CECL - Current Expected Credit Loss

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Overview

The strengths of AxiomSL’s ControllerView as a technological platform make it an ideal solution for hosting the entire Current Expected Credit Loss (“CECL”) process. ControllerView can aggregate multiple data sources from different systems, bringing together data from risk, finance, ALM and market data. The data can be seamlessly aggregated without duplicating it, thus avoiding database management and data governance issues.

Once the relevant bank’s data has been aggregated, it is pushed through the business rules that determine assets classification and measurement, impairment and disclosures.

For modeling, clients have multiple options. They can plug their own or third party models onto AxiomSL’s platform that will feed and run them and extract the results. Alternatively, they can write their models in R code within AxiomSL’s platform. Finally, clients can use one or several of the models already built out in ControllerView.

The entire process is transparent and auditable, along with the option to drill down to source data and track data lineage throughout. The outputs of the models, along with the other relevant data are brought together within the CECL Integration Engine.

Key Features

  • Provides comprehensive traceability/lineage on how the data flows upstream and downstream
  • Interfaces with clients' data structure and workflow process without any data conversion
  • Streamlines and automates reporting processes
  • Automates complex workflow process enabling users to review results and enhance accuracy
  • Allows users to drilldown into the source data at any level of granularity

Key Benefits

  • A single platformthat can host anend-to-end solution or separate modulesthat integrate with outside components
  • Robust datawarehouse that can aggregate data from multiple sources
  • Transparent entire process, auditable with full drill down from results to source data
  • Intuitive, web-based dashboards foranalytics and the adjustment process
  • Automatic allocation of ALLL and calculation of GL posting

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