Today, more than 1 in 10 now spend in excess of 10% of their annual budgets on compliance. Banks are eager to find ways to bring this spending down which has lead to buzz that banks are replacing surveillance staff with AI.
Trust in financial services after the 2008 crisis is taking a very long time to rebuild, and so some banks are wary to use new technology. Regulators, frustrated with the slow speed of change, have encouraged banks to deploy more technology. Now, innovative financial institutions, like HSBC and ING, have discovered how AI-powered tools can help them in their efforts to improve risk governance.
Many in compliance functions are looking to AI to help automate routine tasks. For those in risk governance, however, the element of human judgement is irreplaceable. This is particularly so for non-financial risk management.
To evidence the embededness of effective non-financial risk management, firms need to adopt ‘augmented risk intelligence’ tools. Such tools enable risk managers to do more with less. Machine learning tools can spot patterns in standard company data sets that are associated with past risk management failures. The identification of such leading indicators position risk managers to engage proactively. This allows firms to become more timely, efficient and effective in the application of risk management resources. In time, this should produce better risk outcomes, at lower cost.