Consumer credit modelling software of the year – SAS

Terisa Roberts, SAS
Terisa Roberts, SAS

SAS offers an integrated environment that supports the end-to-end credit lifecycle, starting with loan origination all the way to account management, collection and recovery. The deep and broad set of risk models range from regulatory capital and provisioning such as through-the-cycle or forward-looking probability of default, loss given default and exposure-at-default estimation models, to behavioural scoring, income estimation and prepayment models. 

The SAS environment provides data management tools capable of handling enterprise volumes and a single unified platform with integrated governance of data and models, as well as auditability and data lineage. Models can be easily and rapidly developed and deployed through a variety of methods, including application programming and graphical user interfaces, with a choice of methods also available for disseminating results. The models are open and can be tailored to unique customer requirements.

All models can also be deployed in the SAS decisioning engine for integrating automated credit decisions into risk management processes, such as loan origination, limit management and collection. The engine combines business rules management, advanced analytics and decision governance to provide consistency, efficiency and quicker time-to-market for complex models. 

The SAS environment enables data engineers and scientists to access and onboard a wide variety of data, including granular transactional data, data from third-party providers and traditional or alternative data in structured or unstructured formats. Comprehensive capabilities are available for data preparation and quality assurance. Statistical or modern machine learning models can be developed using open-source languages, such as R or Python, or SAS interfaces. The models can be deployed to any combination of in-memory, in-database, batch, real-time or streaming systems without requiring any recoding. A comprehensive set of model monitoring metrics for backtesting, benchmarking and compliance is included. All facilities are available for a variety of model types, including linear and non-linear, scorecards, forecasting, optimisation, simulation, machine learning, deep learning and text analytics.

SAS provides preconfigured credit modelling content that helps ensure compliance with regulations worldwide. The company has a global pool of expertise in consumer credit modelling in house, as well as thousands of user communities from its extensive implementations internationally, all of which share knowledge and experience.

SAS is making its credit risk modelling available on its new Viya cloud-based in-memory analytics engine, which offers scalable and flexible processing.

The judges said:

  • SAS’s offering is enormously impressive across data management, analytics, performance, governance and expertise. The company has a strong reputation and track record.
  • Comprehensive scope and governance, feature-rich across the full lifecycle, with a user base that is significant in both number and prestigious names.

Terisa Roberts, director, global solution lead, risk modelling and decisioning, SAS, says:

“All lenders are facing myriad risk modelling demands, driven by the introduction of digital services, the growing importance of artificial intelligence, and regulatory requirements. In today’s challenging environment, firms want to offer customers a consistent and contextual experience while managing costs and efficiency through automation. To meet these demands, SAS offers an integrated environment that supports the end-to-end lifecycle for a wide range of risk models, all easily integrated into decision strategies. SAS’s broad set of capabilities in data management and analytics, robust open-source integration, model governance and lineage produce an offering unmatched in the market.”

Read more about the Risk Technology Awards 2020 winners


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