Build supervised algorithms that predict future prices and liquidity movements, by extracting hidden features within data. Building flexible models that learn from new trends by adapting to the free flow market information. Determine assets that are mispriced as found in niche markets.
Improve the accuracy of risk modelling by identifying key data features and nonlinear patterns within large datasets. Enhance your decision making with well-trained deep learning models, that ensure a more efficient means to manage risk. Build early warning systems that automate reporting, portfolio monitoring, and contingency plans.
Construct well-diversified portfolios using unsupervised learning, such as cluster analysis to identify assets with similar characteristics, and reinforcement learning to allocate capital dynamically in an ever-changing environment. Use regularisation and semi-supervised learning for portfolios with label data more expensive to assemble.
Natural Language Processing (NLP)
Make use of NLP to more intricately assess value in investment strategies such as long/short equity and discretionary with models that more accurately gauge market sentiment. Provide highly relevant answers to questions related to financial planning.
Fraud Detection and Identity Management
Machine learning in fraud detection has become the go-to technology for classification of fraudulent behaviour. Supervised learning methods such as neural networks have shown to be very effective at detecting fraud when trained on a large amount of financial statement data.
Determine the credit-rating of a borrower from social media activities and transaction history. Build new credit markets with customer and clients that lack a long-term credit history. Use supervised learning to more accurately assess companies’ credit scores, e.g., using neural networks.