The history of credit and business lending dates back to Mesopotamia when the very first payday loans were used by farmers and the Hammurabi code defined how interest charged on cash loans was to be regulated.
More recently, technologies, including online lending, have been as transformative as those first laws carved on clay tablets. Yet despite everything, the processes behind credit and loan services are far from perfect.
In a recent survey of 340 UK businesses that use credit and lending services, when asked what the main improvements are, they cited quick decision-making, mobile and online support and transparent risk management.
In the age of fintech Whiz bang, is it really too much to ask?
The research also found that speed of decision making is the second most important reason, behind the availability of business cards and services, for choosing a provider in the first place.
So how do you improve the processes? First, real-time automated decision-making must become the norm – harnessing the power of AI. Second, banks need to incorporate a variety of other data sources, not just annual financial data, when making lending decisions. This will contribute to more reliable decision-making and fewer controls involving human judgment, which can lead to delays.
Loans and risk balancing are essential to improving the customer experience throughout the credit decision process. If banks build in a more advanced capability to take advantage of the wealth of data available on existing and potential customers, coupled with much faster processing speeds, they will be able to apply more advanced algorithms and rules to decision making. All core loan products now have automated credit rule calculations and AI and machine learning can be applied to give a higher level of confidence in a loan decision, as well as improved efficiency and reliability. lower execution cost. This can reduce potential defaults and speed up the lending process, bringing valuable assets onto the balance sheet at a much faster rate.
In terms of transparency in risk management, the credit decision is at the heart of a financial institution and all rules, algorithms and automated decisions must be fully auditable. Fortunately, it is now possible to integrate a transparency check into the credit decision process using a transparency ‘switch’ so that you can clearly expose and understand what is going on behind the linked AI. to any credit decision. This concept and approach will become the standard not only for credit decision making, but also for any AI or machine learning process in various industries. Humans must be able to clearly justify credit decisions to customers and regulators… like any machine!
What else is on the horizon for the industry? Some anticipated credit and lending trends that were highlighted in the aforementioned research were more decisions based on a detailed financial history, an increase in tailor-made and personalized products, and a greater choice of lenders. While retail banking customers have been waiting for tailor-made services for some time, this expectation is evident in the business environment.
Financial services organizations operating in credit and lending are only scratching the surface of AI and machine learning capabilities, which is understandable given the technology’s relative infancy. However, as these businesses become more familiar with the new tools, we should see changes on an exponential scale, with a much more tailored and responsive offering delivered at a rapid pace that businesses need to operate in our. digital world. While maintaining the levels of transparency required to maintain and build trust. The credit system has hardly changed since Hammurabi and his comrades designed it. Finally, an upheaval could be at the rendezvous.