A data-driven approach to banking can transform the commercial lending landscape by empowering lenders to make more informed and accurate lending decisions, reduce risks, and improve efficiency. By leveraging data analytics, artificial intelligence, and machine learning, banks can gain a deeper understanding of their customers’ financial profiles, credit worthiness, and risk profiles.
Traditional credit scoring models, using historical data, have limitations in predicting credit risk. A data-driven approach to commercial lending that uses a wide range of data sources can create more accurate credit scores and help lenders better understand the risk profiles of their customers, enabling more informed lending decisions.
Utilizing both traditional and alternative data sources allows for a more thorough depiction of individual borrower credit situation
The onset of COVID-19 and the ensuing fundamental changes have demonstrated the necessity for banks to think more proactively about how to increase lending operations while controlling risk in the event of any macroeconomic shock. The current unprecedented economic and geopolitical problems, such as the ongoing Russia-Ukraine conflict and the looming prospect of a worldwide recession, serve to reinforce the strategy even more.
Despite economic uncertainty, banks cannot afford to be overly cautious as they could miss out on lucrative business opportunities by turning away qualified clients. To stay relevant and keep up a high level of financial performance, banks must transform their credit offering by using artificial intelligence/ machine learning, and enhanced data and analytics to make quick and informed lending decisions and to weather an economic downturn.
Traditional methods of commercial lending, such as utilizing historical data and depending on prior credit history, are acceptable during uneventful times. Unprecedented events, such as the COVID-19 pandemic, put these models to test, where their forecasts can turn inaccurate. The only way to effectively evaluate commercial credit risk is by using multiple data sources, including alternative data that often has less lag than traditional sources. Banks can leverage new opportunities by integrating internal customer data with broader and more comprehensive information gathered from external and unconventional data sources.
Banks can utilise AI/ML more frequently to quickly assess massive amounts of traditional and alternative data sources and identify new risk groups or trends. Additionally, lenders can use AI to investigate all relevant customer data, including credit score, payment history, industry, and payroll trends to predict the probability of a loan default. AI and ML can also be used to spot early warning signs of trouble and offer more accurate projections based on real-time data by looking at cash flow projections, sales and expenditure data, and other sources. Additionally, lenders can expand the reach of their services and draw more businesses into the official credit system by utilizing alternative data sources and data analytics.
At OakNorth, we apply data analysis techniques to create unique models that provide a granular level of analysis for each borrower. By combining borrower-provided data with our vast repository of external data, we are able to add depth to point-in-time analysis and monitoring.
As commercial lending involves significant risks, including credit risk, operational risk, and market risk, a data-driven approach can help lenders monitor and manage these risks more effectively.
Adopting a strategy based on data-driven decision-making will give lenders a more comprehensive
picture of individual borrower credit situation, enabling them to lend quickly and wisely to more businesses and driving better outcomes for them.
Disclaimer
Views expressed above are the author’s own.