Data Quality

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”.

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:

Machine learning approaches to synthetic credit data

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 Atlas of Bad Risk Management

The Atlas was discovered recently in archaeological work studying pre-crisis civilizations. Despite the obvious wear and tear, all key risk failure areas have been preserved. We note the remarkable diversity of organizational forms and economic structures. Most interestingly, there is even an uncharted territory that was rumored to be inhabited by black swans.

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.