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NAVIGATING THE LENDING BOOM : HOW US BANKS CAN LEVERAGE AI TO MANAGE NEW RISKS
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 .
id-September marked a momentous occasion for the
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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 . 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
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
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