Open Risk White Paper 13: Federated Credit Systems, Part II: Techniques for Federated Data Analysis In this Open Risk White Paper, the second of series focusing on Federated Credit Systems, we explore techniques for federated credit data analysis. Building on the first paper where we outlined the overall architecture, essential actors and information flows underlying various business models of credit provision, in this step we focus on the enabling arrangements and techniques for building Federated Credit Data Systems and enabling Federated Analysis.
Open Risk White Paper 9: Federated Credit Systems, Part I: Unbundling The Credit Provision Business Model In this (the first of series of three) white paper, we introduce and explore the concept of federated credit systems. We review the rapidly developing fields of Federated Analysis and Federated Learning as already actively studied in the domains of medicine and consumer computing devices. This forms the backdrop for understanding the potential and challenges of applying similar concepts in finance and more particular credit provision.
The motivation for federated credit risk models: Federated learning is a machine learning technique that is receiving increased attention in diverse data driven application domains that have data privacy concerns. The essence of the concept is to train algorithms across decentralized servers, each holding their own local data samples, hence without the need to exchange potentially sensitive information. The construction of a common model is achieved through the exchange of derived data (gradients, parameters, weights etc).
Overview of the Julia-Python-R Universe: A new Open Risk Manual entry offers a side-by-side review of the main open source ecosystems supporting the Data Science domain: Julia, Python, R, sometimes abbreviated as Jupyter.
Motivation A large component of Quantitative Risk Management relies on data processing and quantitative tools (aka Data Science ). In recent years open source software targeting Data Science finds increased adoption in diverse applications. The overview of the Julia-Python-R Universe article is a side by side comparison of a wide range of aspects of Python, Julia and R language ecosystems.
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.
Data Scientists Have No Future: The working definition of a Data Scientist seems to be in the current overheated environment:
doing whatever it takes to get the job done in a digital #tech domain that we have long neglected but which is now coming back to haunt us!
That is nice urgency while it lasts, but it is not a serious job description for the future.
You will always find entrepreneurial institutions to offer degrees and certifications on the latest trending hashtag.
Machine Learning Ballyhoo: Are you getting a bit tired with all the machine learning ballyhoo?
You can blame it all on a German mathematician(*), Carl Friedrich Gauss, who started the futuristic mega-trend back in 1809: He showed us how to train a straight line to pass nicely through a cloud of unruly, scattered data points. To find, in effect, a path of least embarrassment.
Two+ centuries later it is still a profitable enterprise to invent elaborate variations of that theme, now going under the more exalted name of supervised learning, which may or may not include deep learning.
If programming languages were human languages which one would be which? Most developers know (or get to know quickly once they join a team) that programming languages are as much about communicating with other developers as they are about instructing the computer. Which raises the interesting question: If programming languages were human languages which one would be which? Here is a (tonque-in-cheek mind you!) compilation of a mapping between programming languages and human languages.
Job Specification for an Artificially Intelligent Banker: The Artificially Intelligent Banker is responsible for the overall management of the AI2H (AI to Human) lending department. The following requirements (job specifications) were determined by extensive data mining analysis and derived from the job description as crucial for success in the Artificially Intelligent Banker role.
The successful candidate for the Artificially Intelligent Banker position will possess the following qualifications:
Experience Evidence of 8-12 months of continuous uptime without rebooting Progressively more responsible positions in human interface roles, preferably in a similar industry in two different decentralized autonomous firms Indicatively human sales interaction experience with least 10 mln Human subjects is required.