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Innovative FinTech firm
Our team performed a validation of the labor market model to be used for credit applications. In addition to technical model validation, we have also advised the firm on the business model.
Increased model risk in AI/ML solutions
AI and ML models provide cutting-edge solutions with potentially large upside. However, they also exacerbate model risk due to their increased complexity. Although standard model validation frameworks can be applied to ML solutions, their specific risks need significant adjustments to the model validation framework.
Additional high-risk elements of ML models:
- Dependence on large quantities of reliable data;
- Potential gender, age, and other biases leading to substantial reputational risk;
- Challenges in implementation, e.g., large computational and database requirements that would prevent efficient deployment.
Specific validation components
Alongside typical validation, our AI/ML validation contains the following specific components:
- Data policy: availability, quality, adequacy/representativeness, biases;
- Actual modeling choices (choice of the ML model and possible alternatives);
- Outcomes’ interpretability;
- Potential biases towards protected attributes and their mitigation. We can help you to find the right balance between fairness and model performance;
- Hyperparameters choice and sensitivity;
- Benchmarking to industry-standard (classification, regression) models;
- Model deployment and operational assessment.
Black box fintech solutions validation
- Probability & Partners has experience in validating Fintech’s “unique selling point” AI and ML solutions.
- For IP-sensitive validation assignments, we execute them without obtaining the full model documentation or source code.
- In such validation assignments, the model is treated as a “black box,” and detailed diagnostics are performed by:
- Applying it to actual or synthetic (artificial) input data
- Building challenger models and comparing model performance
- We also assess your data policy, deployment and operational characteristics, cybersecurity, compliance to regulations such as GDPR and AI Fairness, and other material or reputational risks.
Examples of Assignments
Ambitious Dutch FinTech start-up
Probability & Partners validated the ML model for credit scoring of the fintech start-up firm. We structured the validation as a “black-box” exercise to safeguard intellectual property. We also reviewed the business model given the ML model performance.
Research
Kevin Rojer: The age of AI in risk management
The financial services industry stands on the verge of a profound transformation, driven by the rapid evolution of artificial intelligence (AI). Once seen as futuristic, AI is now becoming a cornerstone for businesses that want to enhance their decision-making processes, improve operational efficiency, and remain competitive.
Contact
Link to: Svetlana Borovkova
Svetlana Borovkova
Head of Quantitative Modeling
Probability & PartnersLink to: Pim Poppe
Pim Poppe
Managing Partner
Probability & Partners