Projects
Open Credit Scoring is developing open research, technical frameworks, and governance models for trustworthy AI-driven credit and underwriting systems.
Our projects focus on advancing causal AI, transparency, fairness, and institutional trust in high-stakes financial decision-making.
Open Credit Scoring Initiative
The Open Credit Scoring Initiative explores how open standards, causal AI, and transparent governance can help modernize credit scoring and underwriting systems.
The initiative aims to create an open technical foundation for next-generation financial AI systems.
Key focus areas include:
Causal AI for Credit Underwriting
This project explores how causal inference and causal Bayesian networks can augment traditional machine learning systems used in credit underwriting.
Rather than relying solely on statistical correlations, causal models attempt to explicitly represent relationships between key financial and decision variables.
This work investigates how causal AI may help financial institutions better distinguish between legitimate predictive signals and potentially discriminatory effects.
Causal Fairness and Antidiscrimination Research
This research project focuses on applying causal models to questions of fairness, discrimination, and compliance in financial AI systems.
The project also explores how causal models may provide a more operational framework for translating antidiscrimination principles into system design constraints.
Alternative Data and Causal Debiasing
Alternative data has the potential to improve financial inclusion, but it also introduces new risks related to proxy discrimination, digital redlining, and opaque decision-making.
This project explores how causal inference techniques may help evaluate alternative data sources more rigorously.
The goal is to develop more trustworthy approaches for evaluating whether alternative data has a legitimate causal relationship to creditworthiness.
Systems Thinking and Financial AI
Most AI systems are optimized primarily for short-term prediction.
This project explores how systems thinking and system dynamics can help model long-term feedback effects within financial systems.
The objective is to move beyond static AI models toward systems capable of simulating long-term economic and social outcomes.
Open Standards for Trustworthy AI
Open Credit Scoring supports the development of open technical standards for high-stakes AI systems.
We believe open standards are essential for creating trusted and interoperable AI ecosystems in finance.
Future Directions
Open Credit Scoring is exploring future research directions including:
Financial AI needs more than better prediction
We believe the future of financial AI depends not only on better prediction, but on better governance, transparency, and institutional trust.