Banks must deploy AI with urgency to save SMEs
Government-led funding programmes are a vital pillar of support amid the current COVID-19 crisis. However, their effectiveness relies heavily on the ability of lenders to qualify, process and issue loans at speed.
Current evidence suggests this speed and efficiency is not always at the required level. The rate of completed applications under the Coronavirus Business Interruption Loan Scheme (CBILS) has been widely criticised, and the newer Bounce Back Loans scheme has seen banks overwhelmed by skyrocketing loan application volumes.
Banks managing financial support schemes for businesses impacted by the pandemic must deploy artificial intelligence with urgency. This is crucial if they are to process loans quickly enough to save businesses from collapse while managing their risks (by reducing the default rates to banks) to help safeguard economies.
Banks under “immense strain”
Rarely, if ever, will banks have faced the kind of pressure they are under at the current time. They were already under immense strain transitioning their operations to be compliant with social distancing guidelines. Now, they are being asked to cope with record levels of demand for financial relief. They are also being asked to lend in unusual circumstances, requiring them to manage the twin pressures of increasing arrears and political pressure not to trigger defaults.
This pressure is only heightened by banks’ reduced workforce capacity – due to self-isolation or staff looking after family – and by the understandable frustration of small business customers in need of urgent liquidity to keep their businesses afloat.
The need for speed
The slow pace of funds dispersal comes as a result of call centres that are ill-equipped to cope with demand and, in some cases, arduous application processes requiring applicants to download a PDF, print, complete, scan and send it back; or very basic online forms.
In a crisis response like this, rapid change is crucial.
Banks must have the agility to adapt in order to deliver whatever is required to support the financial health of their customers. They need to quickly scale up operations and develop new digital products and processes in highly compressed time-frames.
To cope with the huge demand for Bounce Back Loans, lenders need to provide simplified digital self-service user journeys. In the case of CBILS, they also need to quickly conduct eligibility checks, adhering to bank risk criteria, and establish appropriate pricing – and if the programme is modified in order to stimulate greater demand, banks will need a simple automated system in place that can be easily altered to adapt to new criteria.
To do this, they must urgently deploy technology that can support greater automation and efficiency and improve productivity. This technology not only exists, but is already successfully being used to solve very similar problems in government emergency loans programmes elsewhere.
The technology exists
One technology that can effectively solve these challenges is Explainable AI (XAI), which enables banks to significantly accelerate digital onboarding, conduct eligibility checks and process loan applications, taking into account a wide range of variables when making lending decisions. These include elements like additional incomes, new cost reductions, and alternative forms of collateral. XAI helps banks make urgent lending decisions that are transparent and explainable in human language to the end-users, customers and regulators.
As well as being transparent, XAI can centralise and enforce policy rules to ensure decisions are based on bank-specified criteria. This consistency in decision-making is hugely important in the current context as it reduces the need for manual intervention, which can slow the processing of the huge volumes of applications.
This kind of technology can be deployed in the cloud in days, in some cases, or just a few weeks in others – as has already been proven to great success.
In the US, in response to the Small Business Administration Paycheck Protection Program (PPP), which seeks to inject an immediate $349 billion in rescue loans for small businesses, Atlantic Union Bank substantially scaled up its capabilities in a matter of days.
The bank expanded its team from 40 to 400 in nearly a day and put a specific digital loan portal together with validation and security parameters in place, rolling out updates and improvements every six hours while customers were already applying. The result: in 13 days the bank delivered £1.4 billion for 6,500 business customers.
This is the kind of nimble, customer-first approach that banks should be taking at present. Current circumstances are not normal, so banks can’t rely on their normal development and decision-making processes. Projects that would normally take months need to be delivered in days.
The time to act is now
Crises like these occur once in a generation, maybe even once in a lifetime. How businesses react in these circumstances will be remembered for a long time.
For banks, the pressure is huge. But there is also a once-in-a-lifetime opportunity to make the difference between a huge number of businesses staying afloat or going under. Whether they grasp this opportunity depends on whether they show the agility and decisiveness to sufficiently adapt their operations to deliver the funding SMEs so urgently need.
I believe the deployment of technologies like XAI are key to banks making this happen. This pandemic has shown us how essential agile and scalable technology is to crisis response. Now, more than ever, banks need this future-proof technology to get help to their customers fast.