Blog

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.

According to wikipedia Conflation is the merging of two or more sets of information, texts, ideas, opinions, etc., into one, often in error. This may lead to misunderstandings, as the fusion of distinct subjects might obscure analysis of relationships which are emphasized by contrasts. Why does conflation happen in the first place? There are several possible factors which in some contexts may be co-existing and overlapping:

Informal Risk Management has been practiced by individuals since time immemorial. This is the domain of intuitive decision-making, assessing a situation on the spot and taking immediate action to avoid obvious risks.

A GSOC 2021 summer project collaboration between Open Risk and the Hydra Ecosystem

Summer is underway and for the Google Summer of Code 2021 season Open Risk is happy to join forces with the Hydra Ecosystem. The project aims to guide students to build a hypermedia enabled REST service around standardized credit portfolio data. More specifically the project will build a REST service as backend for a hypothetical banking entity that collects and disseminates credit portfolio data conforming to an established public standard (the EBA NPL templates, see below).

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!).

Motivation

Equinox is an open source platform that supports holistic risk management and reporting of Sustainable Finance (Sustainable Portfolio Management). The platform integrates geospatial information with applicable regulatory and industry standards from EBA, PCAF and Equator Principles to provide a holistic view of the footprint of both individual projects and portfolios, in particular of project finance investments.

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.).

Data Types are a fundamental building block of data science

Data science is about data, but data are not simple and tame beasts. They have character and attitude, which can cause a lot of friction between them and the data scientist. There is a lot of sweat and tears involved when confronting data, but data scientists can do worse than know how to handle in particular Data Type quirks. Namely, a good fraction of data science involves not modelling data, not transforming data, not even cleaning data but simply goading data around the right containers, providing them with the right stage that fits their character. Getting the data type wrong is many a source of both spectacular bugs and hidden, insidious application errors.

Celebrating Pi Day 2021

Pi Day is celebrated every year on March 14th. The reason of course is that the day is denoted in some calendars as (3/14), which evokes of 3.14, the first three digits of “π”. A thin excuse maybe but sufficient for the true believers to join along! The occasion represents an annual opportunity for mathematics and science enthusiasts to recite the infinite charms of Pi, including its irrationality, to talk to friends and family about math and its uses, and, when everything else fails, simply eat pie.

This CrashCourse is an introduction to semantic data using Python.

Course Content

It covers the following topics:

• We learn to work with RDF graphs using rdflib
• We explore the owlready package and OWL ontologies
• We look into json-ld serialization of RDF/OWL data
• We try data validation using pySHACL
• We use throughout a realistic data set based on the Credit Ratings Ontology

What is the future of stress testing?

To speculate on the future of Stress Testing we need first a basic definition what stress testing is. Broadly speaking, the goal of Stress Testing is to assess how a system would behave under adverse conditions that - while not the most likely outcome with the knowledge of today - are within the realm of the plausible.

Visualizing a year in lockdowns and restricted mobility

As we move into February 2021 the world will be experiencing almost a year under pandemic conditions. This has markedly changed behavioral patterns of human mobility across the board. One major difference with previous pandemics is that through the use of a variety of digital technologies and new data collection channels we know have an unprecedented view of those changing mobility patterns.

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).

Introduction

What is FOSDEM?

FOSDEM is a non-commercial, volunteer-organized event centered on free and open-source software development (with a geographic focus on the European open source ecosystems / projects). FOSDEM is aimed at developers and anyone interested in the free and open-source software movement. It aims to enable developers to meet and to promote the awareness and use of free and open-source software.

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”.

Constructing a Global Mobility Index (GMI)

In previous posts (here, and here) we introduced new Open Risk Dashboard functionalities that integrate COVID-19 community mobility data (currently focusing on the datasets provided by Google).

As a reminder, these reports chart over time human mobility trends collected from mobile geolocation data. The granularity is by geography and across different categories of places / activities such as retail and recreation areas, groceries and pharmacies, parks, transit stations, workplaces, and residential areas. Through these data sets we have available (for the first time in history) an overall quantitative view of global mobility (and Mobility Risk )!

Is the size of global debt truly “astronomical”?

The notion of astronomical numbers and figures is quite frequently seeping in everyday language when large quantities of something are encountered in “normal” life. The strict definition of astronomical is obviously something of, or relating to, astronomy and astronomical observations but in common usage it also denotes something enormously or inconceivably large. This is, of course, because astronomical figures are inconceivably large!

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. See also live version and the white paper OpenRiskWP04_061415.

The role of simulation in risk management and decision support

A Simulation is a simplified imitation of a process or system that represents with some fidelity its operation over time. In the context of risk management and decision support simulation can be a very powerful tool as it allows us to assess potential outcomes in a systematic way and explore what-if questions in ways that might otherwise be not feasible. Simulation is used when the underlying model is too complex to yield explicit analytic models (An analytic model is one can be “solved” exactly or with standard numerical methods, for example resulting in a formula).