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Data Science x Finance: From Loan Delinquency to Deep Learning

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Détails

Established tech giants and startups alike have been clamoring for a seat at the table in the banking and insurance industries. Adapting to this new reality means using the tools of the newcomers’ trade - data science, big data, and algorithms - and supplementing those technologies with the unique assets and strengths of traditional banking and insurance companies.

This Meet-up will help you learn a bit more about this thanks to Expert Insights.

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First Talk: Dive Deeper in Finance

Deep Learning is becoming a hot topic in Finance and penetrates the industry quickly. We see applications in anomaly detection, customer behavior analysis, risk management, price prediction, algorithmic trading, portfolio construction, etc.

This talk covers two Deep Learning applications in finance. First we look at how to build generative time series models with deep recurrent variational autoencoders. The second application shows how to apply deep reinforcement learning to implement a data-driven portfolio construction and algorithmic trading methodology. This approach can overcome the common information bottleneck in traditional mean variance portfolio optimization or signal based trading engines. We discuss the model concepts and their implementation in TensorFlow.

Speaker's Bio: Daniel Egloff
Dr. Daniel Egloff (https://www.linkedin.com/in/daniel-egloff-37a34010?ppe=1) is Managing Director of QuantAlea (http://www.quantalea.com/), a Swiss company specialized in numerical computing and GPU software development. He studied mathematics, theoretical physics, and computer science and worked for almost 20 years as a quant and software architect in various industries.

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Second Talk: How to Predict Loan Delinquency

Insurance and banking business has always relied on limiting risk. Today, the increase of customer data and advances in algorithms take this to a whole new level. Let's see how advanced analytics can help the FS to forecast loan delinquency.

Speaker Bio: Alexandre Hubert
Alexandre (https://www.linkedin.com/in/alexandre-hubert-3a540620/) is Lead Data Scientist at Dataiku (https://www.dataiku.com/), he has had the opportunity to work with large consulting companies and technical partners on many different big data use cases. His expertise ranges from banking and telco to retail and manufacturing.

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Additional Content:
We have written a free whitepaper on this topic, you can find it:here (https://pages.dataiku.com/advanced-analytics-for-banking-and-insurance2-0)

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This meet-up is for:

- Data geeks of all ages willing to discover how multiple factors which seemingly have nothing in common, can come together to provide insights and forecasts to predict fuel prices in precise locations.

- Data professionals from all horizons (any role, any sector)

As anyone who has attended our meet-ups before will testify, you can expect excellent talks, discussion, and complimentary pizza and beer for everyone!

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If you haven't completed our 30-second long/4-question survey (https://goo.gl/forms/rJamZJp2n8YYogsz2)please take the time to help us make this Meetup group the best!

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Want to collaborate with us to uplift the data community?

Get in touch (https://pages.dataiku.com/collaborate-with-dataiku-to-uplift-data-science-community)