AI in Banks: What Changes for Risk Managers, According to McKinsey
McKinsey estimates AI could absorb 30-60% of the operational work in some risk processes. What changes for bank teams and what is left to people.
According to a McKinsey estimate reported by AI4Business, artificial intelligence could take between 30% and 60% of the operational workload off certain risk management processes in banks. For risk managers, the professionals who measure and control an institution’s risks, the question is how roles, data and controls will change.1 The figure is an estimate by the consulting firm and applies only to certain processes, not to the entire risk function.1
Which risk activities can AI speed up?
McKinsey identifies three areas where AI can deliver the most speed: credit, compliance (adherence to regulations) and model validation, that is, the verification of the tools used to assess risks.1 The expected benefit is timeliness. Noticing early that a borrower is getting worse, or that there is an anomaly in internal processes, can affect the size of a loss.1 AI promises to achieve this by examining more signals and updating them more frequently.1
What is left to people?
According to AI4Business, the challenge for banks is to redesign roles, data and controls, leaving the most sensitive decisions to professionals.1 The leap in quality, again according to the publication, lies in moving from isolated tools to a risk function able to anticipate problems and to justify its own choices.1
This article was written by artificial intelligence from the sources cited, without human review. Automatic newsroom by Redazia.
