Artificial Intelligence (AI) and especially Machine Learning (ML) tools have generated a lot of interest and investment from banks, asset managers, pension funds and hedge funds. These companies strive to utilize various ML techniques such as supervised and unsupervised ML, Natural Language Processing (NLP) and Data Science to develop better-investing strategies, find new competitive insights from data they collect and buy, and ultimately increase profits and beat the competition. There is a vast amount of data that needs to be properly processed and advanced technologies can assist with this. Their algorithms can process data at a much higher speed than traditional methods, as well as lead to higher accuracy in prediction due to the advanced learning they can do. To ensure they can be adopted within the business, there is still the need to prove that they can achieve better results than traditional methods and ensure key stakeholders within financial institutions trust the results of the models which are sometimes complicated to understand.

The GFMI 4^th Annual Machine Learning in Quantitative Finance conference taking place on October 3-5, 2022 in New York, NY and virtually will offer case studies on how financial firms can implement machine learning within business operations with utmost effectiveness. Solutions of how firms can increase explainability and interpretability in results of black-box algorithms within their models will be focused on.  This conference will also investigate how machine learning can be used within ESG processes and the current most effective uses of natural language processing. Furthermore, the current regulatory environment will be examined and the best practices for implementation of alternative and synthetic data will be discussed and evaluated. 

Attending This Premier marcus evans Conference Will Enable You To:

• Determine the best practices to increase the explainability of machine learning models outputs
• Understand how machine learning can best be implemented within ESG operations
• Acquire increased knowledge and clarity on the potential increase in new regulations of machine learning
• Assess the most effective uses of natural language processing
• Analyze how data can be practically applied to business problems and discuss the future implications of machine learning

Best Practices and Case Studies from:

• Arthur Maghakian, Managing Director, Data Science And Machine Learning, Goldman Sachs
• Amit Deshpande, Head of Fixed Income Quantitative Investments and Research, T. Rowe Price
• Trevor Mottl, Managing Director & Artificial Intelligence Portfolio Manager, Lazard Asset Management 
• Peng Cheng, Head of Machine Learning Strategies, JP Morgan 
• Caio Natividade, Managing Director, Global Head of Quantitative Investment Solutions Research, Deutsche Bank
• Hamdan Azhar, Head of Data Science, Blackrock

Financial IT subscribers are entitled to a 15% early bird discount! For more information please contact Ria Kiayia, Digital Media and PR Marketing Executive at riak@marcusevanscy.com or visit: https://bit.ly/3O2ZK0l