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Founder's Pitch
"Tide provides customizable synthetic datasets for advanced machine learning in anti-money laundering research."
Commercial Viability Breakdown
0-10 scaleHigh Potential
3/4 signals
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4/4 signals
Series A Potential
3/4 signals
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Why It Matters
This research matters because it addresses a critical gap in the anti-money laundering (AML) space by providing synthetic datasets that mirror real-world financial networks with both structural and temporal patterns, facilitating advancements in machine learning models for AML detection.
Product Angle
To productize Tide, it could be developed into a SaaS tool where financial institutions subscribe for access to customizable synthetic datasets enhancing model development and regulatory compliance efforts.
Disruption
Tide could replace existing simpler synthetic data generators and manual methods of data anonymization used in AML research and model development, enabling more accurate model training and evaluation.
Product Opportunity
The market for AML solutions is substantial, with billions spent globally on compliance and detection. By providing a tool that offers customizable datasets, Tide targets financial institutions and AML software providers, who can justify the cost against the risk of non-compliance and fines.
Use Case Idea
The synthetic datasets generated by Tide can be used by financial institutions and compliance software companies to train and evaluate AML detection models without the need for sensitive transactional data.
Science
Tide is a synthetic dataset generator that creates graph-based financial datasets with customizable money laundering patterns. It incorporates both structural and temporal aspects of transactions, allowing users to generate datasets tailored to specific anti-money laundering research. It evaluates detection models under different conditions, showing variation in model performance based on fraud prevalence.
Method & Eval
The method involves generating datasets with varied illicit transaction ratios, testing state-of-the-art models like LightGBM and XGBoost across these datasets. Tide demonstrated its utility by showing how these models ranked differently based on conditions, thus offering a meaningful performance benchmark.
Caveats
The main limitation is that synthetic data, while useful, may not capture all nuances of real-world anti-money laundering activities. Additionally, reliance on synthetic data could potentially lead to models that do not generalize well to unseen, real-world patterns.
Author Intelligence
Montijn van den Beukel
University of Amsterdam
montijnvandenbeukel@gmail.com
Jože M. Rožanec
Jožef Stefan Institute
joze.rozanec@ijs.si
Ana-Lucia Varbanescu
University of Amsterdam/University of Twente
a.l.varbanescu@utwente.nl