Home / Grid / AI in banking is all about risk management

AI in banking is all about risk management

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 know if 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%).

Regardless, with regulations changing every 12 minutes (185 global regulatory changes per day), the use of AI to sift through all of that mess will be critical. Having said that, I agree with the comments from Mark Hurd, co-CEO of Oracle, where he says that most people “talk about AI because if they get on TV and talk about AI, their stock goes up.” Ha! Certainly seems to be the case with a few banks I know out there. In particular, Hurd goes on to say that he “was at a meeting with the CEO of one of the biggest banks in the world. We spent half the meeting talking about patching. Imagine, the CEO of a top bank spends that amount of time talking about patching!”

So how a big bank can truly apply AI for risk management, let alone for customer-centricity, leaves me a little bit bemused still.

About Chris M Skinner

Chris M Skinner
Chris Skinner is best known as an independent commentator on the financial markets through his blog, the Finanser.com, as author of the bestselling book Digital Bank, and Chair of the European networking forum the Financial Services Club. He has been voted one of the most influential people in banking by The Financial Brand (as well as one of the best blogs), a FinTech Titan (Next Bank), one of the Fintech Leaders you need to follow (City AM, Deluxe and Jax Finance), as well as one of the Top 40 most influential people in financial technology by the Wall Street Journal’s Financial News. To learn more click here...

Check Also

The Finanser’s Week: 13th November – 19th November 2017

This week’s main blog headlines are … Old John Cryan had some code, AI, AI, …

  • AI starts at the customer level and branches out to include every financial relationship involving the customer, i.e. insurance providers, lenders, 3rd party vendors, etc.This allows for spreading risk between all of the customer’s obligees vs. just between the customer and the bank. Therefore, everyone shares both loss and gain, which minimizes negative impact to the customer’s finances. This process eliminates the dirty data analysis issue you mention. AI’s main use in banking will be managing the financial relationships affecting the individual customer, along with analyzing all the data that will be generated. Banking will not survive with the boxed-in thinking that’s being displayed.