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AI and Machine Learning for Risk Management
Complimentary CQF Institute Talk

Tuesday, 1st October New York and online

This talk will be led by Senior Machine Learning Scientist at Microsoft, Francesca Lazzeri, PhD

Talk Overview:
Artificial intelligence (AI), and the machine learning techniques that form the core of AI, are transforming how we approach financial risk management. Everything to do with understanding and controlling risk is up for grabs through the growth of AI-driven solutions: from deciding how much a bank should lend to a customer, to providing warning signals to financial market traders about position risk, to detecting customer and insider fraud, and improving compliance and reducing model risk. 
In this talk, Francesca will detail current AI and machine learning techniques being used and current applications of those techniques. In particular, she will present an “AI application to credit risk” use case: the use of AI and machine learning techniques to model credit risk is not a new phenomenon though it is a growing one. Back in 1994, Altman and colleagues performed a first comparative analysis between traditional statistical methods of distress and bankruptcy prediction and an alternative neural network algorithm, and concluded that a combined approach of the two improved accuracy significantly (Altman et al, 1994). The evidence is that credit risk management capabilities can be significantly improved through leveraging AI and machine learning techniques due to its ability of semantic understanding of unstructured data. 
Francesca will further envisage the future role for fully AI solutions as the natural next step after the widespread adoption of machine learning in helping the organization to manage risk.
 

Speaker Biography:
Francesca Lazzeri, PhD is Senior Machine Learning Scientist at Microsoft on the Cloud Advocacy team and an expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her work on these issues covers a wide range of industries including energy, oil and gas, retail, aerospace, healthcare, and professional services. Francesca periodically teaches applied analytics and machine learning classes at universities and research institutions around the world (such as Columbia University, Broad Institute, Massachusetts Institute of Technology, and University of Pavia, etc. etc.)
Before joining Microsoft, she was Research Fellow in Business Economics at Harvard Business School, where she performed statistical and econometric analysis within the Technology and Operations Management Unit. At Harvard Business School, she worked on multiple patent data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation.
Francesca is also Data Science mentor for PhD and Postdoc students at the Massachusetts Institute of Technology, and keynote and featured speaker at academic and industry conferences - where she shares her knowledge and passion for AI, machine learning, and coding.
 

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For any questions, please email us at cqfinstitute@fitchlearning.com