Over recent years, Artificial Intelligence (AI) and Machine Learning (ML) have made waves in risk management – especially in the financial sector.

From this sector, the banking industry has been at the forefront of this shift from traditional to technological financial risk management, given its dependence on data management and risk assessment, it seems that there is no slowing down. Both artificial intelligence and machine learning have contributed to streamlining and improving customer service through automated chats, fraud detection, optimised forecasting, and other automations.

The ability to forecast, quantify uncertainty and manage risk has always been a staple in the financial world and even more so since the financial crisis of 2008. This event is seen as the reason that development within financial risk management has been catapulted up the agenda as risk departments and financial risk management software providers look to provide the foresight necessary to be one step ahead of any pending disaster.

While the traditional manner of risk management has pioneered this practice, recent times have called for increased automation and information leverage to provide an enhanced level of accuracy and efficiency. Traditional risk models are limited to typical scenarios that rely on the extrapolation of previously observed patterns and trends. This is dangerous because it offers a highly limited scope, and given the dynamic changes in the industry, a better solution must be provided. Aside from this, the traditional approach struggles when it comes to shifting gears, often failing to capture unusual and extreme features and outcomes – especially those that have not occurred in the past. Forward-thinking financial services organisation have recognised that this dependence has proven to be a struggle and are embarking upon a technology reboot and retooling.

The adaptation of Artificial Intelligence and Machine Learning software is attributed to several factors, the most significant being the ability to process and assess massive volumes and different types of data in a shorter period of time, whilst ensuring the accuracy of results, streamlined processes and efficiency – giving organisations the advantage of guided business decisions, and a relevant decrease in risk.

With the need and demand for a long-lasting solution, 4-Xtra Technologies has paved the way, taking the first leap to create an innovative method that is aimed to resolve these shortcomings and concerns. Interestingly, the 4-Xtra modelling technology was initially created as a response to an environmental crisis brought about by air pollution. This was the origin of the first modelling technique created through a postgraduate research project entitled “Extreme Value Modelling to Study Roadside Air Pollution Episodes” at the University of Leeds. The developed extreme value theory has become the foundation of the 4-Xtra risk modelling platform, married with a peak-over-threshold methodology – it proves to be one of, if not the most, effective, and advanced extreme risk management solutions available on the market today.

The past ten years have been quite eventful for the 4-Xtra Technologies team, guided by the ultimate goal of providing a Software-as-a-Service (SaaS) solution to the financial services market which empowers banks, as well as other financial institutions, to benefit from the model’s ability to forecast extreme events across a number of use cases.

From portfolio optimisation, trading of any number of asset classes, securities and foreign exchange, these solutions aim to enhance the risk management decision-making processes and ultimately improve forecasting, sustainability, and profitability.

4-Xtra is proving to disrupt the risk modelling technologies currently available, the company’s mandate is being driven by the unstoppable force of regulatory change. The 4-Xtra risk modelling platform incorporates the use of nonstationary data within the latest AI technologies and probabilistic forecasting technologies, such as Markov Chain Monte Carlo (MCMC). This method predicts the probability of different extreme outcomes, based on the intervention of random variables through repeated sampling that results in algorithms that give systematic forecasting, leveraging high-dimensional probability distributions and eliminating the difficulty that is brought about by a large number of variables.

Using the Bayesian model for comparison, based on the “Bayes factor”, the 4-Xtra methodology emerges as the superior model framework against classical models currently deployed by the majority of the risk modelling software available in the market. The difference in accuracy using the MCMC methodology of 4-Xtra is six to seven times. The use of AI and machine learning techniques also allows for an automated adjustment to threshold change, efficient dynamic estimation, and a fast model fitting.

As mentioned earlier in this article, 4-Xtra marries its model with the peak-over-threshold methodology, giving it a significant advantage compared to the classical regression implementation of statistical modelling. Its threshold stability means that if there are any changes in the values or factors (in any case other levels of risk need to be tested), the parameter of the current fitted model does not require re-running, and can merely be recalculated using simple algebraic formulas. This allows clients to save a significant amount of time and resources while achieving accurate results.

To summarise, below are some of the key benefits of utilising the risk modelling software of 4-Xtra technologies:

1. Improved Risk Management – Receiving an accurate report on the probabilities of risks in differing scenarios, with various stressors, provides clients with the foresight to recalibrate movements and make better business decisions to fit the possible risks involved.

2. Optimised Forecasting – An improved view and understanding of probable outcomes allows users to adjust strategies as needed. In the past, when forecasting is executed using the traditional manner, there is a higher chance of inaccuracy and limitation as it is always based on previous achievements and estimates versus the automation that includes present factors as well.

3. Streamlined and Efficient Calculations – This program allows users to look into more variations of possibilities without having to do a complete recalibration, it can easily be recalibrated through simple algebraic formulas. This saves clients a significant amount of time, all while receiving optimal results.

4. Adaptable Business Configurations – The flexibility of this software can be configured to match the industry-specific needs of clients with ease.

5. Automated Software-as-a-Service (SaaS) – This fully automated program can run unsupervised. In addition, since it functions as a SaaS, it allows clients to use the software at any given time, in any location by their registered users. The convenience and easy access will prove to be an advantage.

6. Flexible User Interface – Variable thresholds and swift recalibration turnaround time prove to be an advantage to clients as the models and interface can be adjusted to meet their specific business needs.

7. Threshold Stability – While various research groups study nonstationary multivariate peaks-over-threshold, 4-Xtra is the only study that addresses threshold stability.

In relation to these key benefits, we are also sharing some of the possible use cases of 4-Xtra Technologies’ risk modelling software below:

1. Portfolio Optimisation – This model provides users with software that is designed to provide a better understanding of the risks within their investment portfolio, allowing them to use this tool to adjust their strategies and optimise their portfolios at the same time.

2. Hedge Fund Managers and Option Writers – Traditionally, the classical model of Monte Carlo is used for this but 4-Xtra provides a better tool to adjust and manage risks for clients through their MCMC methodology paired with a peaks-over-threshold method.

3. Regulators – Historically, this vital group of people in a financial institution require a more accurate way to decipher any unusual movement or irregularities. This tool will allow them to observe, manage and decide to intervene as they deem fit, all to avoid contagion or the possibility of profiteering.

4. Fraud Detection – The possibility of having a more accurate algorithm for detecting fraudulent transactions for clients through their credit cards will benefit the business entity and provide a better customer experience by preventing these transactions.

5. Credit Risk – Although traditional credit risk models remain the standard for predicting variables for this kind of assessment, the use of AI technology can optimise the parameters in current models. This is supported by the rise in financial institutions that seek to improve their forecasting under stressors and other scenarios.

These advantages and usages will provide financial institutions an opportunity for a better assessment of their current practices, what can be improved and streamlined, and how service, sustainability, growth, and profitability can be maximised. Ultimately, this reiterates the goal for 4-Xtra Technologies which is to provide a convenient yet accurate solution to clients through a Software-as-a-Service version of their program, readily available for clients to use anytime, anywhere.

Make financial risk management, optimised forecasting, and efficiency within reach by contacting our team through email info@4-xtra.com. Your enquiries will be treated with utmost importance, and we commit to responding within 3 business hours.