AI in Finance

Artificial Intelligence is becoming the brains in finance. AI and Machine Learning (ML) have the potential to address specific business problems in finance, delivering better trading, fewer losses, reduced risks and improved service. Using either supervised or unsupervised algorithms, AI augments current analysis and predictive models to deliver more accurate predictions on future prices, risks and credit scoring, improved Machine Learning investment strategies and to fight against fraud. Companies not already investing in AI solutions risk being left behind by competitors.

Predictive Analytics

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.

Risk Management

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.

Portfolio Optimization

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.

Credit-rating

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.

The biggest challenge is finding the right data scientists with the qualities, experience and skills required to identify, build and implement the right kind of AI solution for your business. Brainpool has already helped some of the biggest names in finance thanks to its global network of world-class data scientists. We create tailored solutions for our clients and carefully manage each project, overseeing the build, implementation and testing stages of the system to ensure our clients are completely satisfied with the final product. Thanks to this approach and expertise of members of Brainpool community we maintain a 100% success rate on our projects done to date.

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