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SAS Model Risk Management

In this blog, will discuss about model risk and how model risk management is important for business needs and how beneficial by implementing the solution using SAS.

Here we will majorly focus in the finance sector and refer in terms of Financial Services business needs.

Decision making plays a very important role in any organization's business. In order to make the efficient decision making, building model which captures the specifications of these decisions is very crucial.

Hence the loss which is resulted by using inappropriate or inefficient model is nothing but Model Risk.

So nowadays Model Risk Management has captured plenty of attention among various risk managements for many of the financial institutions.

In order to mitigate the model risk, majority of financial institutions are in the process of developing Model Risk Management frameworks.

Not only financial institutions, but also the regulatory authorities are also keeping a tab on these frameworks by enforcing certain standards in order to bring model risk awareness to effectively identify and manage model risk along with governance of entire model life cycle.

Challenges in Model Risk Management:

1) Governance: In order to comply with the standards enforced by the regulatory authorities, the financial institutions should maintain a centralized repository which contains all the required documentation, reports, checklists, dashboards, codes and regulatory feedback.

2) Data Management: Financial institutions are supposed to submit various documents/reports/dashboards to the regulatory authorities at regular intervals as per the policy. In order to submit those documents, institutions has to collect and store historical data related to customers and facilities. Not only collecting and storing the data, they have to ensure the data quality which will comply with the standards as per regulatory authorities.

3) Monitoring and Validation: Last but not the least, monitoring and validation is very crucial in the model risk management. Ongoing monitoring against the market at periodical intervals is very important for any business. In order to avoid letting small issues snowball into larger issues later on, model validation at various stages by performing various model and factor level tests which capture stability, trend and robustness of the model is essential to identify the accurate risk.


Benefits of implementing solution using SAS to mitigate challenges of Model Risk Management:

1) Centralized model repository: Irrespective of the model type, technology, platform where the data resides, SAS will allow you to access the information thereby providing the end to end workflow management.

2) Data management: SAS allows you to collect and store the required historical data accurately and also makes sure that it is aggregated by identifying the relevant fields for the creation of the data marts or repository which will connect with the live systems offline and store data. This will effectively govern the entire data management for the model risk management.

3) Model monitoring and validation: SAS will enforce the appropriate testing on the platform for various model and factor levels along with adequate reviews in order to adapt the regulatory guidance on monitoring and validation.



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