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Credit intelligence: A more robust alternative to current commercial loan modelling approaches
Journal of Digital Banking,
6
(1),
25-32
(2021)
Abstract
Most commercial lending is based on a decision-making process and modelling approach largely unchanged by technology. By adopting a data-driven alternative that takes into account the fundamental differences between businesses, lenders are able to make data-driven decisions that will ultimately lead to better credit outcomes. This paper aims to briefly outline some of the limitations of the current approach to commercial lending and suggest improvements (collectively, ‘credit intelligence’), taking specific note of lessons of the current COVID-19 crisis and how this has transformed the economic landscape. It also provides a case study (OakNorth in the UK) where these principles have been implemented and notes the promising results so far.
Keywords: credit intelligence; credit risk; portfolio monitoring; commercial lending; commercial banking
The full article is available to subscribers to the journal.
Author's Biography
Neil Kahrim is the Director of Growth & Operations at OakNorth and is based in New York. Neil was formerly at Bank of America Merrill Lynch for almost 15 years, where he focused on leveraged finance and derivatives.
Sean Hunter is Chief Information Officer at OakNorth, a multi-billion dollar FinTech firm focused on solving the problem of bespoke SME lending globally using data and technology. Prior to joining OakNorth, Sean was one of the first commercial engineers at Palantir Technologies in Europe where he led trader oversight partnerships with large financial institutions, particularly Credit Suisse, which led to being co-head of the JV called Signac. Before Palantir, Sean was a strategist at Goldman Sachs for eight years, working in a number of areas including equities, fixed income and algorithmic trading.
