Credit Risk Management
Traditional credit risk management involves the use of both qualitative and quantitative inputs, coming from several internal and external sources, and ultimately, it requires human judgment and overall conformity to the internal policies and risk appetite of the financial institution, as well as the observance of regulatory requirements.
As such, historical data can carry different types of biases, sometimes human-driven (consciously or unconsciously), sometimes simply derived from historical market conditions or prior internal policies.
With so many layers of complexity, ensuring a robust, fair, and accurate credit process, compliant with the upcoming regulatory requirements (EU AI Act) can be a real challenge for financial institutions, hampering efforts to achieve healthy and sustainable growth.
The benefits of using AI in the credit risk process are undeniable, leading to a more granular approach, which results in a better overall distribution of risks.
Artificial intelligence can allow shifting from a single transaction-based process to a more holistic view of the overall risk position and exposure which improves cost/income ratios, cost of capital, and risk-weighted assets distribution. Furthermore, it can allow to proactively monitor risk as new information becomes available.
Tackling fairness and ethical aspects is made also possible, by focusing on the quality of the data and the way they interact with AI models.
Modulos Data-Centric AI approach is a powerful tool to address existing unbalances in the data used for credit risk management, and produce fair, yet accurate credit decisions.
Using our platform, with its feedback loop between data and model, financial institutions can overcome limitations and shortcomings of the traditional approaches to bias mitigation, simplifying regulatory compliance, and achieving their objectives faster and more efficiently.