BoE published a paper that discusses a study on the textual complexity of banking regulations post the financial crisis of 2007-08. The authors have attempted to interpret regulatory complexity in terms of processing complexity by using techniques from natural language processing, or NLP, and network analysis and applying these techniques to the new post-crisis international banking rules. The results of the study suggest that the linguistic complexity in banking regulation is concentrated in a relatively small number of provisions and the post-crisis reforms have accentuated this feature.
The paper covers the ultimate normative question: “How complex does bank regulation have to be?” However, before this, there is another question: “How complex is bank regulation?” This study provides evidence to help answer this question by calculating textual complexity indicators on the near universe of UK prudential rules. The dataset used for this study captures legal sources comprehensively, allows like-for-like comparison between the post- and pre-crisis frameworks, and captures the entire structure of cross-references within the regulatory framework (to facilitate network analysis). The dataset included the near universe of prudential legal obligations and supervisory guidance that applied to UK banks in 2007 and 2017. It captured changes in both the scope of what regulators seek to control and in the legal architecture.
In this paper, the authors define complexity in terms of the processing difficulty encountered when comprehending a particular linguistic unit—for example, a single regulatory provision. Dimensions of processing difficulty for a provision include its length, lexical diversity, use of conditional statements, and the overall readability of its sentences (defined as “local” complexity). Some processing difficulties can only be resolved after accessing information outside the immediate context of the provision—for instance, cross-references or regulatory precedents needed to understand a provision’s intent (“global” complexity). The authors use natural language processing and network analysis techniques to measure these dimensions of local and global complexity and apply these measures to the constructed dataset.
The study found that linguistic complexity in banking regulation is concentrated in a relatively small number of provisions. Starting from the simplest provisions, the measures of complexity increase slowly, but then pick up rapidly as the study approaches the last 10% of most complex provisions. This stylized fact has been accentuated by the post-crisis reforms, which have resulted in the rise of highly complex provisions, in particular a tightly connected core. The authors recognize that more benchmarking for these indicators is a necessary next step toward answering the question on how complex does bank regulation have to be. Benchmarking against non-financial regulatory frameworks, or frameworks in other jurisdictions, is challenging given differences in legal systems and policy substance. However, authors plan to exploit variation within the used dataset to compare changes in complexity measures for different policy standards and test how they correspond to the expectations of policymakers.
The authors stress that these measures do not exhaust all the dimensions of linguistic complexity—in particular, resolving ambiguity in regulation is very likely to be important for information burden. In addition, to understand the economic effect of regulatory complexity “soft” textual information needs to be combined with traditional “hard” numeric data. For example, textual regulatory complexity could be compared to balance sheet complexity. Eventually, natural language processing can help enrich the economic evaluation of rules in terms of the interaction between rules, the impact of linguistic complexity, and the effectiveness of “rules vs standards.” The study contributes to this long-term research agenda, by creating a dataset of all provisions for UK banks and analyzing how they have changed with post-crisis reforms.
Related Link: Staff Working Paper
Keywords: Europe, UK, Banking, NLP, Machine Learning, Machine-Readable Regulations, Artificial Intelligence, Regtech, BoE
Previous ArticleHKMA Revises Guidance on Risk Management of Electronic Banking
The European Commission (EC) published a report summarizing responses to the targeted consultation on the supervisory convergence and the single rulebook in the European Union (EU).
The Office of the Superintendent of Financial Institutions (OSFI) published an update on the discussion paper that intended to engage federally regulated financial institutions and other interested stakeholders in a dialog with OSFI, to proactively enhance and align assurance expectations over key regulatory returns.
The European Central Bank (ECB) published its opinion on a proposal for a regulation on European green bonds, following a request from the European Parliament.
The Advisory Scientific Committee (ASC) of the European Systemic Risk Board (ESRB) published a report that explores the expected impact of digitalization on provision of financial and banking services, and proposes policy measures to address the risks stemming from digitalization.
The European Banking Authority (EBA) announced that the guidelines on the reporting and disclosure of exposures subject to measures COVID-relief measures shall continue to apply until further notice.
The Swedish Financial Supervisory Authority (FI) announced that the capital adequacy reporting as at December 31, 2021 must be done by February 11, 2022.
The Central Bank of the Philippines (BSP) issued communications covering developments related to online lending platforms, open finance framework and roadmap, and on the expected regulations in the area sustainable finance.
The Board of Governors of the Federal Reserve System (FED) published the final rule that amends Regulation I to reduce the quarterly reporting burden for member banks by automating the application process for adjusting their subscriptions to the Federal Reserve Bank capital stock, except in the context of mergers.
The European Banking Authority (EBA) published its assessment of risks through the quarterly Risk Dashboard and the results of the Autumn edition of the Risk Assessment Questionnaire (RAQ).
The Malta Financial Services Authority (MFSA) updated the guidelines on supervisory reporting requirements under the reporting framework 3.0.