So what is happening? Banks are integrating AI and ML to improve customer experiences and advance efficiency. And very importantly, this technology push will make finding compliance issues early and systematically easier, thereby ensuring adherence to financial market regulator rules and make gigantic fines of the past a distant memory. It will also lead to massive consolidation.
As an example see this list of tasks that case managers at UBS have:
Indentify Machine Learning opportunities
• interact and closely engage with senior stakeholders across the bank to identify and specify use case opportunities for the deployment of AI and machine learning solutions applied to non-financial risks
Requirements and Functional Specifications
• define requirements and functional specifications in close collaboration with the AI model developers team
Use Case qualification and prioritation
• manage use cases from ideation to qualification and prioritization
Support during model development phase
• provide stakeholders management support during PoCs and model development phases
Operative deployment preparation
• ensure alignment with change the bank teams in Group Compliance, Regulatory and Governance (GCRG) to prepare for the operative deployment and solution transfer to end-users
Indicator, Measurment and Effectivness
• define indicators for the post deployment measurement of efficiency gains and benefits in terms of risk effectiveness
Oversight of Use Case portfolio
• keep oversight on a fast growing portfolio of use cases, identify opportunities to expand coverage of available solutions
This also fits the famous six sigma DMAIC -> define, measure, analyse, improve, control framework.
UBS at the forefront of Artificial Intelligence and Machine Learning
As an example, at UBS, the team Methodologies and Models within Group Compliance, Regulatory and Governance (GCRG) is a recently established unit which uses state of the art AI/Machine Learning based solutions, covering the entire non-financial risk control and compliance space.
Next-generation methods being applied and developed in Swiss Banking
The teams mandate includes the design of the next-generation methodology set as foundation for the AI development pipeline in an end-to-end responsibility.
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As an example: This will allow the major banks to take market share from players like Partners Group, who have been overhyped and unchallenged. What happens to overhyped and underchallgend companies when the competition from global players sets in, is something that could be seen with a company like Leonteq. Swiss Banking giants are awakening to the mistakes of the past and improving their stance, in order to make sure they do not become the next victims of the digital age.