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    Academy News

    Mathematical Representations of Credit Portfolio Data (New Blog Post)

    by Ad Min -
    Picture of Editing Authors

    What do we mean by credit data? For our purposes Credit Data is any well-defined dataset that has direct applications in the assessment of the Credit Risk of an individual or an organization, or, more generally, a dataset that allows the application of data driven Credit Portfolio Management policies.

    The appearance of credit data is quite familiar to practitioners: A spreadsheet, or a table in a database, with a number of columns and rows full of all sorts of information about borrowers and loans. Digging into the meaning of these data collections, the logic that binds them together, is essential for understanding what they can be used for and what limitations and issues they may be affected by.

    This new blog post explores a new angle to look at an old practice. Enjoy!

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    The second part of the Mortgage Data Processing series is now online

    by Ad Min -
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    Course (PYT37066) is now available on the Academy for all users


    An illustration of various stages of data processing


    Course Content:

    This crash course illustrates how to process loan-level US Agency mortgage data using awk, pandas and django. The second part of the course focuses on the performing book. This part covers the following topics:

    • Concepts of the Credit Life Cycle and how changing states are captured in Loan Data as Dynamic (Variable) Fields with a focus on performing loans (excluding delinquent loans)
    • Selecting performing loan data using awk and pandas, manipulating and exporting derived data models using pandas
    • Data Quality Concepts for Performing Loans and Concepts from Bitemporal Databases
    • Importing performing book credit data models into a django based web platform (openNPL) that enables further interactive work with such data

    Nota Bene: The course requires actual historical loan performance data for its proper completion. Those data are not provided within the course. Students must source such data themselves from the Data Dynamics website and agree to be in compliance with the applicable terms and conditions.

    Who Is This Course For:

    The course is useful to:

    • Data Engineers / Data Scientists across the financial industry and beyond that need to work with mortgage data
    • Credit Risk Management professionals and students
    • Credit Portfolio Management professionals

    How Does The Course Help:

    Mastering the course content provides background knowledge towards the following activities:

    • Improved ability to process large loan-level historical performance data
    • Pre-process, categorize, segment and improve on such data sets in preparation for further analysis

    What Will You Get From The Course:

    • You will be able to confidently work with Loan-level historical performance data
    • You will be able to contribute to the specific use cases mentioned above

    Course Level and Difficulty Level:

    This course is part of the Risk Modeling using Python family.

    • This is a Core Level course in Risk Modelling. A good grounding at Introductory level to various Data Engineering and Data Science topics is a prerequisite for making the most out of this course.
    • This is a Technical course which means certain technology elements (Python, CLI) are needed for mastering the material.

    If you have not taken an Open Risk Academy course before the "CrashCourse Academy Demo" provides a quick overview of the Academy.

    The following table places the course in the Open Risk Academy skills diagram:

    Course Level & Type
    Introductory Level Core Level Advanced Level
    Non-technical
    Technical CrashProgram
    PYT37066

    Course Material:

    The course material comprises the following:

    Time Requirements and Important Dates

    • The course is self-paced and can be undertaken at any point. It requires a commitment of about five hours total, depending on student familiarity and existing development environment.
    • It is advisable to pursue this course after completing the first part of the series

    Where To Get Help:

    If you get stuck on any issue with the course or the Academy:

    • If the issue is related to the course topics / material, check in the first instance the Course Forum
    • If the issue is related the operation of the Open Risk Academy check first the Academy FAQ. If the issue persists contact us at info@openrisk.eu

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    New Course: Processing (US) Agency Mortgage Data Using Awk, Pandas (Part 1)

    by Ad Min -
    Picture of Editing Authors

    A new crash course (PYT26065) is now available on the Academy for all users


    An illustration of various stages of data processing


    Course Content:

    This crash course illustrates how to process loan-level US Agency mortgage data using awk, pandas and django. The first part of the course focuses on static (acquisition) type attributes. This part covers the following topics:

    • Downloading and preprocessing historical loan-performance datasets using awk
    • Processing loan-performance datasets using pandas
    • Working and fixing any missing data type issues
    • Classifying data attributes according to a Credit Data Taxonomy
    • Manipulating and exporting derived data models using pandas
    • The Split-Apply-Combine process
    • Importing data models into a django based web platform (openNPL) that enables interactive work with the data

    Nota Bene: The course requires actual historical loan performance data for its proper completion but those data are not provided. Students must source such data themselves from the Data Dynamics website and agree to be in compliance with the applicable terms and conditions.

    Who Is This Course For:

    The course is useful to:

    • Data Engineers / Data Scientists across the financial industry and beyond that need to work with mortgage data
    • Credit Risk Management professionals and students
    • Credit Portfolio Management professionals

    How Does The Course Help:

    Mastering the course content provides background knowledge towards the following activities:

    • Improved ability to process large loan-level historical performance data
    • Pre-process, categorize, segment and improve on such data sets in preparation for further analysis

    What Will You Get From The Course:

    • You will be able to confidently work with Loan-level historical performance data
    • You will be able to contribute to the specific use cases mentioned above

    Course Level and Difficulty Level:

    This course is part of the Risk Modeling using Python family.

    • This is a Core Level course in Risk Modelling. A good grounding at Introductory level to various Data Engineering and Datea Science topics is a prerequisite for making the most out of this course.
    • This is a Technical course which means certain technology elements (Python, CLI) are needed for mastering the material.

    If you have not taken an Open Risk Academy course before the "CrashCourse Academy Demo" provides a quick overview of the Academy.

    The following table places the course in the Open Risk Academy skills diagram:

    Course Level & Type
    Introductory Level Core Level Advanced Level
    Non-technical
    Technical CrashProgram
    PYT26065

    Course Material:

    The course material comprises the following:

    Time Requirements and Important Dates

    • The course is self-paced and can be undertaken at any point. It requires a commitment of about five hours total, depending on student familiarity and existing development environment.

    Where To Get Help:

    If you get stuck on any issue with the course or the Academy:

    • If the issue is related to the course topics / material, check in the first instance the Course Forum
    • If the issue is related the operation of the Open Risk Academy check first the Academy FAQ. If the issue persists contact us at info@openrisk.eu
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    Latest version of openNPL starts support for Fannie Mae Loan Performance Data

    by Ad Min -
    Picture of Editing Authors

    The openNPL credit portfolio management platform implements and builds on the detailed European Banking Authority loan templates for NPL data. It thereby enables the collection and easy management of non-performing loan data according to best-practices.

    In the the latest update (0.5.3) the platform is expanded to enable working also with US Agency (Fannie Mae) Mortgage Loan Performance data. This functionality is still under development and will mature with the upcoming 0.6 release of openNPL (along with other related resources) so stay tuned! The updated code documentation is available here. The corresponding data dictionaries and concepts are documented with a new category at the Open Risk Manual.

    OpenNPL snapshot


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    Another year in permacrisis, yet the future is in our hands

    by Ad Min -
    Picture of Editing Authors

    Dear users and friends of the Open Risk Academy, it is this time of the year again... Where the, all too-visible, signs of fragility and failure in how we collectively organize our affairs and communities across the globe must be internalized and transformed into positive learnings towards a more resilient future.

    Not an easy task: our infatuations, lazy habits, poor information access and poor decisions that make no rational or emotional sense time and again take the upper hand and create exhaustion and a sense of impossibility. Yet there is no option but persist with an agenda that empowers invididuals and organizations to enhance welfare at all levels. After all, everything positive that has been achieved was due to somebody acting.

    At Open Risk we will continue to try contibute our small effort towards the open future of risk management. We wish you too, a regenerative break this holiday season and a healthy and productive 2023.

    new years 2023 card by Open Risk

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