The widespread use of machine learning (ML) in financial institutions (FIs) has raised significant concerns about the risks associated with these applications. According to a survey conducted by the Bank of England and the Financial Conduct Authority, biases embedded in data, algorithms, and outcomes of ML applications are the primary risk. Furthermore, deploying biased or inaccurate ML applications could lead to unfair, unethical, or discriminatory decisions or actions that may harm customers and have legal consequences for FIs.
As FIs transition from using deterministic rules-based compliance applications to more complex ML applications, the behavior of the latter can be difficult to understand and interpret. Appropriate governance around compliance applications, which is already challenging, is an area that requires further attention to keep a record of changes and testing results. FIs must also maintain scrupulous records of training data used and how, when, and on what the model was trained.
In conclusion, while the risks of ML applications are very similar to those FIs must manage for non-ML applications, new risks, such as training data bias, must be understood and appropriately addressed. FIs must ensure that their governance models can be tailored accordingly, and good practices around application governance can be applied to ML applications.
Frequently Asked Questions (FAQs) Related to the Above News
What is the main risk associated with implementing machine learning in financial institutions?
The primary risk associated with implementing machine learning in financial institutions is the potential for biases embedded in data, algorithms, and outcomes of ML applications.
What are the potential consequences of deploying biased or inaccurate machine learning applications in financial institutions?
Deploying biased or inaccurate machine learning applications in financial institutions could lead to unfair, unethical, or discriminatory decisions or actions that may harm customers and have legal consequences for FIs.
Why is it challenging for financial institutions to govern compliance applications that use machine learning?
It is challenging for financial institutions to govern compliance applications that use machine learning because the behavior of these applications can be difficult to understand and interpret. Additionally, FIs must maintain scrupulous records of training data used and how, when, and on what the model was trained.
Are the risks associated with machine learning in financial services similar to those for non-ML applications?
Yes, the risks associated with machine learning in financial services are very similar to those for non-ML applications. However, new risks, such as training data bias, must be understood and appropriately addressed.
What should financial institutions do to ensure the appropriate governance of machine learning applications?
Financial institutions should ensure that their governance models can be tailored accordingly and that good practices around application governance can be applied to machine learning applications. Additionally, they must maintain scrupulous records of training data used and how, when, and on what the model was trained.
Please note that the FAQs provided on this page are based on the news article published. While we strive to provide accurate and up-to-date information, it is always recommended to consult relevant authorities or professionals before making any decisions or taking action based on the FAQs or the news article.
