Any kind of project, however diligently planned, is exposed to many risks that can incur a great loss. Whilst one might never give a foresight of the future, certain frameworks can help to keep a check on surprises one might face while executing a project. Modelling risk can help us to identify those areas, mitigate them such that one can be prepared to deal with consequential losses if incurred.
Let us understand What model risk is?
Major disciplines need to use high-end applications to develop quantitative models. This involves risks because of possible errors that can happen while creating the models, besides unsuitable implementations and improper usage. The errors are not limited to monetary losses but also shows poor decision making and causes irrevocable reputational issues. There comes the need for Model Risk. It is prominent for two reasons
- Fundamental inaccuracies of the model resulting in errors
- Improper usage that causes a risk
A model is a mixture of data inputs, variable assumptions, resulting outputs, process and scenarios. It applies all kinds of data like economic to statistic, mathematical to financial and their relative techniques. There are three major components of a model.
- Inputs: It can be either the data or the assumptions related to a model
- Process: A process is a method that can transform the input data into potential estimates
- Reporting: An estimate visually expressed into insightful information for the project management
Possible Sources for Model Risk
- Data: The right kind of information helps to develop a flawless model.
- Implementation: Incorrect implementations results in wrong decision making.
- Methodology: Choosing the best suitable method as per the business logic is important
- Assumptions and parameters: Unrealistic assumptions can greatly affect the parameters that could create a model.
- Misuse: A model should be made secure in nature, such that it can never be misused.
- Interpretation: A wrong interpretation can be followed by a wrong action course.
- Inventory: Inaccuracy of inventory might call for a stock up.
A framework of MRM (Model Risk Management)
The right Model Risk Management framework should always be designed based on the best practices followed in the industry adhering to regulatory guidelines. Here is a brief process about the life cycle of the MRM framework
Certain standards are set while developing a model. These standards need to be maintained throughout for qualitative results. Along with the overall criteria, the internal parameters should be either similar or even higher at standards when compared. The standard here is encircled to every phase of creating a model. Like
- Data Quality
- External data
- Reporting is a few important places where setting standards can avoid Model Risk Management
A volume of risk that is assumed by a company and is ready to face the same to achieve the required objectives is referred to as Risk appetite. The risk appetite always depends on the motivation for which the risk model is applied. It is also stated in relevance to risk tolerance which relies on various metrics like
- How many categories of models fall into high-risk?
- What is the total risk exposure quantitatively?
Identifying specific risks that can directly affect the business, can help in modelling that risk accordingly. Having a track of the existing models and accomplishing them as per the above-mentioned metrics helps to identify those models that need a change. A simple model inventory should be able to categorize the following features.
- Model Name
- Describing the purpose
Each model needs to be assessed for the model risk both quality and quantity-wise. These methods help to derive the possible risk assessment on the large picture.
- Quantification methods include sensitivity analysis, backtesting and challenger model. Every distinct model that is quantifiable is measure and collated for deriving appropriate factors.
- Qualitative assessment is derived by assessing the purpose of how a model is fit. These results indicate how robust a model is and directly affect the risk rating. Qualitative metrics are considered to measure risk, check for standards, find errors, assess the risk volume.
Mitigation includes a set of strategies that are formed as part of the framework for assuring flawlessness in a model. The strategies are like
- Developing changes in a model
- Understanding the nature of existing risk and structuring for the new ones
- Employing a strategic team to validate and monitor the risks involved
- Adaptability to adhere to a new set of regulations from time to time
- Enhancement measures to mitigate risk
A team is set to continuously monitor the framework and report the functioning issues. Monitoring if the model has adhered to all the above parts of the framework and reporting the issues to the management regularly will help the model stick to standards. Each model of the entire model inventory is measured as per the MRM framework. Corrective action is taken on all those identified weakness and validated accordingly. Observing new trends is risk is a key part of monitoring.
Big data’s role in MRM
Big data has a whole lot of contribution to Model Risk Management. It can transform an organizations security systems to the finest level. The following are the results obtained with big data intervention into MRM. They are
- Fraud Prevention: Predictive analysis is a great method to identify the fraudulent activities that can take place in a business. Large volumes of data are closely monitored and various approaches are adopted to identify the key junction to identify frauds.
- Third parties: A vendor involvement in any organization should be closely monitored for protecting the private data. Strong rules needed to be launched for curbing the risks linked to third parties. The analytics is used to manage their operational involvement.
- Churn rate: To reduce the loss of a potential customer to the competitor in the market, one should invite big data analytics. This will help identify the customer or client interest and help to address those complaining issues asap.
- Credit Management: For an extraordinary financial discipline of a business, big data helps in managing the economic history and help assess the accurate payment patterns required. This will save the organization from paralyzing completely due to monetary loopholes.
- Commercial Loans: Be strong enough to decline a loan to all those clients that could defect. Big data helps in identifying through analytics the repayment pattern of a client, therefore, ensuring security for the future.
With clarity on what a model is to identify the loopholes in creating a model, the Model Risk Management is always crafted finest with the accurate framework. Not to be forgotten is how big data analytics has a key role to play in this entire process of Model Risk Management. And, MRM is a must for any business, regardless of the domain, and industries.