An essential part of using better data to grow commercial loan portfolios includes finding new sub-sectors and businesses to support.
Most investment in technology and data in commercial lending in recent years has focused on supporting smaller loans up to a maximum size of $250K. These monies go to smaller businesses and the process tends to be automated, credit-score based, and in many cases, instantaneous.
Once a business reaches a scale where it may be looking for a loan of more than $250k up to tens of millions of dollars however, the process becomes much more complex, with most lenders relying on slow, opaque, and manual processes.
If faced with the choice between spending time working on a $200M loan which will generate $2M in arrangement fees or spending time working on a $20M loan which will generate $200K in fees, which do you think the lender is likely to prioritize? Herein lies the issue: mid-sized loans (i.e. those that range from $1M-£100M) are often too small to justify the unit economics of a manual approach but too large for assessment by an automated process.
Fortunately however, technological developments have created strides in artificial intelligence and large-scale data analytics to create a bridge between fully-automated and fully-manual credit assessment. This semi-automated process accelerates the assessment period while enabling critical understanding and customization for every loan. These same types of technology can also help banks identify underserved and overlooked businesses through better automation and analytics — thus growing and diversifying their commercial loan portfolio.
Assess your own risk, technology and data
Just as lenders must use forward-looking scenario analysis to assess borrower risk, banks must also use this to determine their own risk.
- How could rising inflation impact employee salaries?
- How could extreme weather events impact employees (e.g. the Texas deep freeze which caused black outs for days, the fires in Colorado which forced dozens out of their homes for weeks, etc.)?
- How could these same extreme weather events impact a bank’s branches or data centers? What contingency plans or services are in place to protect data from loss and damage?
- How could ongoing supply chain issues impact a bank’s ability to get the new technology and equipment it may need?
- How could rising fuel prices impact the bank’s underlying costs?
The COVID-19 pandemic proved that banks need to have an in-depth understanding of all operations, their owners, their backup protocols and utilities, as well as the scenarios which could make it vulnerable in the future.
Adjusting to an ever-changing world
Climate change, COVID-19, and supply chain issues are just some of the recent examples of global challenges impacting commercial lending, making granular, forward-looking scenario analysis crucial.
As the world moves forward, lenders' understanding of risk must evolve. Banks can’t only look to history for answers on conducting risk analysis, but must also examine forward-looking data such as sentiment analysis, transition risk, revenue projections, granular industry benchmarks, macroeconomic drivers and scenario analysis.
Improving your bank's tech stack through available data will reduce time on annual reviews, enable you to identify opportunities to support mid-sized businesses in their growth, and give your portfolio managers the time and ability to understand the current and future risk facing borrowers.
By aiding scaling mid-sized businesses, banks can fuel economic growth, champion environmental improvement, drive increased employment rates, and foster a positive impact on communities across the globe.
Author: Peter Grant, OakNorth President and COO
This article is the third in a three-part series on how finance-function professionals can effectively assess a pandemic, climate change, or supply chain shortages with scenario analysis.