Credit Portfolio Management

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.

Summary

This course is a Crash Program introduction to the topic of credit risk concentrations: how to identify, measure and manage excess credit exposure, an essential risk management discipline for any credit portfolio.

Content

The course covers the following topics:

Summary

This course is a CrashProgram in the use of python for credit portfolio modelling purposes, in particular working with data templates and spreadsheets.

Content

The course covers the following topics:

Summary

This course is a CrashProgram (short course) in the use of Python and the package TransitionMatrix for analysing credit migration data.

Requirements

The course is at a medium technical level. It requires some familiarity with python (and a working installation that includes the common numpy/scipy libraries). On the risk modelling side it requires knowledge of basic credit rating migration concepts.

Summary

This course is a CrashProgram in the use of the MongoDB database in conjunction with Python for credit portfolio management purposes.

Content

The course covers the following topics:

Summary

This course is a CrashProgram (short course) introducing the concept of a structured review of risk data.

Outcomes

Step by step we build the knowledge required to review the suitability of data for a given purpose and how to report the findings.

Summary

The Open Risk Academy course NPL270672 is a CrashCourse introducing the EBA NPL Templates.

Content

We start with the motivation for the templates and the domain of credit data (to which NPL data belongs). We discuss three core classes that capture the essence of lending operations from a lenders point of view (Counterparty, Loan, Collateral). Next we explore classes that capture events in the lending relationship lifecycle (which we term NPL Scenarios). We look into the main data types: elementary data types, choice lists, arrays and unstructured text. We close with discussing some more complex issues involving graph and timeseries data.