CECL Model as proposed by FASB aims to shift the focus from incurred loss calculation to expected loss calculation. Financial institutions transitioning to CECL are facing significant challenges to understand, interpret the new requirement, and more importantly, how to best implement the CECL. The impact of transitioning to CECL might require changing credit loss computation methodologies, overhauling data management processes, and in many cases, more refined core systems to capture and store additional data.
Argus CECL Bootcamp is an industry first initiative to jump-start your CECL journey. It’s a one-stop solution for collaboration, discussion, and information to help you transition to CECL. You get access to an auditable CECL data mart specific to your institution and a comprehensive set of data quality reports to build your foundation for CECL. Use CECL Bootcamp Discussion forum to share questions, concerns & implementations challenges as well as learn best practices from experts and peers. It also provides exclusive access to a vast repository of resources, including the latest Regulators Webinars or comment papers.
Get access to auditable CECL data mart specific to your institution.
Exclusive access to the latest whitepaper, blogs, webinars and other resources pertaining to CECL.
Share questions, concerns through the discussion forum, get answered by experts and peers.
Draw value from data using CECL focused comprehensive set of Data Quality Reports.
Receive notifications directly to your inbox related to any new discussion or resources, follow industry leaders and stay updated.
Get institution specific consultation on data requirements and data quality from industry experts.
We believe that the first step to an accurate CECL estimation is improving the quality of underlying data. To this end, as part of CECL Bootcamp, a key objective is to assess the health of data on an ongoing basis and provide recommendations for remediation. This is achieved by creating institution specific CECL data mart and delivering data assessment and data quality reports focused on your portfolio.
Data-health assessment and suggestions for corrective action.
Prompt identification of data issues.
Identification of important attributes that are missing.
Trend analysis to identify potential underlying issues with data quality.
Checks for data accuracy as well as consistency.
Save valuable analysis time spent on data issues.
Drive accurate CECL estimation.