55:15
Coding for High Frequency Trading
Richard Croucher talks about the application environments, design patterns and programming languages commonly deployed across Financial Services including Investment Banks and Hedge Funds.
44:50
Building and Trusting a Cloud Bank
Greg Hawkins discusses how Starling Bank, part of the new movement in FinTech challenger banks, is innovating while addressing the need for resilience in a world where failure is everywhere.
34:33
Reactive & Asynchronous - Adventures with APIs in Financial Trading
Michael Barker discusses several low-latency APIs used for financial trading, what makes them fast and how they compare to HTTP/REST/JSON/XML APIs.
38:06
The Move to AI: from HFT to Laplace Demon
Eric Horesnyi and Albert Bifet discuss how hedge funds have moved beyond High Frequency Trading using AI and real-time data processing.
45:11
Policing the Stock Market with Machine Learning
Cliff Click talks about SCORE, a solution for doing Trade Surveillance using H2O, Machine Learning, and a whole lot of domain expertise and data munging.
26:58
Creating Customer-Centric Products Using Big Data
Kriti Sharma talks about how Barclays is solving some of the toughest big data challenges in financial services using scalable, open source technology.
45:25
Fighting the #fintech Wave with DevOps
Benjamin Wootton discusses doing DevOps in banking and financial institutions, providing advice on performing a DevOps transformation in such an environment.
48:17
Using Pony for Fintech
Sylvan Clebsch talks about using Pony for fintech to build high-performance tools. Pony is a new actor-model statically typed language, compiled AOT, with a GC and a data-race free type system.
26:35
Driving Customer Engagement & Acquisition in the Digital Era
Rana Pereis explores case studies of how banks are using APIs to evolve new digital offerings and business models that help drive customer engagement and acquisition.
42:23
Real-Time Fraud Detection with Graphs
Jim Webber talks about several kinds of fraud common in financial services and how each decomposes into a straightforward graph use-case. He explores them using Neo4j and Cypher query language.
42:33
Financial Modeling with Apache Spark: Calculating Value at Risk
Sandy Ryza aims to give a feel for what it is like to approach financial modeling with modern big data tools, using the Monte Carlo method for a a basic VaR calculation with Spark.
41:45
Functional Patterns in Domain Driven Design applied to Financial Models
Debasish Ghosh demonstrates how DDD principles can be implemented more effectively using functional programming principles, building a ubiquitous language with pure functional abstractions.
