Event-Based Scenario Analysis
Managing risk in an ever-changing world
Most banks take a historical view of their loan book, and don't price loans based on what's going to happen in the future. Scenarios at most banks are theoretical in nature and don’t provide actionable insights. Events such as trade wars, pandemics, natural disasters, and climate change are by their very nature situations that are hard to predict or plan for. We can make assumptions based on what we have seen with similar events in the past, but no two are the same, so any view of them needs to be supplemented with forward-looking data, which takes into account future challenges that may arise.
The pandemic proved banks need to rethink their methods and start using real-time data to avoid lending mistakes in turbulent and unpredictable times.
Forward-looking data, such as projections of revenues, provide an additional means of understanding future risks. As these offer a glimpse of a possible outcome under certain assumptions, they can never be as accurate as historical numbers, but they do give banks and borrowers the opportunity to act with foresight.
Every crisis is so different and ever-evolving that banks need to be able to re-run scenarios on loans on a regular basis, using new, real-time data as it becomes available. Our robust, independent framework provides an effective challenge to the qualitative overlays that most banks apply to scenarios. By adding to your own risk models, you bring more timely information, reporting, and rigor to the control process. Our approach results in significantly lower losses than traditional top-down methods, freeing up capital and helping you work with customers before a default arises.
Lending models need to provide an understanding of the portfolio at the granular-loan level, taking into account the individuality of each business and how metrics such as cashflow may be affected by a crisis. Our solution enables banks to get a forward-looking view of their loan book across 1000+ sub-sectors, and with extreme granularity (L6 analysis). Machine learning and AI rapidly contextualize borrower financial performance using sector-specific insights and scenarios, peer analysis, and anomaly detection. This allows you to interrogate the differences between business at a sub-sector level, where significant variations exist, leading to a better understanding of risk and more granular, tailored credit policies.
"We can’t rely on the old ways of assessing risk. We need to have the tools to be more responsive to these different types of recessions, which is why OakNorth’s software is going to be a very important product for a lot of banks."
EVP, Chief Credit Executive at Old National Bank
"OakNorth enabled us to take a granular, bottoms-up approach, with a company-wide view of credit quality and industry exposure. As a result, we identified the most vulnerable businesses and took proactive measures to support them."
PRESIDENT & CEO AT CUSTOMERS BANK
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