It is the nature of finance that, at its core, is risk. Insurance is all about covering yourself for the risk of uncertainty. You don’t know if your house will be robbed but, just in case it is, you insure it. You don’t you’ll have a car accident but, just in case, you insure it. You get the idea. Banking is all about risk too. You don’t know if you might lose your investment so you hedge it. You don’t know if this person or company will pay back so you assess it. It’s all about risk management.
Now, obviously, you can be better with risk through systems analytics. An intelligent engine can far better assess a risk than a human, using decades of statistics. Even so, you will still only know that a risk exists when you see it. That is why companies, financial institutions and regulators can only deal with risk when it arrives. If we knew what systemic risks were out there, then they wouldn’t be systemic risks as we would know about them and deal with them. This is the critical point: risk is all about unknowns and, if they’re known, then they’re not risks anymore.
Risk: “A probability or threat of damage, injury, liability, loss, or any other negative occurrence that is caused by external or internal vulnerabilities, and that may be avoided through pre-emptive action.”
The reason I mention this is primarily due to the use of artificial intelligence (AI) in banking. I keep saying that a bank cannot apply AI to dirty data, which is where they struggle with customer service. A bank needs a single customer view to effectively apply AI to customer data, but most customer data is fragmented across multiple legacy and fragmented, silo-based systems. This is why applying AI to risk is far easier, as it can be modelled, simulated and calculated, which is why I see so many banks using AI for risk management.
According to a recent survey, 88% of respondents see AI as a foundational change for risk management. There again, in another survey, only a minority of respondents believe these technologies will work in the risk management functions due to legacy technologies (cited by 69% of respondents) and the increased velocity, variety and volume of data (named by 73%).
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