The Age of Generative AI is here for Financial crime compliance

The financial crime landscape is undergoing a transformative shift. Traditional approaches are being swiftly reshaped by the disruptive force of artificial intelligence (AI) and machine learning (ML). Over the past two decades, AI/ML has redefined how we combat illicit activities such as money laundering and fraud. Now, as we look ahead to the next 2–3 years, a new wave of innovation is coming with the advent of generative AI (Gen AI). Generative AI (Gen AI) is poised to be the disruptive force driving this change.

Change within financial crime compliance is swift and relentless. ML algorithms have already revolutionized our ability to detect and prevent financial crimes. Yet, the horizon presents new challenges and opportunities. Gen AI stands at the forefront of this evolution, offering capabilities to push the boundaries of detection further. How will these emerging technologies transform financial crime compliance?

Here is my take:

1. Data Generation and Augmentation: Generative AI can create synthetic data that closely mimics real-world financial transactions. This synthetic data can augment existing datasets, allowing for more robust training of machine learning models without compromising the privacy of sensitive financial information. Remarkably, even fintech startups lacking historical data could leverage Gen AI to test and deploy financial crime solutions effectively.
2. Unsupervised Anomaly Detection: Generative models can be trained to understand the patterns and distributions of normal financial transactions. By comparing new transactions against these learned patterns, anomalies indicative of potential fraud or money laundering can be detected. Generative models can capture subtle patterns that may be overlooked by traditional rule-based systems or simpler machine-learning algorithms.
3. Automating the investigation work: Gen AI could automate various aspects of the investigation process, efficiently generating summaries, reports, and investigation notes, effectively augmenting human expertise and freeing up compliance professionals to focus on more strategic tasks.
4. Adversarial Attack Detection: Adversarial attacks involve malicious actors attempting to manipulate AI systems by subtly altering input data. Generative models can be trained to recognize these adversarial attempts by generating potential attack scenarios and learning to detect patterns indicative of manipulation.
5. Scenario Simulation and Risk Assessment: Generative models can simulate various scenarios of financial transactions and assess their risk levels based on historical data and regulatory requirements.

To better understand the transformative potential of Gen AI, let’s explore two critical areas: anomaly detection and explainability.

Anomaly detection

Traditional machine learning (ML) models for fraud detection often rely on labelled data, where examples of both normal and fraudulent transactions are provided during training. However, obtaining labelled data for fraud can be challenging and time-consuming, especially for emerging or novel fraud schemes. Generative AI approaches, on the other hand, can perform unsupervised anomaly detection without the need for labelled data.

Let us understand this in detail.

Traditional Unsupervised ML Approach:

Using a traditional unsupervised ML approach, the financial institution might employ clustering algorithms, such as k-means, to group transactions into clusters based on their features (e.g., transaction amount, time of day, location). Anomalies could then be identified as transactions that fall outside the clusters or have significantly different characteristics compared to the majority of transactions. However, this approach may struggle to capture complex patterns in the data and distinguish between genuine anomalies and benign fluctuations.

Generative AI Approach:

In contrast, a generative AI approach could involve training a variational autoencoder (VAE) to learn the underlying distribution of normal credit card transactions. The VAE would be trained to reconstruct input transactions while also generating new, synthetic transactions that closely resemble real ones. During training, the VAE would learn to represent the complex relationships and dependencies present in the transaction data.

Once trained, the VAE could be used for anomaly detection by reconstructing each incoming transaction and comparing it to the original input. Transactions that cannot be accurately reconstructed, or have high reconstruction errors, could be flagged as anomalies. Additionally, the VAE could generate synthetic transactions and compare them to real transactions, identifying deviations as potential anomalies.

Explainability and Automated STR Reporting in Local Languages:

Explainability has long been a challenge in ML-based systems. With the advent of techniques like LIME and SHAP, generative AI seeks to unravel the complexities of decision-making processes, providing transparency to compliance officers and regulators navigating the intricate terrain of the meta-world.

Moreover, the automation of suspicious transaction report (STR) reporting, facilitated by AI-powered natural language processing (NLP), transcends geographical boundaries, streamlining compliance efforts across diverse regulatory landscapes.

Challenges on the horizon:

Despite the promising potential of Gen AI, there are significant challenges that must be addressed both from a technical and regulatory standpoint.

LLMs and long text processing

One such challenge is ensuring that Generative Language Models (GLMs) like the Large Language Model (LLM) can transcend mere summarization tasks and exhibit true “smartness.” Gemini 1.5 introduces capacities for long text processing, yet there remains a need to enhance the model’s ability to understand complex financial transactions deeply.

Hallucination trap

Another hurdle is breaking free from the “hallucination trap” — a phenomenon where the model generates plausible yet incorrect information. To overcome this, better inputs and a meticulous chain of thoughts to break down tasks into manageable steps are imperative. Additionally, fine-tuning the model with human oversight can mitigate the risk of erroneous outputs, ensuring that Gen AI remains a reliable tool in financial crime compliance.

Implementation hurdles:

• Data Quality and Preprocessing: Generative AI models are highly sensitive to the quality and consistency of the training data. Ensuring clean, standardized, and representative datasets is crucial for effective model training and performance. Data preprocessing techniques, such as feature engineering, normalization, and handling missing values, play a vital role in preparing the data for Gen AI models.
• Model Training and Scalability: Training large-scale Generative AI models like LLMs and GANs can be computationally intensive and require significant computing resources. Strategies for distributed training, model parallelization, and efficient hardware utilization are necessary to make these models scalable and deployable in real-world AML/CFT systems.
• Evaluation Metrics and Interpretability: Defining appropriate evaluation metrics for Generative AI models in the AML/CFT context can be challenging. Metrics such as reconstruction error, sample quality, and diversity may need to be combined with domain-specific metrics to assess the model’s performance accurately. Additionally, ensuring the interpretability and explainability of these complex models is crucial for building trust and facilitating regulatory compliance.
• Potential Limitations and Pitfalls: While Generative AI offers powerful capabilities, it is essential to recognize its limitations and potential pitfalls. These models can be susceptible to biases and inconsistencies in the training data, leading to the generation of unreliable or harmful outputs. Robust techniques for bias detection and mitigation, as well as risk assessment and monitoring, are necessary to ensure the safe and responsible use of Gen AI in financial crime compliance.

Regulatory and Ethical Considerations:

• Regulatory Compliance: The adoption of Generative AI in AML/CFT systems will require close collaboration with regulatory bodies to ensure compliance with relevant laws, guidelines, and best practices. Establishing clear standards and frameworks for the development, validation, and governance of these AI systems is crucial.
• Ethical AI and Fairness: Financial crime compliance systems must uphold principles of ethical AI, fairness, and non-discrimination. Generative AI models should be rigorously tested for potential biases and unfair outcomes, particularly in high-stakes decisions that can impact individuals or businesses.
• Privacy and Data Protection: The use of synthetic data generation and privacy-preserving techniques should be carefully evaluated to ensure compliance with data protection regulations and customer privacy rights. Clear policies and safeguards should be in place to protect sensitive financial information.
• Model Security and Robustness: Generative AI models, particularly LLMs and GANs, can be vulnerable to adversarial attacks and model extraction techniques. Robust security measures, such as differential privacy, watermarking, and secure enclaves, should be explored to protect the integrity and confidentiality of these models.

Looking Ahead

The future of financial crime compliance is inexorably intertwined with AI and ML. In the next 2–3 years, Gen AI is poised to augment our detection capabilities further. Large Language Models (LLMs) like GPT-3 and its successors show promising potential in automating various tasks in the AML/CFT domain, such as generating suspicious activity reports (SARs), conducting risk assessments, and providing decision support to compliance analysts.

Additionally, Generative Adversarial Networks (GANs) could play a crucial role in synthetic data generation, enabling the creation of realistic yet privacy-preserving financial transaction datasets. These synthetic datasets can be used to train and evaluate machine learning models, mitigating the challenges of limited real-world data availability.

Moreover, the combination of LLMs and GANs could lead to innovative solutions, such as generating synthetic transaction narratives or explanations to enhance the interpretability and explainability of AML/CFT systems.

It is incumbent upon us to embrace these technological advancements and leverage them to create a more secure financial ecosystem. However, effective implementation and adoption of Gen AI in AML/CFT will require close collaboration between researchers, financial institutions, and regulatory bodies to address the challenges and ensure responsible and trustworthy deployment.

Fighting financial crime with technology

We at Tookitaki, are obsessed with using technology to simplify the process of fighting financial crime. If you are keen to know how AI can transform your compliance operations then check out my articles on

1. Transforming transaction monitoring and threshold tuning with machine learning
2. What is needed to build super powerful, name matching AI models for screening

If you are looking to eliminate manual effort from your compliance operations, reach out to us!

Also, you can download our white paper on how we automate the threshold tuning process.