The history of credit and lending to business customers dates right back to Mesopotamia when the very first payday loans were used by farmers and the code of Hammurabi defined how the interest charged on silver loans was to be regulated.
In more recent times, technologies including online lending have been as transformative as those first laws stamped on clay tablets. Yet, despite everything, the processes behind credit and lending services are far from perfect.
In a recent survey of 340 UK businesses who use credit and lending services, when asked what the key improvements should be made, they cited speedy decision making, mobile and online support, and transparent risk management.
In the age of whizz bang fintech, is this really too much to ask?
The research also revealed that speed of decision making is the second most important reason, behind the availability of corporate cards and services, for choosing a provider in the first place.
So, how can processes be improved? Firstly, real-time automated decision-making must become standard – harnessing the power of AI. Secondly, banks must integrate a variety of other data sources, not just annual financials, when making decisions to lend.
This will contribute to more reliable decision making and fewer checks involving human judgement – both of which can cause hold ups.
Lending and balancing risk is critical for improving customer experience through the credit decisioning process. If banks integrate a more advanced capability to draw upon a multitude of data available on existing and potential customers, coupled with far
faster processing speeds, they will be able to apply more advanced algorithms and rules to decision-making. All core lending products now have automated credit rules calculations and AI and machine learning can be applied to give a higher level of confidence
to a lending decision, as well as improved efficiency and lower cost in execution. This can reduce potential defaults as well as speed up the lending process, getting valuable assets onto the balance sheet at a much faster rate.
In terms of providing more transparency in risk management, credit decisioning is at the heart of a financial institution and any rules, algorithms and automated decision-making must be fully auditable. Fortunately, it is now possible to build transparency
control into the credit decisioning process using a transparency ‘switch’ so you can expose and understand clearly what is happening behind the AI linked to any credit decision. This concept and approach will become standard not just for credit decisioning
but any AI or machine learning processes in a variety of sectors. Humans need to be able to clearly justify credit decisions to customers and regulators…as should any machine!
What else is on the horizon for the industry? Some anticipated trends in credit and lending that were highlighted in the aforementioned research were more decisions based on detailed financial history, an increase in bespoke and personalised products, and
greater choice of lenders. While retail banking customers have expected tailored services for some time now, clearly this expectation is emerging in the corporate environment.
Financial services organisations operating in credit and lending are only touching the surface of the capabilities of AI and machine learning – which is understandable given the technology’s relative infancy. However, as these businesses get to grips with
the new tools, we should see changes on an exponential scale, with far more tailored and suitable offering being delivered at a rapid pace that businesses need in order to operate in our digital world. All while maintaining the levels of transparency required
to maintain and build trust. The credit system has barely changed since Hammurabi and his mates conceived it. Finally, a shake-up might be on the cards.