In the wake of the global economic crisis there has been a significant change in banking economics; preceded by an avalanche of regulation.
By Solethu Maku, committee member on the IRMSA Risk Intelligence Committee
Although these prudential reforms have resulted in well capitalized financial institutions; this has certainly come at a cost to the shareholder.
The regulatory storm seems to have abated, slightly, and the question remains: will shareholders see an upside on their investment?
One of the ways banks seek to harness shareholder value is undertaking a rapid digital transformation journey, which is becoming critical considering the inroads by fintechs, characteristic of the advances we are seeing in open banking.
“To safeguard their organization through the digital transformation journey, financial firms need to close the gap between their digital aspirations and the reality of the legacy IT estates,” Matthew Hayday at Parker Fitzgerald concluded in the report, Digital banking transformation creating new systemic risks.
As commercial banking divisions embrace the use of artificial intelligence (AI), machine learning (ML) and predictive analytics; so should risk management rapidly position itself as an enabler for conscious risk taking through the exploitation of technology.
The adoption of technology to a large extent been particularly slow, largely because the cost of errors in the risk environment can be unacceptably high.
If risk management practitioners are to optimally partner with business, a shift in how risk management tools are deployed needs serious consideration towards providing intuitive, real-time risk management.
Techniques such as AI, ML and analytics are best positioned at modernizing how risk is managed.
As we employ these techniques towards the implementation of a robust and proactive RCSA process, precise capital modelling and efficient risk alert systems we need to consider:
* Do we understand our institution’s internal operational limitations in a manner that manages the additional digital systemic risk?
* Do risk and IT practitioners understand the limitations of the systems/solutions (including models) that are available to the organisation, as well as data privacy protocols, to effectively enable intuitive/real-time risk management?
* Is your organisation having honest conversations on the kind of workforce needed to elevate the relevance of risk management in the changing (changed) face of banking?
* Does the risk framework between bank and the regulator(s) sufficiently embrace the benefits of digital transformation?
AI and ML are not the silver bullets, however coupled with the expertise of risk practitioners as well as a consistent process of re-design and improvement, they can be exploited towards transforming how risk is managed.
“It was eight years ago that those robots began showing guests around Santander City, but there is still not a single robot to be found in any of Santander’s 13, 697 bank branches,” the Financial Times concluded in, AI in banking: the reality behind the hype.