Prior to the 2008 financial crash, the financial services sector had been regulated with a somewhat ‘light-touch’ approach. After the crash, things changed dramatically. Determined to ensure a similar financial meltdown would not occur again, central banks and supervisory authorities started tightening up regulations.
This has resulted in an ever-increasing volume of data and created a challenge around managing that data volume but also data complexity and data frequency for financial regulators. These continue to rise exponentially as tighter and more frequent regulations mean that more data has to be collected, processed, interpreted, managed and stored.
Increased manpower has been used as part of the solution, but, while data metrics continue to grow, central banks can’t continue hiring people to manage it manually. Technology holds the key to managing these data challenges for central banks.
Granular data enables a digital lingua franca for financial supervision. Granular data refers to data that is broken down into the finest, most-detailed level that is practical to use. Breaking the data down to its smallest components is called disaggregation.
With granular data, it is easier to understand the nature of the request since the language is broken down into its simplest components. The scope for misinterpretation is minimised and reporting is more accurate which reduces the need for resubmissions. Over time, granular data can significantly reduce costs.
In the past, using AI and machine learning in financial supervision meant mathematics experts in labs using supercomputers to process data. Thanks to technological developments, today the benefits of machine learning are available to financial regulators worldwide. That means computers can process the data while regulators focus on making important supervisory decisions.
There has been a surge in industry confidence concerning AI and its subfield machine learning. It is now a commodity as opposed to an out of reach concept. These technologies help regulators to identify trends in large data sets and make predictions based on these trends. As AI techniques have become more mainstream, their usability and accessibility have improved.
Machine-to-machine reporting can allow data to be processed, validated, and analysed without human interaction. It is faster and more accurate than humans, and it frees supervisors’ time up.
With M2M, humans no longer need to do the cumbersome and distracting work of scrubbing large amounts of data and moving it around. Once a machine-to-machine reporting solution has been delivered and effectively tested, it does not make mistakes no matter how much data it processes.
In April 2019, the Vizor SupTech Platform was selected by the Australian Prudential Regulation Authority (APRA) as the software platform of choice to replace its Direct to APRA (D2A) solution.
The Vizor APRA Connect software is the first solution to automatically synchronise with all APRA data models and rules. This eliminates complex system upgrades and reduces the cost and impact of future regulatory changes for superannuation.
The key benefits include automatic system updates in sync with APRA Connect templates and rules, pre-validation of data to guarantee “right-first-time” submission and easy integration with existing systems using RESTful APIs.
The system is cost-effective – it eliminates manual effort by automatically converting your Excel data to an APRA-ready submission file. It is also fast & scalable. It is possible to get started in as little as four weeks in our secure, enterprise Cloud and you only pay for what you need.
The Covid-19 pandemic has forced businesses, financial institutions, and regulators worldwide to accelerate their investment in digital transformation.
The challenge of data complexity for regulators is not one that is going to go away, so technology is slated to play a pivotal role in the future of financial regulation. The result will be a faster, more accurate system where regulators can spend more time focused on supervision, while technology looks after system and data management.
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