Risk Model

What are ActivityPub Networks?

ActivityPub is a technical specification towards decentralized (more precisely, federated) social networking (termed the Fediverse) based upon the exchange of ActivityStreams messages that follow the Activity Vocabulary. The ActivityPub proposal has been standardized and published by the W3C and has motivated the design of several federated social networking systems.

Open Risk White Paper 13: Federated Credit Systems, Part II: Techniques for Federated Data Analysis

In this Open Risk White Paper, the second of series focusing on Federated Credit Systems, we explore techniques for federated credit data analysis. Building on the first paper where we outlined the overall architecture, essential actors and information flows underlying various business models of credit provision, in this step we focus on the enabling arrangements and techniques for building Federated Credit Data Systems and enabling Federated Analysis.

Modeling economic networks and their dynamics

Economic networks are the primary abstractions though which we can conceptualize the state (condition) and evolution of economic interactions. This simply reflects the fact that human economies are quite fundamentally systems of interacting actors (or nodes in a network) with transient or more permanent relations between them.

Concentration, diversity, inequality and sparsity in the context of economic networks

In this second Open Risk White Paper on Connecting the Dots we examine measures of concentration, diversity, inequality and sparsity in the context of economic systems represented as network (graph) structures. We adopt a stylized description of economies as property graphs and illustrate how relevant concepts can represent in this language. We explore in some detail data types representing economic network data and their statistical nature which is critical in their use in concentration analysis. We proceed to recast various known indexes drawn from distinct disciplines in a unified computational context.

Course Objective

Graphs (and the related concept of Networks) have emerged from a relative mathematical and physics niches to become mainstream models for describing and interpreting various phenomena. The objective of the course is to review various important graph types as they are increasingly explored in data science and sketch their linkages and relationships (a graph of graphs!).

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.).

Federated Credit Systems, Part I: Unbundling the Credit Provision Business Model

As an architectural design and information technology approach, federation has received increased attention in domains such as the medical sector (under the name federated analysis), in official statistics (under the name trusted data) and in mass computing devices (smartphones), under the name federated learning.

Open Risk White Paper 9: Federated Credit Systems, Part I: Unbundling The Credit Provision Business Model

In this (the first of series of three) white paper, we introduce and explore the concept of federated credit systems. We review the rapidly developing fields of Federated Analysis and Federated Learning as already actively studied in the domains of medicine and consumer computing devices. This forms the backdrop for understanding the potential and challenges of applying similar concepts in finance and more particular credit provision. The context of modern banking is substantially different from the above-mentioned use cases. Understanding and shaping federated information systems to cater to its unique features and constraints (key added value, competitive landscape, regulatory frameworks) will help accelerate the adoption of new designs. Towards that purpose we construct a framework that conceptually unbundles the complex operation that is modern credit provision. We introduce a number of fundamental business entities (subunits) and their associated functions and discuss the underlying business models. We discuss, in particular, how and why they exchange data and metrics and the key risk management challenges of each. Finally, we sketch current architectures for credit information sharing with an overture to the new possibilities opening up with federation architectures.

Connecting the Dots: Economic Networks as Property Graphs

We develop a quantitative framework that approaches economic networks from the point of view of contractual relationships between agents (and the interdependencies those generate). The representation of agent properties, transactions and contracts is done in the context of a property graph.

A typical use case for the proposed framework is the study of credit networks.

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. OWL is a computational logic-based language such that knowledge expressed in OWL can be exploited by computer programs, e.g., to verify the consistency of that knowledge or to make implicit knowledge explicit. OWL documents, known as ontologies, can be published in the World Wide Web and may refer to or be referred from other OWL ontologies. OWL is part of the W3C’s Semantic Web technology stack, which includes RDF, RDFS, SPARQL, etc

The motivation for federated credit risk models

Federated learning is a machine learning technique that is receiving increased attention in diverse data driven application domains that have data privacy concerns. The essence of the concept is to train algorithms across decentralized servers, each holding their own local data samples, hence without the need to exchange potentially sensitive information. The construction of a common model is achieved through the exchange of derived data (gradients, parameters, weights etc). This design stands in contrast to traditional model estimation where all data reside (or are brought into one computational environment).

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.

The challenge with historical credit data

Historical credit data are vital for a host of credit portfolio management activities: Starting with assessment of the performance of different types of credits and all the way to the construction of sophisticated credit risk models. Such is the importance of data inputs that for risk models impacting significant decision-making / external reporting there are even prescribed minimum requirements for the type and quality of necessary historical credit data.

Motivation for Building an open source database based on EBA’s Standardized NPL Templates

In an insightful recent piece, “Overcoming non-performing loan market failures with transaction platforms”, Fell et al. dug deeply into the market failures that help perpetuate the Non-performing loan (NPL) problem. They highlight, in particular, information asymmetries and the attendant costs of valuing NPL portfolios as key obstacles. In the same wavelength, the European Banking Authority published standardized NPL data templates as a step towards reducing the obstacles that prevent the reduction of NPL’s.

Data Scientists Have No Future

The working definition of a Data Scientist seems to be in the current overheated environment:

doing whatever it takes to get the job done in a digital #tech domain that we have long neglected but which is now coming back to haunt us!

Machine Learning Ballyhoo

Are you getting a bit tired with all the machine learning ballyhoo?

You can blame it all on a German mathematician(*), Carl Friedrich Gauss, who started the futuristic mega-trend back in 1809: He showed us how to train a straight line to pass nicely through a cloud of unruly, scattered data points. To find, in effect, a path of least embarrassment.

The Zen of IFRS 9 Modeling

At Open Risk we are firm believers in balancing art and science when developing quantitative risk tools. The introduction of the IFRS 9 and CECL accounting frameworks for reporting credit sensitive financial instruments is a massive new worldwide initiative that relies in no small part on quantitative models. The scope and depth of the program in comparison with previous similar efforts (e.g. Basel II) suggests that much can go wrong and it will take considerable time, iterations, communication and training to develop a mature toolkit that is fit-for-purpose.

Guiding principles for a viable open source operational risk model (OSORM)

Such a framework:

1. Must avoid formulaic inclusion of meaningless risk event types (e.g., legal risk created by the firm’s own management decisions) or any risks where the nature and state of current knowledge does not support any meaningful quantification. Such potential risks would be managed outside the framework
2. Must employ a bottom-up design that addresses the risk characteristics of simpler business units first and (if needed) creates a combined profile for a more complex business in a building block fashion.
3. Must avoid formulaic integration between different event risks (that is, positing dependency or correlation) when qualitative and quantitative reasoning does not support it
4. Must cover a comprehensive event risk taxonomy that is well adapted to the nature of the firm
5. Incorporates effectively all relevant organizational information (e.g., Key Risk Indicators or other business line characteristics that are demonstrably generating or affecting operational risk)
6. Involves clear explanatory narratives as to the causes of operational risk (and the drivers of severity). Thus, it links operational risk realizations to concrete attributes of the firm and its environment. This implies preference for fundamental (factor) modeling over agnostic reduced form approaches
7. Is sufficiently concrete and expressive to enable introducing management control parameters. In other words, answering the question: how can we reduce operational risk
8. Must use all relevant empirical historical loss data either at the stage of the model building or at the validation stage
9. The probability distributions employed should reflect the nature of the risk and be valid at the widest possible confidence level range
10. Must incorporate forward looking elements (e.g. via scenarios) that are consistent with the current and past behavior / response of the firm
11. Should allow the documentation any difficult or subtle choices with solid expert reasoning
12. Is the simplest model that is fit for purpose!
13. Last but not least: It is open source, making its advantages and limitations transparent to all stakeholders

Discussion / contributions are welcome.

Reducing variation in credit risk-weighted assets - The benign and vicious cycles of internal risk models

March 2016 wasn’t a good month for so-called internal risk models, the quantitative tools constructed by banks for determining such vital numbers as how much buffer capital is needed to protect the savings of their clients.

Risk Capital for Non-Performing Loans

Currently many countries are drowning in bad credits

This visualization from the World Bank shows the current distribution of non-performing loans (NPL’s in short) around the world, as fraction of the total outstanding loans:

Translated in absolute numbers (according to IMF data) the European NPL book alone stands at around 1 trillion EUR.