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This internship within KPMG's Digital Assurance and Innovation team in Amstelveen, focuses on applying Graph Neural Networks (GNNs) to financial transaction data, aiming to revolutionize audit processes. As audits increasingly rely on large volumes of digital records, traditional manual methods struggle to scale. This project introduces a novel approach by modeling transactions as graphs, enabling detection of anomalies such as fraud or misstatements. Your work will help auditors identify high-risk transactions efficiently and reliably, while ensuring transparency through Explainable AI. This is an opportunity to develop scalable, interpretable tools that make a real impact in high-stakes financial engagements.
Job Responsibility:
Explore and implement Graph Neural Network architectures for anomaly detection
Apply representation learning techniques to financial transaction graphs
Integrate Explainable AI methods to ensure interpretability of flagged anomalies
Collaborate with researchers and auditors to align technical solutions with audit requirements
Develop, evaluate, and document methods in collaboration with academic supervisors at KPMG and UvA, with the aim of producing publishable results for leading international venues in machine learning
Requirements:
Currently enrolled in a Master's program in Computer Science, Data Science, AI, or related field
Strong programming skills and familiarity with machine learning frameworks
Knowledge of graph-based models and neural networks
Nice to have:
Interest in financial systems and audit processes is a plus
What we offer:
KPMG buddy who will help you along the way
A laptop
A good way to earn a starters position at KPMG
Access to a professional network with lots of knowledge and experience
Social activities with your colleagues
You earn a day of paid holiday leave per month
Flexible hours to combine work and your appointments with the university or other obligations
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