Why trusted data makes ethical AI possible

Tom Mallon is data partner manager at Fintech Sandbox, helping fintech startups access high-quality data through its Data Access Residency program.

Every ethical AI model starts with one essential ingredient: clean, trusted data. The FR editors sat down with Fintech Sandbox’s Tom Mallon to discuss why access to reliable financial data isn’t just a technical advantage, but a prerequisite for building AI systems that are fair, transparent and built to last.

Why is clean, trusted financial data such a critical building block for AI systems that are both effective and ethical?

Clean data is not just a technical requirement. It’s an ethical necessity. Access to clean data ensures programming models are accurate and deliver quality results. I’ve seen this completed successfully for predictions around credit risk, portfolio optimization and guaranteeing compliance through regulatory bodies. 

Organizations that are empowered with trusted, reliable data will help systems to operate fairly and ethically as high-quality data reduces the chance of hidden biases, like categorizing certain demographics incorrectly. It’s also key to helping a brand build trust with customers, investors, and regulators by showing that its AI isn’t a “black box” built on a shaky foundation, but a transparent, dependable system grounded in accuracy and reliability.

How does high-quality financial data help mitigate risks such as bias or unethical decision making in AI applications?

High-quality data must be the cornerstone of AI to eliminate the risk of hallucinations — when a model generates false or misleading information — and to ensure a clear audit trail back to the trusted source. With bias detection systems, data can even help fix its own errors, such as demographic bias in lending or insurance models, while ensuring strong safeguards remain in place.

Metadata including lineage, source, and auditability will often accompany high-quality data, which allows models to provide the clarity and reasoning behind outputs. This transparency is essential for developing ethical AI systems. 

On the other hand, if poor data quality is introduced, it can amplify errors across financial networks, creating ripple effects in areas such as reporting, compliance and operations.

High-quality data lowers the chance of models making socially or economically harmful recommendations and reduces systemic risk, leading to more equitable and accurate outcomes.

Through the Data Access Residency, what types of financial data are available for startups, and how do different datasets enable different kinds of growth and AI innovation?

Startup founders need high-quality financial data to build and test their fintech AI applications. 

Providing free, high-quality access to this data is exactly why Fintech Sandbox exists. “Sandboxed” datasets let founders test feasibility without commercial or regulatory risk. For AI, as models mature through training, they require real, granular and compliant data to prove commercial value. Access to datasets at no cost gives innovative startups more opportunity to move from idea to prototype and, eventually, to product.

When data is restricted or expensive, many startups stall at the proof-of-concept stage and can’t demonstrate trustworthiness or scalability. In order for startups to accomplish the move from scaling to market leadership, they require wide and ongoing access to fresh financial data that lets them refine models, automate more processes and compete with incumbents. 

Having experienced this firsthand at a freight-tech startup building a SaaS platform, I wish we had had access to free, high-quality data: it would have let us move and build faster. At Fintech Sandbox, creating this free data repository helps foster an environment where more fintech solutions can reach the market to solve big problems.

What role does data access play in helping startups move from idea and experimentation to scaling their AI programs successfully?

At Fintech Sandbox, we value our work, which lowers the barriers many early-stage entrepreneurs face as they build their businesses. One of the main hurdles is gaining entry to a data sandbox, a costly resource often out of reach for founders early in their company’s journey. Through the Data Access Residency (DAR), we provide free access to high-quality financial data from more than 40 data partners.

The founders we work with benefit from access to a range of data types, including financial market data across all asset classes, alternative data, credit scores and ratings, employment and demographic information, and even personal finance and banking data. Each component helps entrepreneurs improve AI models and ensure accurate outputs.

Creating accessible data for growing startups has come full circle. In some cases, founders who gained access to the DAR became successful and chose to offer the same opportunity to the next generation of entrepreneurs, partnering with us as new data providers. Recent examples include our work with Kaleidoscope, an AI-driven securities research platform; MarketReader, which produces financial market data; and ConsciESG, which provides ESG scoring for investment analysis. All three began as DAR members and are now data partners, paying it forward to other fintech founders.

What are some of the most exciting or surprising ways you’ve seen financial data being used to fuel growth in AI?

Our startup members are using data in many ways to drive AI growth. A few stand out:

• Personalized financial coaching: AI-powered “financial wellness assistants” that recommend budgeting, investing, or savings strategies in real time, using granular transaction data.

• Investment decision efficiencies: Applying AI to manual tasks like data gathering and normalization to improve the speed and accuracy of investment decisions.

• Fraud prevention and compliance automation: Using trusted datasets to train models that detect subtle shifts in transaction sequences or spending patterns, spotting risks like money laundering faster than traditional rules-based systems.

These are just a few examples. We’re also seeing growth in credit scoring and climate finance or ESG applications. As AI evolves, the demand for high-quality data will continue to grow, shaping how the next generation of fintech solutions will be built and scaled.