by Ajay Katara
Basel III end game is the latest update to the US Capital requirements which will bring about sweeping changes to the existing capital requirements in place for the US banks. Under the existing provisions there are two approaches which apply to US banks. The standardised approach applies to all banking organisations (other than community banks) and advanced approaches apply to category 1 and category 2 US banks, and they must also compute Risk Weighted Assets (RWA) under the standardised too. The US regulators have proposed July 2025 for compliance with new requirements with a three-year transition period ending in Jun 2028. The new regulation will bring about a lot of changes and will fundamentally apply to banks with asset sizes greater than USD 100 Mn with specific changes applying to banks designated between category 1 to category IV.
A high-level synopsis of key changes is mentioned below:
- Dual capital calculation for large banks under both standardised and expanded risk approaches, with the higher of two being considered for minimum capital requirements.
- Application of Market Risk provisions to banks with significant trading activity.
- Globally systemically important banks will experience a significant increase in capital.
- Less reliance on internal proprietary models and leverage standardised approaches for calculating certain risk metrics for category I and category II banks.
- Accumulated other comprehensive income (AOCI) changes for Category III and IV firms.
- Expanded Application of Supplementary Leverage Ratio and Countercyclical Capital Buffer Requirement for category III and category IV banks.
Given the quantum of changes, it is no surprise that regulators have provided two years to implement the new rules and assess the impact on their existing processes, data, and technology. This also provides them with an opportunity to update their existing tech stack and become more nimble and agile towards this mammoth compliance.
Generative Artificial Intelligence or Gen AI as it is popularly called has recently emerged as a technology enabler which is seeing lot of user cases across banking industries as well with its ability to generate content in the form of text, images, and media by learning from the training data set and generating new data with similar characteristics.
When banks are complying to newer regulations and because business requirements are the backbone of all technology implementations, Basel III end game too will require documentation of business requirements, which is typically a very manual process, and given the nuances involved in the regulation, it can be too time consuming to interpret and elicit requirements. While regulators are very careful about the usage of Gen AI technologies for regulatory purposes, they can still be leveraged for initial data gather which can be subsequently reviewed by the business stakeholders thereby reducing the cycle time for producing the business requirements and freeing up their time for more strategic activities like review and collaboration.
Mentioned below are some of the key use cases where Gen AI can be leveraged during the business requirements phase:
- Automated Requirements Generation – Regulatory text often runs in huge volumes and is very complicated to comprehend. Gen AI can help create automated requirements from going through the voluminous data in a structured and a simplified format, which can be used by even business stakeholders as well as technical teams to understand the requirements. For example, Basel III end game requirements can be created having structured topics on themes it covers like New Minimum capital requirements, Credit Valuation adjustment etc.
- Gap Identification in Existing requirements – With the onset of new requirements from Basel III end game, considerable updates will need to be made to the existing business documentation and identify gaps that need to be addressed as a part of the new requirement. Gen AI can potentially help in identifying gaps is existing requirements and also provide suggestive additions that need to be included in the existing business requirements to comply with the new regulation.
- Deciphering the Regulatory Requirements – Regulatory text sometimes uses complex wording, which can cause ambiguity among various stakeholders. Gen AI can help in deciphering the complex requirements in simple texts and clarify meaning through its chat interface and avoid inconsistencies in understanding. For example, Accumulated Other Comprehensive Income (AOCI) changes in the Basel III end game and its applicability.
- Regulatory Requirements Management – Gen AI can help in better business requirements management. It can help in identifying cross dependencies on other similar requirements such as stress testing requirements generally have dependencies on the output produced from Basel requirements, tracking changes from prior versions like for example, changes from Basel II to Basel III.
- Testing of Business Requirements – Testing services or assurance is one of the key activities that occurs after the business requirements are developed. Assurance teams test the output generated by the developed solution against the business requirements and ensure that the solution is producing output in line with the requirements. Gen AI can help in generating simple and complex test scenarios and test cases which can be leveraged by the assurance team to test the completeness and the required functionality expected from the regulatory business requirements.
Though Gen AI helps automate a lot of areas in business requirements, it should be used only as an enabler to create business requirements ensuring completeness, reducing inconsistencies, improving comprehension and helping stakeholders to collaborate more actively. Gen AI business requirements outputs will still need to be reviewed and signed off by business stakeholders before the technology implementation. Gen AI brings in efficiency in the business requirements process and should be leveraged with the right checks and controls and thereby provide business value and reduce time to market for many new regulations.
The views and opinions expressed in this article belong solely to the authors and do not represent those of the authors’ employer organisation.
The author, Ajay Katara, is a Consulting Partner and Heads the RegTech Portfolio in Banking Risk Management area at Tata Consultancy Services (TCS). He has extensive experience of more than 19 years in Business Consulting, Transformation & Solution design space cutting across Regulatory compliances like Basel, CCAR, AML and BSA, to quote a few, and has worked with several financial enterprises across geographies. He has significantly contributed to the conceptualisation of strategic offerings in the risk management space and has been instrumental in successfully driving various consulting engagements.
