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.