Intractābilis delivers next-generation fraud detection solutions tailored to the high-stakes, regulation-driven financial sector. By integrating interpretable deep learning, graph-based analysis, and structured reasoning, we help banks, insurers, fintech platforms, and regulatory bodies proactively detect and respond to fraudulent activity. Our systems are designed not only for predictive performance but also for auditability, adaptability, and compliance with data governance standards across jurisdictions.
Conducive Fraud Detection
Conducive Fraud Detection
Fraud Detection in Complex Digital Networks
In today’s digitally connected world, fraudsters are becoming increasingly adept at concealing themselves within legitimate user populations. To evade detection, they employ sophisticated camouflage strategies. By imitating the behavioural patterns of genuine users or by forging connections with legitimate accounts, they can cause significant financial losses and increase regulatory exposure.
Intractābilis has developed a robust and unified fraud detection architecture that tackles different forms of deception. It leverages a Transformer-based framework that combines structure-aware feature filtering with advanced relation modelling, delivering substantial and measurable gains in detection accuracy.
Our framework is designed to identify fraud in graph-structured data by estimating class distributions, removing reliance on deceptive node features, and applying contrastive learning at both the individual and class-prototype levels to enforce tighter class boundaries and improve separation. It also breaks down a network into subgraphs based on different relation types and assigns each subgraph to a specialised model expert, allowing deeper pattern recognition within specific connection types. A dynamic routing system learns the optimal combination of experts to evaluate each node, improving robustness and generalisation. Built-in regularisation encourages collaboration across experts while avoiding overfitting to specific patterns.
Our solution has been deployed in multiple real-world financial networks, enabling lower compliance risk through demonstrably effective fraud modelling.
Noise Resilient Corporate Fraud Detection
Corporate fraud remains a persistent threat to financial markets, with trillions lost globally each year to schemes such as manipulated financial statements and insider trading. As financial data grows in both scale and complexity, identifying fraudulent behaviour becomes increasingly difficult.
Intractābilis addresses this challenge by combining domain-rich financial data with advanced graph neural networks to reveal hidden and complex relationships between companies. An attention mechanism highlights the most relevant connections while filtering out misleading or irrelevant links. By drawing insights from both direct relationships and multi-step pathways within the network, the model captures the broader financial ecosystem that surrounds each entity.
This solution marks a significant advance in automated financial surveillance. It delivers strong detection capabilities while complementing approaches such as time series anomaly detection and hybrid models that combine tabular and network data. Unlike other methods, it is purpose-built to process multiple relationship types and to remain effective even in the presence of noisy information.
Quantum Machine Learning for Credit Card Fraud Detection
Credit card fraud is a pervasive and rapidly escalating global challenge, inflicting billions of pounds in losses on individuals, financial institutions, and businesses every year. The problem is exacerbated by the ever-growing volume and complexity of transactional data, the inherently imbalanced nature of fraud detection datasets, and the mounting demand for instantaneous and high-confidence decision-making. These factors collectively expose the limitations of conventional machine learning methods, which can struggle to maintain both speed and accuracy under such conditions.
Intractābilis has developed an advanced Sampler Quantum Neural Network (SQNN) based framework designed to model the intricate probability landscapes inherent in financial transactions. By capturing critical probability peaks, the SQNN significantly enhances sensitivity to subtle and rare fraudulent behaviours that might elude classical detection frameworks. This capability is particularly valuable for identifying low-frequency and high-impact anomalies in real time.
As quantum hardware matures, the intrinsic sampling efficiency of SQNNs has the potential to surpass that of classical probabilistic models, offering superior accuracy and higher throughput for large-scale fraud monitoring systems. Furthermore, the stochastic sampling process embedded within SQNNs acts as a form of inherent regularisation, mitigating overfitting in imbalanced datasets and ensuring greater robustness across diverse operational environments. The result is a scalable and future-ready fraud detection solution capable of delivering both immediate performance gains and long-term competitive advantage.
Our fraud detection technologies are built for financial institutions facing the dual pressures of sophisticated adversaries and stringent compliance requirements. Whether deployed for transactional screening, behavioral monitoring, corporate fraud audits, or network risk analysis, our solutions offer a unified advantage: advanced AI performance paired with regulatory alignment, scalability, and interpretability.
Ready to Work Together?
©2025 Intractābilis