AI in Product Development

Building AI features in financial products comes with unique challenges. Here are some lessons from the trenches.

Trust is Everything

In finance, users need to trust the system. AI that feels like a black box won't be adopted. We invest heavily in explainability—showing users why the system made a particular recommendation.

Start Small

It's tempting to build a sophisticated ML system from day one. But simpler models that you understand completely often outperform complex ones that you don't.

Human-in-the-Loop

AI should augment human decision-making, not replace it. The best outcomes come from combining algorithmic efficiency with human judgment.

Measure What Matters

Accuracy is just one metric. In production, you also care about:

• Latency and reliability
• User trust and adoption
• Edge case handling
• Bias and fairness

The 80/20 of ML Products

Most of the value comes from:

• Good data pipelines
• Simple, robust models
• Clear user interfaces
• Fast iteration cycles

The fancy stuff matters less than getting the basics right.