In the latest step forward in the field of AI-driven regulatory reporting, Wolters Kluwer has completed a successful Proof of Concept (PoC) showing that a machine can learn how to take over any end-to-end regulatory reporting process for financial institutions and regulators, across every jurisdiction, by using historical source data and its corresponding regulatory submissions.
As global regulators impose ever more rigorous reporting obligations on financial institutions, regulatory reporting has become more onerous, with an increased risk of potential error. Emerging regulations require more prescriptive and highly granular data sets, reported in increasing frequencies. Financial institutions are therefore looking to new technologies, such as ML, to relieve these regulatory reporting burdens.
The latest PoC from Wolters Kluwer found that it is possible to build predictive models with high accuracy and flexibility that complement human judgement and oversight, making it likely that production reporting mechanisms will incorporate Machine Learning (ML) in the near future.
The PoC was trained on two separate end-to-end regulatory reporting processes: the Monetary Authority of Singapore’s MAS 610 and APRA’s Economic and Financial Statistics. After just a few minutes of training, a total of 250,000 records of previously unseen raw data (the ‘internal vocabulary’) containing 260 features (input) and 240 corresponding labels (output) were predicted with very high accuracy – in many cases, the corresponding regulatory reporting output was predicted with >99% accuracy.
“If humans are capable of designing processes which ultimately convert the financial institutions’ raw data into structured regulatory submissions, I see no reason why machines can’t learn to do the same. Our PoC shows that machines can indeed learn to take over any end-to-end regulatory reporting process for any financial institution and any regulator in any jurisdiction,” comments Wouter Delbaere, Director of APAC Regulatory Reporting for Wolters Kluwer FRR. “AI has the potential of disrupting today’s regulatory reporting landscape; rather than taking the traditional approach of explicitly creating deterministic logic, financial institutions can instead adopt machine learning to replace any existing regulatory reporting process with significantly reduced time and effort.”
Last year Wolters Kluwer FRR launched a software-as-a-service (SaaS) Regulatory Reporting solution, and also unveiled a major upgrade to its OneSumX Regulatory Engine.