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In the rapidly changing landscape of artificial intelligence (AI), ensuring compliance with regulatory frameworks is critical. Synthetic data is emerging as a vital tool, offering significant advantages for model validation and regulatory compliance.

In today's technology-driven world, many businesses are incorporating embedded financial services like payments, cards, and lending into their operations to enhance customer experience. However, these businesses often lack banking licenses, necessitating partnerships with licensed banks. This innovative business model known as Banking as a Service (BAAS), has emerged as a catalyst for financial inclusion and a driver of growth in the fintech sector.

In the aftermath of the 2008 financial crisis, the financial industry faced a stark reality: the models that had been used to make critical decisions had failed, leading to devastating consequences. This exposed the significant risks associated with model risk, and regulators worldwide began to implement stricter guidelines for model validation.

I once was responsible for Getting Things Done in a small Fintech. We were on a mission to change the world, enabling more inclusive financial products. Our claim to fame was a unique way of underwriting, we viewed customers, not just through the narrow lens of their credit score, but as comprehensive individuals with inherent potential.