Risk Data

Summary

In this short course we explore how some programming languages, data formats, database API’s and web frameworks handle hierarchical classes.

Content

Object-oriented programming and techniques (OOP) such as using classes and inheritance are common in many application programming environments but alas don’t “travel well” outside computer memory. The potentially intricate relationships of objects (both the data they hold and the meaning and possible uses of the data) are not easy to transfer (except of-course by full replication of code and data). Hence when considering data science tasks and objectives that involving exchange of data, the transition from object hierarchies that live inside memory, to data structures that can be exchanged with another computer is not straightforward.

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:

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.

The trailblazing Wikidata project

In this post we want to introduce another facility, an online database that allows the (relatively) easy publication of structured risk data. The project is inspired by the visionary and already quite succesful deployment of wikibase software (an extension to Mediawiki)

Open Risk White Paper 8: 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.

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.

Accounting probably would not count among the more glamorous of professions. The reasons for that status and whether it is justified are beyond the scope of this brief commentary.

What is interesting to note, though, is that the relative attractiveness of accounting is arguably improving, driven by a number of systemic societal developments:

• the need for more proactive assessment of the state of the world, eliminating the infamous “rear-view mirror” pathology. A prime example is the introduction of IFRS 9. A key feature of IFRS 9 is that it requires a forward looking view to be embedded in reported accounts. This is a remarkable (and controversial) break with prior principles which generally shun potentially “subjective” elements (but in practice hide them deep inside the meaning of various reported numbers)
• the need for more inclusive assessment of the state of the world, eliminating blind spots and under-reported externalities that have material impact also on the economic state of an entity. A good example is the adoption of Sustainable Finance which necessitates disclosure of sustainability profiles. This too, is a break with historical practices that focused exclusively on economic / monetary figures.
• the need for more democratized assessemnt of the state of the world, removing friction and improving transparency and accountability. This area is not yet fully developed but a number of developments around automated reporting (XBRL) and technologies for decentralized account keeping (aka blockchain) promise to eventually reshape the profile of accounting for good

None of the above is without growth problems. After all the history of accounting itself is a thousands years history or continuously evolving technologies and practices, reflecting the values of the societies of each era.

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.

What Inka quipus teach us about data management

Chances are that your knowledge of ancient Peruvian culture is a bit rusty. Maybe you have some vague high-school memories of an extensive but backward empire that was conquered and then asset-stripped by a handful of Spanish conquistadores. Or maybe your best preserved memory is the excitement of reading von Daniken’s speculations that the Nazca lines are extraterrestrial spaceports. But unless you happened at some point later in life to hear about the work of Prof. Urton or his collaborators, most likely you have no idea what a quipu is (see image above).

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.