Second webinar of the Financial Challenge series, presented by VMetrix

Dr. John C. Hull: “Machine Learning is generating a huge effect on both pricing and hedging of derivatives that impact the risk calculation.”

Throughout his presentation and the subsequent conversation with the audience, the expert and author of “Machine Learning in Business: An Introduction to the World of Data Science”, one of the most important books on this methodology, highlighted the fact that the discipline is encompassing a wide range of improvements in different financial operations and therefore its impact transcends the market and focuses on the entire business environment.

The technology associated with Machine Learning (ML) has advanced by leaps and bounds and has been taking over the agenda of the digital transformation that has impacted the investment and financial asset management market since the beginning of the pandemic.

For this reason, in the second online conference of VMetrix’s Financial Challenge cycle, the leading academic and expert in risk and derivatives calculation presented an insight that defined the process related to Artificial Intelligence and Data Science as the way in which technology accesses a large amount of data with the aim of learning from it and establishing connections between variables so that predictions can then be made to improve the performance of financial products.

A panel of VMetrix experts led by Pedro Melo, CEO, Mónica Sánchez, Chief Revenue Officer, and Luis Cisneros, Head of Risk Management of the company, participated in this new webinar and engaged in an interesting conversation after the lecture given by Dr. Hull.

According to the professor’s analysis, the model has now become a key piece for credit derivatives and hedging, as well as in the entire management of investment portfolios.

“Thanks to Machine Learning, as well as the advance of computers, it is possible to handle large data sets at a very relevant and never before experienced speed,” he said.

At the beginning of the masterclass given by Dr. Hull, the author emphasized the fact that the use of these models will be the focus of the fifth industrial revolution, making the risk calculations and derivatives management more efficient for financial institutions such as banks, pension funds, securities agencies or stockbrokers.

“I have worked a lot on the application of ML in credit decisions and derivative transactions. In the first case, I can say that it is almost certain that if an entity or person requests a loan, today it will be the Machine Learning algorithm that will make the decision to give it or not”. Regarding the second point, the academic from Maple Financial Group at the University of Toronto explained the extensive work he has done at a theoretical and practical level in the application of this methodology for derivatives, where he assures that the models have had an important effect on both pricing and hedging.

“It is now being proved that ML algorithms are actually capable of taking better credit decisions than human beings and that is a big success story of Machine Learning,” he said to illustrate how important the methodology is becoming and why it is taking over the study of Finance today.


Value offer in service mode

This is certainly a breakthrough that VMetrix has not missed and therefore the new SaaS solution that the company will launch to the market by the end of this year is incorporating Machine Learning methodologies to deliver better tools to our customers in terms of investment analysis, to detect counterparty risk more efficiently and to make better operational controls. In this regard, CEO Pedro Melo added, “Machine Learning is a fundamental pillar in the development of our software and to create real value in terms of investment portfolio management”.

The new VMetrix platform will allow all types of organizations to move towards the digital transformation that their business require and thus optimize the management of investment portfolios, all in SaaS mode. For this purpose, the system will include 95% of the financial instruments and market data previously loaded, ready to be used. It will also include a deployment developed with world-class technology, which will not require any infrastructure on the part of the client and whose implementation will be fast and efficient.

“We want our clients to trust their investment decisions thanks to our software services and that implies that, for instance, the ML allows us to detect unusual operations and send alerts in real time, which are nowadays essential to protect the portfolios of large, medium and small financial institutions,” explained Monica Sanchez, Chief Revenue Officer of the company.

In this regard, Dr. Hull presented his latest paper on how Reinforcement Learning -one of the three types of Machine Learning along with Supervised Learning and Unsupervised Learning- has been the key to define the coverage of a derivatives portfolio given the high risk involved, since “this is how we discover the optimal strategy to introduce other options to the portfolio”.

According to Luis Cisneros, Head of Risk Management at VMetrix, this is related to the project the company is developing, which seeks to generate better innovations in risk management with counterparties through these three types of ML models. “We have tested supervised models that allow us to classify counterparties; unsupervised models in the event we do not have all the necessary information, and finally regression models to calculate probabilities of default of potential counterparties, so as to incorporate them into our SaaS,” he explained.

In fact, Cisneros assured that the Machine Learning methodology also offers the possibility of performing a better instrument calculation through the CVA (Sensitivity Calculation, defined as a valuation adjustment for counterparty risk) which in the short term will be transferred to a new product called XVA, which is composed of a set of valuation adjustments for derivatives.

It is precisely this last issue that will be the main topic addressed by our speaker in the third and final webinar of our Financial Challenge cycle, called “XVA Experience”, which will be held online on December 5 through The recording of the masterclass on Machine Learning and the first lecture on the end of LIBOR are also available at the same website.

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