited to larger institutions with established data science capabilities and strong regulatory engagement.

• Buying third-party solutions, such as synthetic data generators or AI analytics tools, provides a faster route to deployment. Vendors often offer proven, scalable platforms with technical support, regular updates, and domain-specific functionality. While this approach reduces the burden of development and speeds up time-to-value, it may limit customisation and can create vendor lock-in risks. Institutions must also conduct thorough due diligence to ensure the chosen solution meets privacy, security, and compliance standards especially if it handles sensitive data.

• Partnering with academia, fintech innovators, or regulatory sandboxes can offer a balanced and future-forward approach. These collaborations allow institutions to access cutting-edge research, specialised capabilities, and shared learning environments. For example, co-developing synthetic datasets in partnership with regulators or participating in TechSprints can accelerate innovation while keeping development aligned with policy goals. Partnerships may also mitigate some of the reputational or regulatory risks by demonstrating transparency and collaborative intent.

Ultimately, a hybrid approach is often most effective. Institutions might partner to develop synthetic data frameworks, buy foundational technology, and then build bespoke analytics or integrations in-house. The key is to approach this decision strategically, aligning technological adoption with long-term compliance transformation goals and enterprise data governance standards.

Conclusion: Shifting from Financial Crime Compliance to Intelligence

As financial crime grows in sophistication, the imperative for institutions to modernise their AML and fraud prevention frameworks becomes more urgent. When applied responsibly, generative AI and synthetic data are not merely tools; they represent a strategic opportunity to transform compliance into a proactive, intelligence-led capability.

This convergence offers a clear inflection point. Financial institutions no longer need to choose between innovation and regulatory rigour. With the right governance, testing protocols, and partnerships, both can be achieved in tandem.

The opportunity is clear. The frameworks are emerging. The tools are ready. The time to act is now.

Is Your Financial Crime Framework Ready for the AI Era?

At OpusDatum, we help financial institutions deploy synthetic data and generative AI to modernise AML and fraud detection; safely, responsibly, and effectively.

Whether you're testing new typologies, building AI-ready datasets, or designing model governance, we’ll help you move from reactive compliance to intelligence-led prevention.

Get in touch to explore how we can support your next step.