For the Standardized, the parameters used by the bank are based on what the supervisory committee has provided. This means that they look at the external ratings of the customer.

There are diverse types of customers. There are sovereign customers, which include Central government banks. Financial institutions are also customers. There are also corporate customers and retail customers. These customers have different risk characteristics; that is, they have different ratings.

The parameters used also depends on the jurisdiction you are in as well. For example, when the PRA were in the EU, they set preferential treatments for EU countries, such that, if one is a member of the EU, you get professional risk weights which would ultimately reduce your RWA. So the parameters used depend on the type of customer you are and the jurisdiction you are from.

For the Internal Ratings-Based approach, the bank can do their modelling. For example, we have the Probability of Default (PD), which is the internal rating. This is the bank using its measures to provide internal ratings for customers. They could also use the modelled LGD assigned to that exposure. It is pertinent that even though the bank can model their parameters, they still have to take these models for approval.

• Data quality: To calculate or optimize your RWA effectively, you need the correct information. This means that you need the right information about your customer. The information you put in your risk engine, the system used in calculating your RWA, is the result you get. For example, as a financial institution, if the information you get concerning a customer says that the risk associated with lending to the customer is low, your RWA will be low. Likewise, suppose information obtained shows that the risk associated with lending to a customer is high. In that case, RWA will be increased. The information you see is what you get. To effectively optimize your RWA, you need quality data. For many banks, this problem starts from the information collected while onboarding the customer. The information gathered significantly impacts the downstream consumption of such data over time. It affects your RWA calculation as you move toward the credit risk management process.
• Lack of investment in Technology: It’s very evident that some banks still use Excel in calculating their Risk Weighted Asset for credit risk. This has caused issues as using Excel makes it slow and difficult to optimize, making your RWA calculation useless. It is imperative to invest in modern technology to calculate your RWA. Banks that invest in technology are allowed to use the internal rating-based approach. Hence, there is a need for a sophisticated application as a risk engine to calculate your RWA effectively.

Other challenges includes;

• Inadequate Training: Banks need to invest in their resources. A bank’s staff need knowledge of credit risk and RWA calculation to capture the correct information adequately. Knowledge is vital, and banks need to train their staff to understand the exposure.
• Faulty implementation of regulatory policies and requirements, e.g., Basel Regulations: The regulatory policies keep changing, making it necessary for banks to keep themselves abreast of the recent changes. There are different implementations in different jurisdictions when it comes to capturing the requirements of the Basel committee. Only the so-called members of the Basel committee thoroughly implement the Basel 2, Basel 3, and Basel 4 requirements. However, other jurisdictions or countries should ensure they implement the Basel regulation accordingly or based on the committee requirements as there are so many loopholes. Bank regulators also need to ensure that they implement the correct requirements. If the regulation is incorrect, then the banks’ implementation would be wrong.

There are many challenges to optimizing RWA; however, these are the critical challenges to RWA optimization.