Prior to his consultancy career, Yerbol Orynbayev served as the Deputy Prime Minister of Kazakhstan from 2007-2013 and Aide to the President on economic policy from 2013-2015. In this article, Orynbayev discusses the recent Fed rate cut, its potential impact on lending growth for US banks, and the heightened need for advanced AI-driven credit risk management.
Mid-September marked a momentous occasion for the US economy. Following a two-and-a-half-year grapple to lower inflation, the world would finally see Fed Chair Jay Powell step up to his lectern and deliver a long-awaited cut to interest rates. And by a bold 50 basis points, too.
For banks across the States, the announcement meant one thing – the possible return of a spike in lending. After all, with the borrowing cost now reduced, customers would undoubtedly be more willing to take out loans, especially as the Fed continues to gradually ease its benchmark rate. But this comes with its risks – and banks must remain alert.
Of course, for all banks, lending is always welcome, but as more and more people begin to knock on their doors, the risk of loan growth – i.e. increased chances of defaults and delinquencies – only becomes more real. Fear-mongering aside, this low-interest future could pose some harm – and banks must meet this boom with tightened, sophisticated and automated risk management defences.
That said, I can’t help but think the announcement was slightly bittersweet for the US banking sector. After all, recent reports found that they collectively made a huge US$1 trillion windfall from the Fed’s period of high rates; they were essentially able to offer comparatively lower rates to their savers and, as a result, capitalise on bumper interest rate revenue. Surprisingly or unsurprisingly, US banks adapted well to the shifts in the macroeconomic tide.
Now, however, that cushion has to be put to good use. They need to dust the cobwebs off their credit risk management desks, deploy some of this excess revenue to give them more technological firepower and prepare themselves for a possible influx of borrowers to come. Currently, risk costs only comprise 2.5% of banks’ overall operating expenses (McKinsey) – this will hardly be sufficient as lending activity climbs.
Doubling their investment in their credit risk management processes would be a sensible move to tackle any onslaught of borrowers. And where they invest this capital is, for me, at least, pretty obvious: Artificial Intelligence (AI) and Machine Learning (ML).
AI and ML could help assess potential borrowers’ credit histories as well as their capacity to repay loans, allowing banks across the States to grapple with a surge in loan requests more efficiently. Of course, that’s not to say the overarching credit scoring system in place today would change – these techs would just be able to speed up the time it takes to analyse potential borrowers according to this framework.
Of course, I completely recognise this is all well and good for the JP Morgans of this world, who certainly have the liquidity to splash the cash on any innovative emerging techs that break onto the scene. But, still, that’s not to say the smaller players of the US’ diverse and vibrant banking sector would be left in the dust.
These regional and community banks, institutions with total assets between US$10 and US$100 billion and those with less than US$10 billion in assets, respectively, could partner up with AI-focussed startups – and collaborate to modernise how they analyse the credit risk of new borrowers. Teaming up with these startups would allow regional and community banks to enjoy the efficiencies of AI and ML without skimping on quality. FinTech experts, for example, already know the processes of building these systems through and through – they could handle all of these, including the building of data lakes, all without regional and community banks incurring the pitfalls and costs of experimentation in-house.
The time to make this jump is now. The memories of last year’s banking crisis are still fresh, and while banks across the States will definitely be eager to dish out credit, they have to be wary of striking a balance between growth and caution. We’ve seen what faulty risk management, in any form, can do to key regional institutions – those lessons cannot be forgotten.
For US banks, automation and digitalisation should be a prerequisite for loan growth. In the coming months, more and more people will begin to bash on their banks’ revolving doors, requesting all types of credit, from mortgages all the way to small-dollar loans (American Banker). To meet this demand, banks need to integrate AI and ML into their credit risk management processes and bolster them with a vital boost of capital.
So, to all banks across the States, please beware: a lending boom is on the horizon, and while I’m sure you’ll jump at the demand, you must exercise caution.