Automating Credit Risk Surveillance Using Statistical Models
Date:
Tuesday, February 15, 2022
Location:
On-Demand
Duration:
45 minutes
Credit ratings play an essential role in monitoring credit risk and identifying investment opportunities. Based on criteria that includes both qualitative and quantitative considerations, and subject to committee review, ratings movements can happen at any point in time. Being prepared for these significant changes is critical.
Join this webinar to:
Get a demonstration of how you can automate your credit risk surveillance with a cutting-edge statistical model that can be used to generate early warning indicators of potential credit risk deterioration and/or improvement, that may trigger portfolio rebalancing
See how you can perform exploratory analysis with different datasets and build models supported by multiple languages and data visualizations, hosted in a notebook environment on the S&P Global Market Intelligence Workbench IDE platform
Natalia TymchyshynProduct Marketing Coordinator,Credit Risk Solutions, S&P Global Market Intelligence
Speakers
S&P Global Contributor
Onik KurktchianProduct Manager, Data Management Solutions, S&P Global Market Intelligence
S&P Global Contributor
Giorgio BaldassarriPhD, Global Head, Analytical Development Group, S&P Global Market Intelligence
Giorgio leads the Analytical Development Group, part of S&P Global Market Intelligence’s Credit Risk Solutions. Full Bio
Giorgio leads the Analytical Development Group, part of S&P Global Market Intelligence’s Credit Risk Solutions. His team includes highly-skilled individuals with multiple qualifications in the financial and quantitative space (FRM, CFA, PhD) and develops all credit risk statistical models that power the Credit Analytics product.
Giorgio’s team developed several statistical models to assess credit risk of public and private companies, more recently including a trade-payment model that was used to succesfully monitor payment behaviour of SMEs during the 2020 pandemic.
On the climate-change front, Giorgio’s team developed two statistical models that estimate the financial and credit risk impact of energy transition scenarios on public and private firms:
Climate Credit Analytics, developed with Oliver Wyman, offers a sector-specific, bottom-up approach for companies operating in carbon-intensive sectors.
Climate Risk Gauge, developed internally, offers a consistent and scalable view across all industry sectors.
Giorgio regularly speaks at international credit risk conferences, webinars and events (RiskMinds, RISK EMEA, IECA). More recently, he authored a paper in the Journal of Energy Markets on the credit risk implications of various carbon tax scenarios (Journal of Energy Markets, Vol 13, number 2, pages: 1-24, June 2020) and spoke at several conferences (IRMC 2020, EDHEC 2020), panel discussions (“Navigating climate risk as a financial risk”) and webinars (“Tackling climate change for banks: the culture, the data and the analytics”) on banks’ challenges to implement climate-related stress testing scenarios, climate-related scenario analysis and credit risk assessment in TCFD reporting, and impact of various carbon tax policies on public companies credit-worthiness. Minimize
S&P Global Contributor
Kieran ShandApplication Specialist, Data Management Solutions, S&P Global Market Intelligence
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