Comprehending digital lending outlook for 2023: Transforming the sector with advanced technologies – Times of India
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Praveen Agrawal, Co-Head, India at Oaknorth.
As we look ahead to 2023, I have no doubt that technology will continue to play a significant role across the entire commercial lending sector. In the face of several unprecedented economic and geopolitical challenges, such as the ongoing Russian / Ukraine conflict, universal supply chain disruption, and the looming threat of a global recession, technologies that streamline processes, strengthen efficiencies, and increase accuracies become even more vital and in demand.
According to research by Forrester, global tech spending will surpass $4tr in 2023 because of 65% of those in senior tech positions planning to grow their budgets throughout the year, despite the macroeconomic headwinds listed above. Furthermore, according to a recent Arizent survey conducted with American Banker, most businesses plan to increase their tech spend by at least 10% in 2023, with top priorities for the year ahead including advanced technologies, such as data and analytics, and cloud-based architectures.
It’s encouraging to see the substantial impact that technology has already had on the commercial lending industry, as evidenced recently throughout the Covid-19 pandemic, with it powering lenders to better serve their customers through turbulent times. However, we know we’re only skimming the surface and we’re excited to see what further advanced technologies await around the corner for commercial lenders to capitalise on.
These technologies may be several years ahead, so for now, we’ve outlined two below that we feel will have a large impact on commercial lenders over the next 12 months.
Events-based scenario analysis
As demonstrated by the Covid-19 pandemic, when it comes to adverse events, the traditional approach to commercial lending – using historical data, financial modelling of a base case, worst-case and best-case scenario, and conducting annual reviews – is an approach that is not fit for purpose. In uneventful times, these models are fine. However, for unprecedented events such as the pandemic, the traditional models proved useless as historical correlations were broken; employing the traditional look-back approach was meaningless.
Commercial lenders need to be able to run a “bottoms-up” analysis of their loan books, assigning each business a vulnerability rating based on a subsector-specific, forward-looking credit scenario taking liquidity, debt capacity and profitability into account. This more dynamic view of risk is still valuable in a more stable economy because we can update risk inside a lender’s review cycles, allowing them to take a critical view of their loan book and maintain constant focus on the items of highest impact.
The realization that many industries experienced through Covid-19 is the same: we can’t predict the future but must be better prepared for the unknown and reduce risks across our businesses with an ability to adapt quickly with data-driven decision making. In doing so, banks will identify opportunities to lend faster, smarter and more to businesses.
Data science
We continue to be firm believers that data and credit science can be leveraged by traditional lenders to enhance humans, rather than replace them. This hybrid approach is a pragmatic compromise where computers perform various tasks to allow the credit analyst to be more efficient, but the analyst remains in the driving seat and is able to train the models and direct and shape the final outputs to ensure they are coherent and understandable.
Due to the complexity of the space, we don’t believe full automation is a desirable end goal, and aim instead to achieve c.80% automation, with a human analyst always involved in the process. This critically allows human judgement to always have an influence on the outcome and helps ensure understandability of outputs.
Furthermore, looking ahead to commercial lending in 2023, traditional lenders need to assess the ability of a business to sustain a certain level of debt and repay loans. This is where data science comes in.
The only way they can effectively assess commercial credit risk is by using multiple data sources – including what may be unconventional or previously unavailable data – rather than just relying on what they’ve used in the past.
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Views expressed above are the author’s own.
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