Hot Technologies behind Modern Finance

Senior architects in the Finance industry are constantly looking for cutting edge technology to gain competitive advantage. These systems require real-time computations, reliable/secure connectivity, high-performance messaging and intense data science. This track will explore the current techniques for Risk and Trading systems, near-time delivery with mobile web applications, new approaches to high-performance computation and intelligent distributed computing. Premier speakers will discuss their approaches and their applicability to capital and money markets, retail/investment banking, trading systems, credit card companies, securities exchanges, etc.

Day of week:

Wednesday

Location:

Legends Ballroom Robinson / Whitman

Host:

Vassil Avramov

Presentations

Advanced Data Visualization: HTML5 and Risk Analysis

10:40am - 11:30am

By: Fabrice Aresu
Product Manager for Risk Solutions at Luxoft

The financial industry is progressively adopting HTML5 for its UI developments, a trend accelerated by the increasing appetite for Mobile solutions. High volumes and the need for near-real time analysis are challenges faced by all institutions, especially in a context of strong regulatory pressure and deeper scrutiny. In addition, regulatory requirements are driving the industry towards adopting the same systems for management reporting as are used for run-the bank. In this session, we will discuss the concrete challenges that we faced at a very large European Investment Bank and techniques we employed to work around them. Subjects covered will include performance, architectural & design choices, as well as lessons learnt from some of the U-turns taken.

Python: why are the big dealers making big bets?

1:45pm - 2:35pm

By: Andy Fundinger , Mario Morales
Senior Consultant at Risk Focus, Python Evangelist-- Machine Learning Research Scientist and Quant Developer, Cofounder, tatt.io

In the last few years a number of large dealers and many other financial institutions have made big investments in Python. We will examine the reasons behind this trend and discuss techniques that facilitate successful projects. Python's strength is in its flexibility. Starting with dynamic typing and extending to more advanced meta-programming capabilities, Python allows code to be written many different ways and is especially good at integrating with C and C++ for optimized code. This makes Python a great fit for finance (especially Risk projects) as it is pretty common to have different groups that want to use different languages for the same project: e.g. Quants usually write in C/C++, while Application Development prefers to use a language with better support for plumbing and UI functionality. We will demonstrate the design and use of an environment for quantative researchers building a market risk simulation first as a basic system and then adding a hypothetical systemic shock. We will also discuss how we can leverage the dynamic typing of the language without sacrificing some of the benefits of a strongly typed languages.

Leveraging OpenSPL for Financial Risk Computation

4:15pm - 5:05pm

By: Ryan Eavy
Executive Director, Enterprise Architecture, CME Group

Across all industries, there is a drive to process information in less time, with greater predictability and with higher efficiency. As the datasets and scenarios continue to grow, data doesn't only become big, it gets heavy, which increases the need to discover methods for higher degrees of parallelism without sacrificing time to market. Beyond software solutions, Finance is increasingly turning to hardware acceleration, but this poses additional challenges in terms of portability and flexibility. During this session we will explore the use of the dataflow computing language OpenSPL and its use in FPGAs for computational finance methods. Through example, this session will provide an overview of the language and expose you to the natural parallelism of dataflow computing using some well-known risk management algorithms like Black Scholes, SPAN and Value at Risk.

Presentations

Hot Technologies behind Modern Finance Open Space

5:30pm - 6:20pm

Big Data in Capital Markets

3:00pm - 3:50pm

By: Alex Raitt , Clive Saha
Director at Credit Suisse, Investment Bank Application Architecture-- Lead Consultant at Risk Focus, Distributed Computing

With all the hype and promise of big data, and big data analytics, widespread adoption of the technology in capital markets industry has lagged behind other financial services sectors, and other industries. That is starting to change. In this presentation we will discuss the how capital markets firms are incorporating big data technology and the challenges they face. We will discuss emerging 'design patterns' and concrete use cases supporting them, in credit risk, price discovery, trading with sentiment analytics and other innovative examples. We'll discuss various technology stacks and their advantages, including batch-based processing, real-time analytics, and noSQL systems. Finally we'll take a look into the future and discuss key technology trends affecting big data in capital markets.

Leveraging Big Data for Payment Risk Management

11:55am - 12:45pm

By: John Canfield
Vice President, Risk at WePay

The payments market's hottest growth is coming from small- & micro-businesses, individuals, and fundraisers. The burrito truck using Square or the Care.com babysitter accepting credit cards through a mobile app are the new normal. These non-traditional entities present new challenges for payment risk management because there is little traditional risk data on these micro businesses. A next generation of risk management is emerging that incorporates new data sources. Social network profiles and digital footprints can be used to confirm one's identity. Additionally, marketplaces and business software providers who integrate payments into their offering have a lot of data about their merchants' identity, operations, and reputation that can be leveraged. The challenge is collecting this heterogeneous data from hundreds of data sources and storing it in a usable data model. The other obstacle is building the policy, rules, and machine learning systems that will make optimal decisions on this data. This talk will discuss the changing payment ecosystem, innovations in mining and organizing unstructured data from many sources, and approaches to decisioning for loss minimization and user experience.