Evolution of AI in Finance: The Journey From POC to Deployment
Abstract: Financial industry as compared to other industries is very young when it comes to applying sophisticated machine learning techniques to their data problems. There are a huge number of challenges faced in global organisations that come in the form of technical debt, legacy code, outdated infrastructure, bad data practices, regulatory restrictions and so on. In such an environment, running a data driven machine learning algorithm that often comes with black box methods can prove to be really challenging. This talk will address some of the various challenges faced in the industry including explainability and how some of that can be mitigated more effectively. It will also address the distinction between roles for data scientists, machine learning engineers as well as analysts and how a more collaborative effort along the various stages of the pipeline will help drive projects from poc levels to deployment when running large scale projects in a global team.
Roshini has a background in AI and electronics from Edinburgh university. She has more than eight years of experience in applying machine learning techniques to design scalable robust solutions in the fields of e-commerce, travel and finance. She has worked with modelling user behaviour, preditive models, recommendation systems, generative adversarial models with deep learning frameworks and is currently working on models in finance to assess risk. She is very interested in understanding how AI techniques can be applied in various industries to make them more efficient and accurate. She is also very passionate about encouraging more women to enter and lead in this field and runs the London chapter for women in machine learning and data science. In her free time she dabbles with creating artwork with neural style transfer and travel photography to show how easily AI can be integrated with day to day activities and enhance our creativity.