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Financial Risk Management is the study of all the risks a business can experience. These possible risks of losses within the business can be related to many sources. Some of them can be customers, interest rates, etc. However, the way organizations identify financial risks has developed a lot during the last years. Because of that, finance directors are expanding not onlt the techniques. But also the methods and ways of analysing the Financial Risk in order to avoid losses. If you want to know more about the different methods of avoiding Financial Risk keep reading!

New plans in Financial Risk Management

Because of the increasing number of cybercrime threats , it is inevitable the demand of new Risk Analysis Methods. There is also a need of data security. Because of that, some banks were expecting to assign around 30% of their budget to the development of new analysis tools. Thanks to new modeling capabilities and risk management solutions, financial directors have a wide range of options to choose from. This tools being for example MiFID II, Volcker 2.0, GDPR or the EU’s new Benchmark Regulation. It is also important to point out that many of the market’s most dynamic risk analysis methods are very basic. They still use very basic techniques that need improvements.

Same tools, more power

Even nowadays, the most innovative fintech platforms still rely on very basic techniques. This methods can be sensitivity analysis, scenario analysis or Monte Carlo simulations. These techniques are the solution to calculate and determine Financial Risk. Up until now, these models have been superfeed to offer risk liability systems to provide functional analysis and provide good reports. Two of the most employed models in the last years have been liquidity and credit risk. That emphasis on financial legislation will deepen in the next years due to the impact of Basel III’s implementation in January 2019. Basel III will introduce tougher capital requirements and liquidity regulations.

Another major concern for banks is transparency. There is a demand of regulations for cellular datasets. On the one hand, government institutions want to ensure that financial institutions are holding enough quality capital. On the other hand, this is also because regulators will now demand banks release more data related to financial risk. Financial directors have been looking for solutions in order to accomplish those regulatory obligations. One of these solutions is machine learning. Even though machine learning financial risk models  are still experimental in relation to their availability as commercial services or products for financial institutions. Nonetheless, machine learning and these financial risk models are already inspiring existing risk management systems even if they need more development.

Beyond Machine Learning

Machine Learning is a method by which computers can be taught to analyse data and predict an outcome to a financial risk issue. Nowadays there are many interesting models fintechs have been testing to improve their existing financial risk analysis solutions. One machine learning technique developers have been testing is unfolding of artificial neural networks (ANNs). This technique are mathematical simulations to use several input values to connect hidden data layers.

This solution would be perfect to analyse credit risk. Studies show that the potential impact of using ANN suggest this technique is more accurate to assess credit risk. However, ANNs are somewhat intensive in order to set up and maintain. Financial director are currently encouraging hybrids that incorporate characteristics of experimental machine learning methods and existing risk management techniques.

For example, Temenos, a banking software company, has an accounting application that enables organisations to instantly and automatically decide loans to applicants. This is based on predefined policies and a range of granular matrices. The building of custom risk and decision models  is helping companies to improve decision making.

Cyber Wargaming

New systems have very advance data capabilities that are improving financial risk modelling solutions. However, these advances are also creating new institutional threats. According to think tank, the Center for Strategic and International Studies, the global cost of cybercrime exceeded $600bn in 2017. Organisations are feeling more exposed than ever to new threats because of the IoT infiltrating to new business processes.

Cyber wargames are becoming very popular tools in order to create an organization’s ability to resist that exposure. Wargaming is a solution created to improve risk-informed decision making through rigorous and experimental learning. Cyber wargaming is a fairly new method of financial risk analysis. Nevertheless, there are already plenty of industry players offering complete simulation services for all kind of institutions.

There are many possibilities nowadays for risk managers to decide which strategy is the best for each company. However, if want to improve your career in this field, the best option for you is to do a Master on Financial Risk Management. In the University of Alcalá we will guide you with the help of the best professionals in order to give you the best education possible. You will also develop as a professional with a deep and comprehensive understanding of regulations and methodologies to manage financial risk. Do not miss this incredible opportunity and contact us now. We are waiting for you!

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