The Artificial Intelligence Finance Institute’s (AIFI) mission is to be the world’s leading educator in the application of artificial intelligence to investment management, capital markets and risk. We offer one of the industry’s most comprehensive and in-depth educational programs, geared towards investment professionals seeking to understand and implement cutting edge AI techniques.
Taught by a diverse staff of world leading academics and practitioners, the AIFI courses teach both the theory and practical implementation of artificial intelligence and machine learning tools in investment management. As part of the program, students will learn the mathematical and statistical theories behind modern quantitative artificial intelligence modeling. Our goal is to train investment professionals in how to use the new wave of computer driven tools and techniques that are rapidly transforming investment management, risk management and capital markets.
Yields.io is the first FinTech platform that uses AI for real-time model risk management on an enterprise-wide scale.
Our clients use our solution to speed up model validation tasks, to generate regulatory compliant documentation and to industrialize model monitoring. The platform works with all models that are used within the financial sector such as credit risk models, valuation algorithms, market risk, AML, AI and behavioural models.
Yields.io was founded by Jos Gheerardyn and Sébastien Viguié. The company is expanding quickly and has offices in Brussels and London. Yields.io has an international portfolio of clients with both investment banks as well as regional financial institutions.
ALib® is a Quantitative Financial Analytic Library relied on by banks, hedge funds, exchanges, asset managers and their service-providers.
Suite LLC delivers and supports ALib for a global user base requiring transparent industry-standard pricing and portfolio risk management tools. Supported asset classes include Fixed Income Cash/Derivatives, Credit Derivatives, FX and Equity Derivatives.
The libraries encapsulate real world market intelligence and are continuously enhanced to employ current pricing methodologies. Deployed most rapidly as Excel® Add-ins or via MATLAB®, the ALib functions are also commonly integrated as the analytic component of vendor or in-house applications – ensuring consistent calculations across the front, middle and back office.
Quantitative engineers save time by leveraging ALib’s proven financial-math functions as a foundation upon which they can innovate. Portability across technical environments is ensured via well documented APIs including C#, C/C++, Java, Python and more.
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Both through regulation and industry practice, there is an increasing number of risk calculations that need to be done on a regular basis. These calculations require the valuation of portfolios on up to hundredths of thousands of scenarios making them computationally very expensive in time and cost.
MoCaX technology, based on Chebyshev Spectral Decomposition methods, is a methodology and software application which massively reduces the computational burden in a risk calculation. This is achieved by pricing the portfolio on very small number of pre-defined collection of points yielding an object capable of approximating a pricing function and its greeks to a very high degree of accuracy. The object can then be evaluated on thousands of risk scenarios in an ultraefficient and numerically stable manner.
Several benefits are obtained with this technology. Applications include Market Risk VaR, IMAFRTB, Dynamic Initial Margin for MVA and IMM, Exposure profiles for CVA and IMM, what-if analysis tools, etc.
The Numerical Algorithms Group (NAG) are experts in numerical algorithms, software engineering and high-performance computing. They have served the finance industry with numerical software and consulting services for over four decades because of their outstanding product quality and technical support. Specifically, relevant to the finance industry, NAG pioneer in the provision of the NAG Library – numerical, machine learning and statistical components ideal for building Quant Libraries, Risk Applications and the like.
NAG also provides best-in-class C++ operator-overloading AD tools for CPU and GPU called dco (derivative computation through overloading) and dco/map (dco meta adjoint programming). The NAG Library and AD tools are used by many of the largest Investment Banks where they are embedded in Quant Libraries and XVA applications. As a not-for-profit company, NAG reinvests surpluses into the research and development of its products, services, staff and its collaborations. www.nag.com