Cortex Labs: Singapore's Explainable AI Pioneer for Financial Services

Date: February 24, 2026

As artificial intelligence becomes increasingly embedded in financial decision-making, a fundamental challenge has emerged: how do we trust algorithms we cannot understand? Enter Cortex Labs, a Singapore-based AI startup that is tackling this problem head-on with its explainable AI (XAI) platform designed specifically for financial services.

The Black Box Problem

Financial institutions have been rapid adopters of AI technologies—from credit scoring algorithms to fraud detection systems and automated trading platforms. However, the complexity of modern machine learning models, particularly deep learning systems, has created what industry experts call the "black box" problem. These models can produce highly accurate predictions while remaining essentially incomprehensible to humans.

"Accuracy alone is not enough when lives, livelihoods, and regulatory compliance are at stake," explained Marcus Tan, Cortex Labs' founder and chief executive officer. "Financial institutions need to understand why an AI system made a particular decision—not just that it made a correct prediction. This is especially critical when those decisions affect whether someone gets a loan, or whether a transaction is flagged as suspicious."

Building Transparent Financial AI

Founded in 2024 by alumni from the National University of Singapore and Singapore Management University, Cortex Labs has developed a proprietary explainable AI framework that can overlay on existing machine learning models to provide real-time explanations for their decisions.

The platform works by generating human-readable rationales for each algorithmic decision. When a loan application is rejected, the system can identify and communicate the specific factors that contributed to that decision—whether it's credit history, income-to-debt ratio, or other variables. For fraud detection, it can explain what patterns triggered a security alert.

Regulatory Tailwinds

Cortex Labs' timing has proven fortunate. Regulators worldwide are increasingly demanding transparency in AI-driven financial decisions. The Monetary Authority of Singapore (MAS) has been particularly active in promoting responsible AI adoption, issuing guidelines that require financial institutions to be able to explain their automated decisions to customers.

"Singapore's regulatory framework has been incredibly supportive of responsible AI innovation," noted Dr. Priya Sharma, Cortex Labs' head of regulatory affairs. "MAS understands that explainable AI isn't just good ethics—it's good business. It builds trust, reduces risk, and actually improves model performance by helping developers understand where their systems are going wrong."

Growing Market Traction

The startup has already secured partnerships with several major financial institutions operating in Singapore, including regional banks and insurance companies. Its client base spans Southeast Asia, with interest from institutions in Malaysia, Indonesia, and Thailand.

The company's recent Series A funding round raised US$12 million from investors including Sequoia Capital's Southeast Asia arm and Singapore's Vertex Ventures. The funding will be used to expand its engineering team and develop industry-specific solutions for wealth management and insurance underwriting.

Part of Singapore's AI Ecosystem

Cortex Labs' growth reflects the maturation of Singapore's AI startup ecosystem beyond the more visible consumer-facing applications. While generative AI and large language models have captured public attention, specialised B2B AI companies like Cortex Labs are quietly building the infrastructure that will power the next generation of financial services.

As AI continues to transform financial services, the ability to explain and justify algorithmic decisions will become not just a regulatory requirement but a competitive advantage. Singapore's position as a global financial centre and AI innovation hub positions companies like Cortex Labs to lead this transformation.

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