Financial institutions operate in a highly regulated, data-sensitive environment. At the same time, these institutions are under pressure to modernize credit scoring models that balance fairness, accuracy and compliance.
The challenge? Most financial institutions lack access to the volume and diversity of high-quality, privacy-safe data needed to fuel these models.
This is where synthetic data – algorithmically generated data that mimics real-world data – can provide a powerful alternative for assessing creditworthiness.
Why banks need synthetic data now
Traditional credit scoring models rely heavily on historical data, which often lacks diversity and overlooks underbanked or emerging customer segments. At the same time, privacy regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) limit how real customer data can be used, making innovation risky and compliance more complex.
Synthetic data offers a way forward. Because it doesn’t map to real individuals or firms, it can be safely used to train AI models without violating GDPR, HIPAA or other data protection laws. This advantage makes synthetic data especially attractive in highly regulated industries like financial services and health care.
For banks, synthetic data makes it possible to:
- Simulate borrower behavior across a wide range of economic scenarios.
- Train and test models without exposing sensitive customer information.
- Mitigate bias by generating representative data for underserved groups.
- Accelerate innovation by removing data access bottlenecks.
In practice, this means banks can generate more robust borrower profiles, address gaps in scarce or missing data and reduce historical bias in model training – ultimately enabling fairer and more inclusive lending decisions.
Synthetic data also strengthens resilience by helping institutions model credit risk under different economic conditions and stress-test more effectively.
Expanding credit access and closing data gaps
For banks aiming to expand credit access, synthetic data offers a way to see the full financial picture of every applicant – even when traditional credit histories are missing.
Banks can use synthetic data to simulate nontraditional credit indicators, such as transaction behavior or utility payments. Many credit scoring models only capture certain types of debt, like loans and credit cards, but omit other financial behaviors, such as paying rent, utilities or phone bills on time. These transactions often go unreported in traditional credit data and are not included in traditional credit scoring, leaving an incomplete picture of a borrower’s reliability.
Synthetic data is particularly valuable for customers moving from one country to another who have little to no domestic credit history. Even if they have a strong financial track record in their home country, traditional models often fail to account for it.
When traditional credit scoring models lack sufficient data, applicants may face lower credit limits or outright denials. Foreign-born residents – who make up nearly 14% of the US population – usually arrive as “credit-invisible” no matter how strong their financial history may have been back home. Synthetic data helps fill these gaps, supporting fairer lending decisions and greater inclusion.
By enabling banks to model these alternative data points securely, synthetic data allows institutions to innovate while maintaining customer trust and regulatory compliance. Embracing synthetic data today means banks and other FIs can drive smarter, fairer lending while staying ahead in a rapidly evolving, regulated market.
