Chartis RiskTech100 2020 – SAS

Traditionally driven by regulation, financial institutions’ approaches to risk management are becoming increasingly business-focused. Regulation may still drive the initial requirements for risk solutions, but institutions are now seeking to unlock the benefits of data gathered and to share that data and associated processes across the business to improve performance and efficiency. This evolution of the market underpins the strategy and approach to risk management of top five‑ranked competitor SAS. 

 

Risk & finance integration

SAS has been an active analytics solution provider for more than 40 years. Its vision is to transform a world of data into a world of intelligence and its long-standing mission is to empower and inspire with the most trusted analytics. To maintain its position as a market leader, SAS invests heavily in research and development – 25% of revenues are regularly reinvested, with core areas for development including its open Viya architecture, machine learning and results-as-a-service. As the winner of the Chartis RiskTech100 Risk & finance Integration award, the company has made significant investment in creating a centralised, scaleable and flexible approach to risk management. This strategy enables financial institutions to take a streamlined and process-oriented – rather than solution-led – approach to risk. 

“While there is still demand for point solutions, institutions want to treat data, processes, scenarios and models as objects that they combine in an order that meets the needs of the situation,” explains Martim Rocha, director, industry consulting at SAS. “They want to break up risk management into core processes, such as credit loss forecasting, and then execute to meet needs.”

A key aspect of SAS’s approach to risk management is the separation of its technical platform and business content. Its risk and finance platform provides the underlying technology such as data management, process workflow and sophisticated analytics, while the content refers to the intellectual property and methodologies for specific areas of risk and finance, for example, International Financial Reporting Standard (IFRS) 9 or IFRS 17. This content can be provided by SAS, by partners or developed by the customer directly. “The purpose of separating the platform from the modular content is to provide customers with the flexibility needed to face ever-changing business and regulatory requirements without having to change the underlying architecture,” details Rocha. 

 

IFRS 9

The subject of IFRS 9 is particularly topical, with its adoption into risk and finance processes a key priority for financial institutions in recent years. SAS is the winner of the Chartis Research RiskTech100 2020 award for its approach to the new accounting standard. “We track audit and regulatory requirements closely, and our approach is to combine key analytic elements of the regulations with out-of-the-box capabilities to develop reports and workflows for IFRS 9 impairment calculations,” says Rocha. SAS also seeks to ensure its solutions can support future requirements, its SAS Solution for IFRS 9 and SAS Solution for current expected credit loss (CECL) share several components with the SAS Solution for stress-testing so it can easily be extended for stress-testing or capital planning, for example. 

Looking forward, a key area of focus for SAS in risk and finance is asset and liability management (ALM). A demanding market environment, interest rate uncertainty and pressure on margins are pushing financial institutions to re-evaluate their ALM infrastructure. This also provides an opportunity to consolidate the multiple ALM solutions and pricing engines typically in place in financial institutions. Given this context, SAS is investing heavily in updating its ALM solution to reflect the wider industry move towards scenario-based risk management, together with risk and finance integration.

“A key part of SAS’s new ALM development is to help banks optimise their balance sheets. With SAS’s position as a category leader in the Chartis RiskTech quadrant for credit risk solutions (2019), improving ALM is a natural step towards providing a holistic view of the balance sheet,” Rocha explains. “We seldom come across an opportunity where the customer just wants to perform reporting and standard analysis. Customers are seeking to enhance margins by optimising their balance sheets. Traditionally, this was done from a hedging point of view, but with more data, computing power and enabling technologies, customers are seeking to optimise across use cases.” SAS, for instance, can support analysis of different balance-sheet evolution scenarios against various market and behavioural risk factors. This allows customers to anticipate challenging situations and adapt their balance sheet to enhance its resiliency to potential future shocks or to maximise income.

 

Model risk management

SAS is also the winner of the model risk management (MRM) award, reflecting its expertise in helping financial institutions ensure governance of their models and enhance the value of the data generated. The role of MRM is increasingly important, as Rocha explains: “Models are proliferating and growing in complexity, while regulators demand more detailed information. Consequently, the need for a robust review and validation framework to effectively manage model risk becomes mission-critical.” Without an MRM framework, businesses are unable to enforce best practice across the enterprise. A fragmented approach often means little integration, with siloed model development ecosystems – which results in a limited enterprise view of model risk.

SAS MRM enables clients to centrally manage the risk involved in building, deploying and using models, while improving efficiency across the model lifecycle. Rocha says: “Our customers benefit from risk management across all elements of the model lifecycle, such as model development, implementation, use, validation, monitoring and retirement, while increasing the business value of models.” The solution is linked to SAS’s Model Implementation Platform, enabling developers to create, test and validate models, before obtaining a ’key’ from MRM to release them into production and track them.

Recent areas of focus for SAS in MRM have included helping customers safely use machine learning and the automation of simple tasks clients need to perform repeatedly, such as performance monitoring and model usage capture. Looking forward, SAS expects demand for model governance to rise as the adoption of artificial intelligence and machine learning models increases. Trust will be key for the adoption of these new models, which will need to be underpinned by a rigorous, enterprise-level approach to model governance.

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