Current industry problems of applying AI in the Financial Industry – What are the solutions ?

Synopsis

Delta Capita Artificial Intelligence Practitioner Partner and Director of AI Development will share some of the current industry problems of applying AI in the Financial Industry ; and share solutions that are evolving to address these problems of:

1) Model Interpretability

This is about making “Black Box” models a “White Box”. Interpretability refers to the ability to make the behaviour and predictions of Machine Learning systems understandable and helps explain crucial aspects of our models:

• What drives model predictions?
• Why did the model take a certain decision?
• How can we trust model prediction?

Interpretability is not only relevant to system engineers and data scientists, but also end-users, model validation teams, risk and compliance for whom it is important to understand the results generated from Machine Learning model.

2) Model Compression of Complex Models

Any firm using machine learning should consider using Model Compression techniques on their usually very large models to save on compute power and make the models run faster. The main approach to tackling challenges in machine learning compression is to produce models which are significantly smaller in both memory and computational requirements without sacrificing the accuracy of the complete original model. An optimal model compression technique would allow you to reduce the space required to store your model, allowing you to deploy your models to smaller hardware devices or, where appropriate, mobile phones and wearable technology

Besides sharing the development and solutions in this space, Delta Capita experts will have time for Q&A.

About the Speakers

Sylvia Smit

Ms. Smit has been the Partner and Head of Equity Markets Delivery (Cash Equities, Equity Derivatives and F&O) at Delta Capita Limited since April 2014. She is a Senior Financial Markets professional specialising in defining and driving Investment Banking organisations to deliver change and meet their tactical and strategic challenges. She has significant experience in project management and business analysis translating business challenges into practical requirements and drive solutions and delivery working as part of the organisation rather than alongside it. She has proven track record of global delivery of large and complex front to back business and technology transformations, cross-asset products and regulatory expertise.

Her Key Competencies includes: Business transformation programme and project management; Senior stakeholder and team management across the business and IT; Regulatory expertise and compliance/business/IT impact analysis; Cash equities, Derivatives and Prime Broking and exposure to all asset classes; Order and execution management, trading strategies and position keeping and Compliance and Middle Office and front to back interfaces. She served as Senior BA/Project Lead at Societe Generale, SGCIB from February 2013 to February 2014. She served as Business Analyst at Bank of America Merrill Lynch from October 2011 to March 2012. She served as Senior Business Analyst at Mizuho Investment Management from 2007 to 2008; Deutsche Bank, London since 2007 and Fidessa from 2000 to 2007. She was Business Analyst at UBS and UBS Investment Bank from 1996 to 2000. She holds BSc. (Hons) in Computer Science from University of Westminster, London.

Ricardo Cruz

Mr. Cruz is a financial services professional with 10+ years’ experience driving project execution and supporting clients with the expertise required to address challenges presented by front-office technology, model risk management, machine learning/AI, digital wealth management and regulation. Entrepreneur and investor, with experience in assessing and leveraging. Fintech to address real world business cases. He thrives on challenges, thinks strategically while driving execution and firmly believes in team work built upon personal responsibility, accountability, ownership and strong work ethics.

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