BAS AI Summer Intern

About ActiveViam

If you believe that data analytics can contribute to making Financial Services a more accountable and risk-aware business in an ever-increasing volatile environment, ActiveViam is the right place for you.

Founded by a group of industry experts, ActiveViam understands the data analytics challenges faced by financial institutions across trading desks, risk, and compliance. That is why we pioneered the use of high performance analytics in finance, helping the largest investment banks, asset managers and hedge funds make better decisions, explain results with confidence, and simulate the impact of their decisions. Unlike most BI players, we are not generalists. Our mission is to deliver train of thought analysis on terabytes of data in the most cost-effective way so our customers can explain what happened with confidence and model the scenarios that will optimize their business. We are a pure player specializing in risk data analytics for one of the fastest-moving and most regulated industries with a presence in the world’s leading financial market places – London, New York, Singapore, Hong Kong and Paris.

Our 150 employees take pride in being experts in developing and supporting a purpose-built analytics technology that has been recognized as “FRTB Product of the Year” by Risk and the “Award for Excellence for Regtech, Big Data and Analytics” by Regulation Asia.

The Role

As part of our ongoing efforts to improve our product and development environment, we are investigating the use of AI technology.

In this role, you will have the unique opportunity to combine the latest AI technology, machine learning models, with our cutting edge product for data analytics, to prototype and produce an AI/ML based product for use in risk management.

You will collaborate with the team to define requirements, investigate various tools, prototype  and present recommendations while using GitHub for version control and team collaboration.

Project Details

Background

• GAN (Generative Adversarial Networks) can be used to produce P&L vectors.
• They are comprised of two (adversarial) components:
  • A Generator: producing fake P&L vectors; it’s goal is to make them look real.
  • A Discriminator: determines if a P&L vector is real or fake (generated)
• In the long run, an equilibrium is achieved where the generator produces P&L vectors that the discriminator cannot tell from the real ones.

Context

• In the FRTB Stress Calibration Cube, we load in 15 years of historical P&L values at the trade level.  We use these to identify the 1 year stress window.
• In the regulations, the IMA ES capital requirement calculations start with ES values that have been “calibrated” to the 1 year stress window.
• In our FRTB solution we start with trade-level P&L vectors.
• In theory these vectors can be generated by any mechanism (e.g. Monte-Carlo).  In practice, I think all clients are currently using historical vectors of size 250.

Idea

• Use the P&L values loaded into the stress calibration cube as inputs to a GAN network.
• Use the outputs from the GAN network as the “calibrated” P&L vectors for use in the IMA ES calculations.

Potential Benefits

• Once the stress calibration data has been created, there is limited need for the risk-engine.
• Better stability than historical vectors (of size 250).

Questions to be Investigated

• Does the GAN network produce high quality P&L vectors?
• How does it compare with other mechanisms for increasing the vector size (e.g. bootstrap)?
• Are the vectors produced “better” than historical vectors?

 

 

Qualifications

• Enrollment or recent graduation from a program in Data Science, Computer Science, Mathematics, or a related field, with a keen interest in data analysis and machine learning,
• Overall GPA of 3.5 or higher
• Strong problem-solving skills that translate to working, maintainable software
• Proficient in Python or Java
• Must have excellent written and verbal communication skills
• Excellent analytical skills
• A self-starter with the ability to work independently and deal effectively with multiple tasks/priorities in a fast-paced environment
• Familiar with basic Unix commands
• Team player with ability to collaborate effectively
• Experience in AI/Machine Learning/GenAI is a plus
• Graduate student preferred

Choose to be different. Join ActiveViam and make a difference in data analytics for the financial services industry.