In this post we are after a flexible financial services taxonomy that can help us understand both existing and evolving financial system developments. To this end we examine a range of existing classification systems and synthesize the salient requirements.
Who Needs a New Financial Services Taxonomy? Our age is increasingly dominated by the dual challenges and opportunities of the sustainability transition on the one hand, and digital transformation on the other. We witness emerging new financial domains with novel names such as Fintech , or TechFin, or various combinations and hues of Green and Sustainable in Sustainable Finance and we see forces that are reshaping the direction of travel for the financial industry.
The Role of Open Risk Manual Taxonomies A taxonomy is the categorization of concepts. It can be a very useful tool in supporting effective knowledge management. Fundamentally a taxonomy is a scheme of classification, typically a hierarchical classification, in which things or concepts are organized into groups or types of increasing specificity.
Mathematically, a hierarchical taxonomy is a tree structure of classifications for a given set of objects. It is sometimes also named a containment hierarchy.
Offline versus Online In computer technology and telecommunications, online indicates a state of connectivity over digital networks, and offline indicates a disconnected state. Both states have many sub-divisions. For example the type online access varies enormously according to the bandwidth and latency of connections. Similarly, people may be “offline” as not having network access or completely unplugged, as in not having access or using any electronic device.
While the number of people, the fraction of time and the type of activities they engage on has rapidly expanded as digital technology increasingly diffuses, the online state is certainly not the default state and in many regions or population segment might be completely out of reach.
Semantic Web Technologies integrate naturally with the worlds of open data science and open source machine learning, empowering better control and management of the risks and opportunities that come with increased digitization and model use
The ongoing and accelerating digitisation of many aspects of social and economic life means the proliferation of data driven/data intermediated decisions and the reliance on quantitative models of various sorts (going under various hashtags such as machine learning, artificial intelligence, data science etc.
The Non-Perfoming Loan Ontology The Non-Performing Loan Ontology is a framework that aims to represent and categorize knowledge about non-performing loans using semantic web information technologies. Codenamed NPLO, it codifies the relationship between the various components of a Non-Performing Loan portfolio dataset.(NB: Non-performing loans are bank loans that are 90 days or more past their repayment date or that are unlikely to be repaid, for example if the borrower is facing financial difficulties).
Risk, Randomness, Uncertainty and other Ambiguous Terms Uncertainty versus Risk is a popular discussion topic among risk managers, especially after major risk management disasters. The debate can get really hairy and drift into deep philosophical areas about the nature of knowledge etc. Yet the significance of having an as clear as possible language toolkit around these terms should not be underestimated. Practical risk management typically shuns too deep excursions into the meaning of things, yet that is not quite compatible with the use of sophisticated methods and tools (such as a Risk Model ) that assumes an understanding of the scope and limitations of “knowledge”.
The Risk Function Ontology The Risk Function Ontology is a framework that aims to represent and categorize knowledge about risk management functions using semantic web information technologies. Codenamed RFO codifies the relationship between the various components of a risk management organization. Individuals, teams or even whole departments tasked with risk management exist in some shape or form in most organizations. The ontology allows the definition of risk management roles in more precise terms, which in turn can be used in a variety of contexts: towards better structured actual job descriptions, more accurate description of internal processes and easier inspection of alignement and consistency with risk taxonomies.
What is Risk Compensation? Risk Compensation is a behavioral model of human attitudes towards risk which suggests that people might adjust their behavior in response to the perceived level of risk. It follows that, depending on the strength of the effect, that it might counteract and even annul the impact of risk mitigation, if the updated attitude and behavior modifies the actual underlying risk
Examples of potential risk compensation effects abound A prominent example of potential risk compensation in recent times that established the concept in more formal terms in public policy debates concerned the beneficial role of safety belts in automobiles.
A survey of existing definitions of risk When looking up the meaning of Risk we are confronted with a surprising situation. There is no satisfying and authoritative general purpose one-line definition that we can adopt without second thoughts. Let us start with the standard dictionary definitions:
The online Merriam Webster Dictionary defines risk as the possibility of loss or injury The online Cambridge Dictionary opines that risk means the possibility of something bad happening The Oxford English (Concise, Hardcover!
NACE Classification and the EU Sustainable Finance Taxonomy The integration of climate risk and broader sustainability constraints into risk management is a monumental task and many tools are still lacking. Yet there is strong support and bold initiatives from policy bodies and an increasing focus from the private sector side.
The EU (Sustainable Finance) Taxonomy is one such initiative of fundamental significance as it attempts to map at a granular level economic activities with respect to their climate risk mitigation or adaptation potential and create tangible metrics and thresholds to measure progress (the ultimate anti-greenwashing treatment)
Semantic Web Technologies The Risk Model Ontology is a framework that aims to represent and categorize knowledge about risk models using semantic web information technologies.
In principle any semantic technology can be the starting point for a risk model ontology. The Open Risk Manual adopts the W3C’s Web Ontology Language (OWL). OWL is a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things.
A new logo for the Open Risk Manual We have updated the logo for the Open Risk Manual.
The new logo aims to make more explicit both the inspiration that the Open Risk Manual project draws from the trail-blazing Wikipedia initiative (and increasing collection of associated Wikimedia projects) and the reliance on the open source ecosystem of software and tools, including the mediawiki software and the important semantic mediawiki extension.
An overview of EU Financial Regulation initiatives In the European Union there are several ongoing large scale legislative and regulatory projects that transform the context within which individual, firms and the public sector interact economically. While financial and regulatory reform is an ongoing process in all jurisdictions globally, the size and supra-national nature of the European Union makes those projects particularly interesting.
A new entry at the Open Risk Manual aims to provide a brief overview of ongoing projects / initiatives.
We introduce a side-by-side review of the main open source ecosystems supporting the Data Science domain: Julia, Python, R, the trio sometimes abbreviated as Jupyter
Overview of the Julia-Python-R Universe A new Open Risk Manual entry offers a side-by-side review of the main open source ecosystems supporting the Data Science domain: Julia, Python, R, sometimes abbreviated as Jupyter.
Motivation A large component of Quantitative Risk Management relies on data processing and quantitative tools (aka Data Science ). In recent years open source software targeting Data Science finds increased adoption in diverse applications. The overview of the Julia-Python-R Universe article is a side by side comparison of a wide range of aspects of Python, Julia and R language ecosystems.
What is a Risk Taxonomy? There are various formal definitions of risk taxonomies (and we will go over those below), but it might be useful to first look at a very intuitive example of a risk taxonomy: the classification of fire hazards (also known as fire classes)
Everybody knows (or should know!) that the different types of fire (which is the underlying Risk in this context) cannot be treated the same way because they respond in different ways to the substances used to suppress the fire.
Limit frameworks are fundamental tools for risk management A Limit Framework is a set of policies used by financial institutions (or other firms that actively assume quantifiable risks) to govern in a quantitative manner the maximum risk exposure permitted for an individual, trading desk, business line etc.
Why do we need limit frameworks? A limit framework is expressing in concrete terms the Risk Appetite of an institution to assume certain risks.
Python versus R Language Motivation for the comparison A large component of risk management relies on data processing and quantitative tools. In turn, such information processing pipelines and numerical algorithms must be implemented in computer systems.
Computing systems come in an extraordinary large variety but in recent years open source software finds increased adoption for diverse applications (machine learning, data science, artificial intelligence). In particular cloud computing environments are primarily based on open source projects at the systems level.
ESMA Securitisation Templates are now documented at the Open Risk Manual The ESMA Securitisation Templates are now fully documented at the Open Risk Manual. Users can browse, search and cross-reference with the rest of the knowledge base.
Category Browsing The ESMA Templates Categories are part of both the Securitisation category and the Information Technology Category. Each one of the templates and each one of the sections within a template forms its own category.
Representing economic activity using pictograms Visualization can produce significant new insights when applied to quantitative data. It is currently undergoing a renaissance that mirrors other developments in computing and data science. Sophisticated open source libraries such as d3.js or matplotlib, to name but a couple, are enabling an ever wider range of users to distill valuable information from the avalanche of data being produced.
Yet when it comes to visualizing data that relate to abstract concepts it can be quite difficult to find an appropriate grammar to express the quantitative context.
Transparency, collaboration key to regaining trust in financial services In banking, confidence is the first order of business Maintaining the confidence of market participants, clients, shareholders, regulators and governments is uniquely important for the financial sector. Trust is, quite literally, the real currency. Yet it is a truism that confidence is hard to build up and rather easy to destroy. Why is this so?
The short answer: The difficulty in rebuilding trust is linked to the lack of transparency.
