Financial institutions rely on revenue, risk, and other financial models to operate better. This chiefly boils down to data these models provide and their immense potential to lift an institution’s performance notches higher. The reliance on these models is so high that many financial institutions may fail to perform their core tasks without the right ones. Performing everything manually in the banking and financial services industry is impossible, and model risk management services can help manage the excess workload. Model risk management consists of both quantitative and qualitative checks and balances.
Quantitative implications of model risk management
Quantitative implications of model risk management are often linked to revenue loss. One of the main challenges for model risk management is data deficiency. When banking employees don’t have access to complete information, they may make wrong decisions. Models that cannot collect/accumulate complete information may impact revenue. For example, banks often use credit scoring models for distributing mortgages. When scoring models do not have access to complete information, they may approve mortgages for applicants most likely to default. When more and more customers fail to repay loans, the bank will lose revenue.
Age-old financial models are the main reason banks suffer from estimate uncertainties. Estimate uncertainties within models can have several quantitative ramifications. Models that cannot make the right assumptions fail to benefit banks. Revenue models with estimate uncertainties may culminate in losses to banks. The only solution for financial institutions is to use accurate artificial intelligence/machine learning models.
Model risk management focuses on updating models at frequent intervals. Consider a scoring model that hasn’t been updated in the last few years. It means the predictive power of the model is based on old data. It also increases the chances of model misuse within an organization. Financial firms rely on third-party model risk management services to steer clear of such scenarios.
Qualitative implications of model risk management on financial institutions
Banks rely on key performance indicators (KPIs) and other metrics, including qualitative metrics, to measure the efficiency of financial models. To assess the quality of data, a bank should choose the right metrics. Poor-quality data may not help banks make the right decisions.
Poor model documentation is one reason for poor-quality data. With proper model documentation, banks and asset management companies can update models as and when required. Leading qualitative problems in models can be identified from time to time through model documentation and monitoring. Every financial firm must have a dedicated team to monitor the quality of models regularly.
How to reduce qualitative and quantitative loopholes within models
Financial firms may find risk management somewhat tricky. Model risk management covers testing, issue management, and documentation, among others. Financial institutions may not have internal model experts to perform these tasks. Instead of completing risk management processes internally, they can outsource them to a research firm.
Acquity Knowledge Partners provides reliable model risk management services to financial firms and helps them implement a substantial model risk management strategy.
