Let’s skip the hype. You don’t need a chatbot, a dashboard, or a “smart assistant” unless you know why it matters. The hardest part of building AI in finance isn’t the algorithms – it’s the decision of what to build first.

This guide breaks down a practical, no-fluff method to prioritize features in AI-driven finance tools – whether you’re launching a new product or optimizing internal ops.

Start with the Core Use Case: What Are You Automating?

Before writing any code, ask: “What job does this AI replace, augment, or speed up?”

In financial workflows, these are the usual categories:

• Risk evaluation (credit scoring, portfolio risk, fraud flags)
• Forecasting (pricing, market demand, revenue)
• Classification (document sorting, contract analysis)
• Recommendation (investment suggestions, upselling)

If your product touches more than one, pick one to build around first. AI products fail when they try to “do everything” out of the gate.

At S-PRO, when building an AI platform for the German VC firm Earlybird, the starting point was clear: reduce time spent on manual due diligence. That defined everything from architecture to UI.

Prioritize by Decision Impact, Not Frequency

A common mistake? Chasing what happens most often. Instead, chase what has the biggest consequence.

For example:

• Misclassifying a document = minor delay
• Misjudging startup risk = wasted €10M

In the Earlybird case, the focus went straight to predictive risk scoring. S-PRO helped map historical deal outcomes to train models that flag high-risk profiles early. That feature drove ROI – more than any UI enhancement would have.

So ask: What mistake are we afraid of? Build prevention or alerting around that.

Sequence: Internal First, External Later

If your product serves both employees and customers (e.g., investment analysts + LPs), don’t start with the customer portal. Start with what analysts need to work faster or better.

Example sequence:

1. Data pipeline and ingestion (ETL)
2. Internal dashboard with risk scoring
3. Workflow tools: comments, approvals
4. Customer-facing summaries or reporting

This avoids wasting time on UX polish when your core logic isn’t reliable yet.

Decide Build vs. Buy: Spend Time Where You Create Edge

You don’t need to build everything.

Buy or license tools for:

• Data storage (PostgreSQL, Snowflake)
• Pre-trained models (Hugging Face, OpenAI APIs)
• Visualization (Metabase, Power BI)

But build your own:

• Feature engineering pipelines for your data
• Risk scoring or forecast models tied to your vertical
• UX flows that match your analyst process

Companies like Earlybird gained speed by focusing engineering time only where it differentiated them from other funds.

Validate Early With Non-Developers

Don’t wait for v1. Show logic and mocks early.

• Use Figma or Miro to simulate scoring flows
• Create Google Sheets with fake output to spark feedback
• Demo results with past deals (e.g., “What if we ran this model in 2021?”)

Why it matters: analysts will spot deal breakers in your assumptions that AI engineers might miss.

This is how web development companies working in fintech avoid expensive pivots post-launch.

Plan for Auditability (Or Fail Fast Later)

You’ll need to explain how your model works – to compliance, to investors, to the business.

From day 1, capture:

• Versioned model inputs and outputs
• Feature importance rankings
• Logic path (especially in ensemble models)

This isn’t just good hygiene. It keeps your project alive when stakeholders ask: “Why did we approve that deal?”

Final Thought: Think Like an Investment Committee

Prioritizing AI features is like prioritizing investments. Start with:

• Risk-adjusted return: What gives us the biggest lift for the least complexity?
• Strategic alignment: Does this feature reinforce what we want to be known for?
• Timeline to insight: How quickly can we use this in a real decision?

Earlybird’s AI platform didn’t start with bells and whistles. It started with a clear pain point and ruthless focus. Earlybird built what mattered first – and skipped what didn’t.