Open Risk Data

Recap of Part 1 of the Matrix-to-JSON Post Series

In the first installment of this series, Part 1 we discussed the motivation behind representing and serializing matrices as JSON objects. We defined relevant concepts and in particular the concept of unrolling the matrix into a one-dimensional array and the notion of Column and Row Major orders. We outlined some use cases of interest and initiated a benchmarking exercise that looks into various R and Python JSON serialization utilities (available at the matrix2json repository).

Course Objective

Digging into the meaning of credit data collections, the logic that binds them together towards understanding what they can be used for and what limitations and issues they may be affected by, this new course in the Credit Portfolio Management category explores a new angle to look at an old practice.

Motivation and Objective

Representing a Matrix as a JSON object is a task that appears in many modern data science contexts, in particular when one wants to exchange matrix data online in a portable manner. There is no universally agreed way to achieve this task and various options are available depending on the matrix data characteristics and the programming tools and computational environment one has available.

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.

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 )!

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.

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.

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.

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.

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.

The Promise of Open Risk Data

There is a legend that every time a data set is released into the open, somewhere dies a black swan

Well, it is not a true legend. Legends take centuries of oral storytelling to form. In our frantic age, dominated by the daily news cycle and viral twitter storms, legends have been replaced by the rather more short-lived memes and #hashtags.

From Big Data, to Linked Data and Linked Models

The big data problem:

As certainly as the sun will set today, the big data explosion will lead to a big clean-up mess

How do we know? It is simply a case of history repeating. We only have to study the still smouldering last chapter of banking industry history. Currently banks are portrayed as something akin to the village idiot as far as technology adoption is concerned (and there is certainly a nugget of truth to this). Yet it is also true that banks, in many jurisdictions and across trading styles and business lines, have adopted data driven models already a long time ago. In fact, long enough ago that we have already observed how it call all ended pear shaped, Great Financial Crisis and all.

Open Risk is proud to be funded by the FIWARE FINODEX accelerator!

Finodex, the European accelerator for ICT projects based on Open Data and FIWARE technologies, has already chosen over one hundred projects via two open calls for proposal.

This week the results of the second call evaluation closed in last September have been published, and 52 projects from a total of 297 have been chosen by a panel of experts. These projects will join the other 49 selected in the first open call.

Open Source Risk Data with MongoDB and Python

Open source software is all the rage those days in IT and the concept is making rapid inroads in all parts of the enterprise. An earlier comprehensive survey by Gartner, Inc. found that by 2011 more than half of organizations surveyed had adopted open-source software (OSS) solutions as part of their IT strategy. This percentage may have currently exceeded the 75% mark according to open source advisory firms.

Visualizing the Stress of US Banks

A recurring cycle of regulatory stress testing exercises has become the new normal in the banking world, at least on the two shores of the northern Atlantic. The periodicity of the European stress testing heartbeat has not yet been firmly established. Did we just miss a beat in 2015 (a so called palpitation) or will the European cycle have two (or more) years periodicity? Who knows. Fortunately, there are no such uncertainties around the US stress testing cycle. The US CCAR rhythm seems to be a very robust annual throb and in March we just got the latest iteration.

The rationale for continuing with internal capital models in the Basel 3 world

Overview of the challenges and opportunities offered by internal capital models (economic capital models) in the post-crisis era. Conference Presentation given at:

• Venue: 2nd Annual Capital Modelling under Basel III (Marcus Evans Conference)
• Location: London
• Time: January 28th 2014
• Link to presentation: Local file

A Python module of analytic solutions suitable for testing credit portfolio models has been uploaded in the Open Risk github repository