Event-Based Scenario Analysis

Look forward

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.

Re-run analysis

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.

Get granular

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.