Summarize this content to 2000 words in 6 paragraphs in Arabic Unlock the Editor\u2019s Digest for freeRoula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.You can tell that people are getting serious when their research has a yellow cover like one of those expensive mathematics textbooks:And the team at the European Banking Authority is indeed getting serious \u2014 it\u2019s just published a distinctly better than workmanlike piece on the possibility of using random forests, neural networks and similar techniques to potentially make a bit of progress towards one of the great dreams of central bank economists. That is to say, the possibility to automate the dreary and unprestigious job of bank supervision, using machines to monitor the data rather than going through supervisory returns yourself.The frustrating thing about this exercise is that it always sort of works. As with indicators of financial stress (another favourite research project), there is some actual pattern and structure in the underlying data, and so the right use of statistical methodology will find it.\u00a0 The EBA researchers actually do quite a bit better than previous efforts. \u201cBreaches of supervisory concern levels on a few key ratios\u201d are their better source of training data points than \u201cactual failures\u201d, meaning they can train a variety of models and use an ensemble approach.Unfortunately, though, models like this are subject to the same fatal flaw as indicators of financial stress, which is that they\u2019re always a finely polished rear-view mirror.\u00a0When financial crises and bank failures actually happen, they tend to happen for reasons that can\u2019t be predicted, because they\u2019re not in the data set, because they\u2019ve never happened before. Or at least, they\u2019ve never happened before in exactly this way, so nobody was collecting the right data on them in the right way.Even more annoyingly, we know it\u2019s going to happen this way again. Financial supervisors keep telling us, with growing anxiety, that one consequence of the last round of banking regulations is that a load of business has moved into the \u201cnon-bank financial institutions\u201d sector, where it isn\u2019t regulated and no data is collected. While one might say \u201cwhat exactly did you think would happen\u201d, they have a point.But wait! The data might be able to help with that too! In the EU, at least, every repo and derivatives transaction has to be reported and is stored in a massive data repository. Some more researchers at the ECB have found a way to link up this trade-by-trade data with the small amount of data they do have on hedge funds, and create something that might kind-of-sort-of serve as a measure of hedge fund leverage.It\u2019s possible to see a picture of the future here. Most financial things that you can do will generate at least some sort of data footprint. If that footprint is in some way legible to the regulators, then they might be able to arrange it into the sort of data set that can be observed to see if patterns of excessive leverage are growing up. And as AI and machine learning systems get better, they are going to get better at rearranging atoms of transactional data into meaningful structures \u2014 that\u2019s what they\u2019re for.So the dream of electronic bank supervision is alive. Unfortunately, making the data footprint available to the supervisors is also what\u2019s known as \u201cregulatory burden\u201d, and everyone is currently trying to minimise it. So human beings will have to do the job for a bit longer.<\/p>\n","protected":false},"excerpt":{"rendered":"
Summarize this content to 2000 words in 6 paragraphs in Arabic Unlock the Editor\u2019s Digest for freeRoula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.You can tell that people are getting serious when their research has a yellow cover like one of those expensive mathematics textbooks:And the team at the<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[63],"tags":[],"class_list":{"0":"post-174297","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-tech"},"_links":{"self":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/posts\/174297","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/comments?post=174297"}],"version-history":[{"count":0,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/posts\/174297\/revisions"}],"wp:attachment":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/media?parent=174297"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/categories?post=174297"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/tags?post=174297"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}