Development of advanced credit scoring models leveraging machine learning to accurately assess borrower risk and identify profitable lending opportunities.​
Implementation of robust fraud detection systems utilizing cutting-edge architectures (e.g. Autoencoders & GANs) to identify and prevent fraudulent activities
Creation of models combining pattern recognition and reinforcement learning to predict asset returns and optimize portfolio allocation for maximized risk-adjusted returns.
Implementation of specialized models for calculating Probability of Default (PD), Loss Given Default (LGD), Portfolio Risk (PR), and Value at Risk (VaR) to ensure regulatory compliance and informed decision-making.
Tailored integration of Large Language Models (LLMs) into corporate environments to enhance decision-making, improve customer service, and streamline internal operations.
