New Releases

An introduction to Semantic Python

An introduction to Semantic Python

A CrashCourse introduction to semantic data using Python covering a number of frameworks such as rdflib, owlready and pySHACL

Reading Time: 2 min.
Course Content: This CrashCourse is an introduction to semantic data using Python. 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 Who Is This Course For: The course is useful to:
Non-Performing Loan Ontology

Non-Performing Loan Ontology

The NPL Ontology (NPLO) is a new ontology describing datasets of Non-Perfoming Loan Portfolios.

Reading Time: 4 min.
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).
A Global Mobility Index

A Global Mobility Index

We introduce a global mobility index that averages Google mobility data across all available countries (weighting by population) to provide an overall view of how the pandemic has influenced human mobility

Reading Time: 6 min.
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.
Risk Function Ontology

Risk Function Ontology

The Risk Function Ontology (RFO) is a new ontology describing risk management roles (posts) and functions.

Reading Time: 3 min.
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 white paper (OpenRiskWP04_061415)
Logarithmic Sankey Visualization of Credit Migrations

Logarithmic Sankey Visualization of Credit Migrations

Sankey diagrams are very useful for the visualization of flows, especially when there is a conserved quantity. They can be tricky when some of the flows are much smaller than others. In the latest release of transitionMatrix we include an example of a log-scale version of Sankey

Reading Time: 5 min.
Using Sankey Diagrams: Sankey Diagrams are a type of flow diagram composed of interconnected arrows. The width of the arrows is proportional to the flow rate. Sankey diagrams are often used in physical sciences (physics, chemistry, biology) and engineering but also in economics. They can be used to represent the relative role and significance of various inputs and outputs in a given process. Sankey diagrams emphasize the major transfers within a system.
openNPL 0.2 REST API implementation

openNPL 0.2 REST API implementation

The 0.2 release of openNPL exposes a RESTful API that provides easy standardized online access to NPL credit portfolio data conforming to the EBA NPL templates

Reading Time: 4 min.
openNPL 0.2 release: The open source openNPL platform supports the management of standardized credit portfolio data for non-performing loans. In this respect it implements the detailed European Banking Authority NPL loan templates. It aims to be at the same time easy to integrate in human workflows (using a familiar web interface) and integrate into automated (computer driven) workflows. The latest (0.2) release exposes a REST API that offers machine oriented access using, what is by now, the most established mechanism for achieving flexible online data transfers.
Back to School With the Open Risk Academy

Back to School With the Open Risk Academy

In the Back-to-School for 2020 we have more ways to access the Academy, new functionalities and more courses. In the rest of this post you will find a summary of the changes with pointers to further information where required

Reading Time: 4 min.
Risk Management will not be the same going forward: too much is at stake! The summer is over in the Northern Hemisphere - and what an unusual summer has it been! Worldwide the implications and challenges of adjusting to a Covid-19 pandemic are still a major issue, affecting individuals, companies and governments. At Open Risk we have been tracking and will continue to interpret the impact of the pandemic via a number of projects:
openNPL now Available in Dockerized Form

openNPL now Available in Dockerized Form

Open Source, cloud based management of Non-Performing Loan data following the European Banking Authority's templates with just a few keystrokes!

Reading Time: 1 min.
openNPL now Available in Dockerized Form: Following up on the first release of openNPL the platform is now available to install using Docker. Running openNPL via docker is the installation option that simplifies the manual process (but a working docker installation is required!). Docker Hub You can pull the latest openNPL image from Docker Hub (This method is recommended if you do not want to mess with the source distribution).
openNPL: Open Source NPL Platform - First Release

openNPL: Open Source NPL Platform - First Release

We introduce an open source platform that allows the easy management of non-performing loan data

Reading Time: 4 min.
Non-Performing Loans: The covid-19 crisis will certainly impact the concentration of Non-Performing Loans but given the special nature of this economic crisis compared (in particular) with the 2008 financial crisis it is unclear how precisely things will evolve. In a previous post and white paper (OpenRiskWP07_022616) we discussed the importance of advancing open and transparent methodologies for managing the risks associated with such credit portfolios. Effective management of NPL is also a top regulatory priority.
Comparing Google Community Mobility Reports Across Countries

Comparing Google Community Mobility Reports Across Countries

Reading Time: 5 min.
The community mobility reports and OpenCPM: In a previous post we introduced new OpenCPM functionality that integrates COVID-19 community mobility data (currently from Google). The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. While these reports are unlikely to persist as open data sources in the long term, the current availability (as of May 2020) enables providing within OpenCPM a mobility data dashboard that can help draw insights through visualization and statistical analysis.
Exploring Community Mobility Reports Using OpenCPM

Exploring Community Mobility Reports Using OpenCPM

Reading Time: 7 min.
The community mobility reports and OpenCPM: As the COVID-19 pandemic unfolded technology providers (most notably Google and Apple) made available to the public aggregated and anonymized data about human mobility in the crisis period (on the basis of smartphone location data). These Community Mobility Reports provide insights into how mobility patterns changed in response both to pandemic news and policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of locations and activities, such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.
New Open Risk Academy Course: Introduction to Geojson

New Open Risk Academy Course: Introduction to Geojson

Reading Time: 2 min.
Course Content: This course is a CrashProgram (short course) introducing the GeoJSON specification for the encoding of geospatial features. The course is at an introductory technical level. It requires some familiarity with data specifications such as JSON and a very basic knowledge of Python Who Is This Course For: The course is useful to: Any developer or data scientist that wants to work with geospatial features encoded in the geojson format How Does The Course Help: Mastering the course content provides background knowledge towards the following activities:
New Open Risk Academy Course: Simulation of Credit Contagion

New Open Risk Academy Course: Simulation of Credit Contagion

Reading Time: 2 min.
Course Content: This course is an introduction to the concept of credit contagion. It covers the following topics: Contagion Risk Overview and Definition Various Contagion Types and Modelling Challenges The Simple Contagion Model by Davis and Lo Supply Chains Contagion Sovereign Contagion Who Is This Course For: The course is useful to: Risk Analysts across the financial industry and beyond Risk Management students Quantitative Risk Managers developing or validating risk models How Does The Course Help: Mastering the course content provides background knowledge towards the following activities:
Making Open Risk Data easier

Making Open Risk Data easier

We introduce an online database that allows the (relatively) easy publication of structured risk data

Reading Time: 1 min.
Making Open Risk Data easier: In an earlier blog post we discussed the promise of Open Risk Data and how the widespread availability of good information that is relevant for risk management can substantially help mitigate diverse risks. The list of Open Risk Data providers, particularly from public sector, keeps increasing and we are aiming to document all available datasets in the dedicated page of the Open Risk Manual.
Risk Model Ontology

Risk Model Ontology

Reading Time: 2 min.
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.
Federated Credit Risk Models

Federated Credit Risk Models

Reading Time: 4 min.
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).
Data Quality and Exploratory Data Analysis using Python

Data Quality and Exploratory Data Analysis using Python

Reading Time: 0 min.
Data Quality and Exploratory Data Analysis using Python: In two new Open Risk Academy courses we figure step by step how to use python to work to review risk data from a data quality perspective and how to perform exploratory data analysis with pandas, seaborn and statsmodels: Introduction to Risk Data Review Exploratory Data Analysis using Pandas, Seaborn and Statsmodels
Open Source Securitisation

Open Source Securitisation

Reading Time: 5 min.
Open Source Securitisation: Motivation After the Great Financial Crisis securitisation has become the poster child of a financial product exhibiting complexity and opaqueness. The issues and lessons learned post-crisis were many, involving all aspects of the securitisation process, from the nature and quality of the underlying assets, the incentives of the various agents involved and the ability of investors to analyze the products they invested in. While the most egregious complications involved various types of re-securitisation and/or the interplay of structured credit derivatives undoubtedly even vanilla securitisation structure has a considerable amount of business logic.
Visualization of large scale economic data sets

Visualization of large scale economic data sets

Reading Time: 3 min.
Visualization of large scale economic data sets: Economic data are increasingly being aggregated and disseminated by Statistics Agencies and Central Banks using modern API’s (application programming interfaces) which enable unprecedented accessibility to wider audiences. In turn the availability of relevant information enables more informed decision making by a variety of actors in both public and private sectors. An excellent example of such a modern facility is the European Central Bank’s Statistical Data Warehouse (SDW), an online economic data repository that provides features to access, find, compare, download and share the ECB’s published statistical information.
ESMA Securitisation Templates are now documented at the Open Risk Manual

ESMA Securitisation Templates are now documented at the Open Risk Manual

Reading Time: 1 min.
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.