Linked Data

The Open Risk Manual is now available also as an Android App

The Open Risk Manual is an open online repository of information (wiki) about risk management in all its forms. The Manual is developed and maintained by Open Risk. Our objective is to create a comprehensive, detailed, authoritative collection of risk management resources that are easily accessible by anybody, anywhere - well, network access is currently required!

Open Risk White Paper 15: Tensor Representations of ActivityPub Networks

In this third Open Risk White Paper on Connecting the Dots we explore representations of online communication networks that are organized according to the ActivityPub protocol. We discuss the main relevant features of the protocol and the broader application ecosystem around it that shapes emerging online network topologies. We develop a stylized description of ActivityPub compliant networks as a mathematical multilayer network. Tensor representations of such complex graphs generalize the more familiar matrix algebra tools and can be useful in various ways: On the one hand they help empirical work in analyzing the characteristics of such networks, on the other they enable simulating and exploring network behavior.

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 14: Integrated energy accounting using relational databases

In this Open Risk White Paper we demonstrate a concrete implementation of an integrated energy accounting framework using relational database technologies. The framework enables accounting of non-financial disclosures (such as the physical and embodied energy footprints of economic transactions) while enforcing the familiar double-entry balance constraints used to produce conventional (monetary) accounts and financial statements. In addition, it allows enforcing constraints associated with the flow and transformations of energy that can happen inside the organizational perimeter.

Open Risk White Paper 12: Deep-Linking Financial and Energy Accounting

We develop a conceptual framework for integrated accounting that produces (where possible) non-financial disclosures subject to the same double-entry balance constraints as those used to produce conventional financial statements and automatically ensures any additional conservation laws are satisfied. We identify the key ingredients required for such a rigorous integrated accounting framework, in terms of concepts, postulates and design choices. Our focus and concrete use case is built around energy accounting, keeping track on an entity’s detailed energy footprint (primary inputs, transformations and waste generation) as an extension of its standard financial accounting and reporting. The central tool is the use of multidimensional double-entry bookkeeping which tracks quantitative information characterizing economic objects beyond their monetary values. This choice ensures the enforcement of both classic balance constraints and any applicable energy conservation laws. Further tools and techniques concern the aggregation and reporting of dual (monetary and physical) dimensions of an entity’s accounting state. The framework is documented using mathematical notation.

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.).

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

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).

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.

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

Extending the Open Risk API to include the EBA Portfolio Data Templates

The Open Risk API provides a mechanism to integrate arbitrary collections of risk data and risk modelling resources in the context of assessing and managing financial risk. It is based on two key technologies of the modern Web, RESTful architectures and Semantic Data.

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.