Finreg-E / Natural Language Processing for Financial Regulation
Lead Participant:
FINREG-E LIMITED
Abstract
FinregE is a trusted RegTech SME, with technical expertise enabling at least 80% time and cost savings through software for managing compliance with financial services laws and regulations. In collaboration with Imperial College London, FinregE are looking to deliver their next-generation platform in providing regulatory ease to global financial institutions (FIs), as well as sharing their ideas with governments/regulators to help their strategies to improve regulatory management and supervision.
Financial sector regulation has grown significantly in scale and complexity since the financial crisis. Regulatory compliance is key to organisations, underpinning an organisation's duties to stakeholders/customers, helping build trust and reduce unforced errors.
Non-compliance has had devastating commercial consequences; ~£243bn fines have been imposed on FIs since 2009 \[Reuters, 2017\]. In most FIs, regulatory compliance processes are too manually-demanding and time-consuming, with FIs struggling to achieve compliance with global regulations growing more complex by the day.
The accepted opinion among FIs/regulators is that the application of machine learning/natural language processing (ML/NLP) technologies to Regulatory/Legal text could be straightforward and imminent. However, standard ML/NLP/Deep-Learning used to analyse text documents perform poorly in the legal/regulatory domain and are yet to be successfully achieved. NLP research-to-date has focussed on user reviews, where the language is colloquial/informal, and of shorter length than laws/regulations.
With this innovation, FinregE will deliver a one-of-a-kind ML/NLP driven regulatory compliance software that quickens and improves the accuracy of regulatory compliance, making the most effective use of technology yet in this domain. FinregE's platform will provide following solutions:
1. Continuous alerts on emerging regulations/laws from multiple sources;
2. Automated classification/organisation of regulations/laws to identify the full network view of regulation/law in a topic classes/domains;
3. Dynamic extraction of summaries of the most pertinent information requiring compliance actions;
4. The ability to map extracted information against entity's internal policies/procedures/controls to automatically alert owners and identify actions required for compliance.
With FinregE's intuitive and innovative compliance software FIs and regulators will be able to determine impact and plan adoption of regulation quickly. FIs will be able to effectively triage every publication/rule to extract key information requiring compliance actions, processing and implementation regulations at least 90% faster than manual processing. The project will deliver a software and framework for a universal set of regulatory topics with defined sets of rules which will keep evolving as societal change creates new areas of law across different topics and global jurisdictions. No such enterprise-wide solution currently exists.
Financial sector regulation has grown significantly in scale and complexity since the financial crisis. Regulatory compliance is key to organisations, underpinning an organisation's duties to stakeholders/customers, helping build trust and reduce unforced errors.
Non-compliance has had devastating commercial consequences; ~£243bn fines have been imposed on FIs since 2009 \[Reuters, 2017\]. In most FIs, regulatory compliance processes are too manually-demanding and time-consuming, with FIs struggling to achieve compliance with global regulations growing more complex by the day.
The accepted opinion among FIs/regulators is that the application of machine learning/natural language processing (ML/NLP) technologies to Regulatory/Legal text could be straightforward and imminent. However, standard ML/NLP/Deep-Learning used to analyse text documents perform poorly in the legal/regulatory domain and are yet to be successfully achieved. NLP research-to-date has focussed on user reviews, where the language is colloquial/informal, and of shorter length than laws/regulations.
With this innovation, FinregE will deliver a one-of-a-kind ML/NLP driven regulatory compliance software that quickens and improves the accuracy of regulatory compliance, making the most effective use of technology yet in this domain. FinregE's platform will provide following solutions:
1. Continuous alerts on emerging regulations/laws from multiple sources;
2. Automated classification/organisation of regulations/laws to identify the full network view of regulation/law in a topic classes/domains;
3. Dynamic extraction of summaries of the most pertinent information requiring compliance actions;
4. The ability to map extracted information against entity's internal policies/procedures/controls to automatically alert owners and identify actions required for compliance.
With FinregE's intuitive and innovative compliance software FIs and regulators will be able to determine impact and plan adoption of regulation quickly. FIs will be able to effectively triage every publication/rule to extract key information requiring compliance actions, processing and implementation regulations at least 90% faster than manual processing. The project will deliver a software and framework for a universal set of regulatory topics with defined sets of rules which will keep evolving as societal change creates new areas of law across different topics and global jurisdictions. No such enterprise-wide solution currently exists.
Lead Participant | Project Cost | Grant Offer |
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Participant |
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FINREG-E LIMITED |
People |
ORCID iD |
Rohini Gupta (Project Manager) |