emPower - PRYZM

Project Snapshot

• Company: Lumiq.ai
• Role: UX Designer
• Domain: Fintech • Risk Intelligence • Enterprise Data Platforms
• Product: https://pryzm.ai/features/
• Figma: https://www.figma.com/design/XnUxffs8eGPi9kHVAxLgnT/Pryzm--emPower?node-id=0-1&t=TTgWY6CYgeMsgSm9-1

Overview

Financial institutions operate in high-risk, high-compliance environments where delayed or incorrect decisions can result in significant financial loss. PRYZM was designed as an AI-powered decision intelligence platform that enables financial organizations to: - Detect emerging risks - Monitor operational performance - Identify revenue opportunities - Make faster, data-backed decisions My role as a product designer was to transform complex financial data into clear, trustworthy, decision-ready insights for non-technical stakeholders.

Domain Context

Unlike consumer fintech (payments or lending), PRYZM operates at the decision and risk layer of financial services. It supports: - Risk monitoring - Financial performance tracking - Compliance visibility - Operational intelligence This makes it comparable to platforms like: - BlackRock Aladdin - Moody’s Analytics - FICO Decision Systems

Problem

Business Problem

Financial institutions faced: - Fragmented data across multiple systems - Delayed reporting cycles - High dependency on analysts - Reactive rather than proactive risk management Leadership often received insights after financial impact had already occurred.

User Problem

Business & Risk Leaders

• Needed fast answers for financial decisions
• Found dashboards too complex
• Wanted clear "What changed and why?"

Analysts

• Spent most time cleaning and validating data
• Had limited time for strategic analysis

Data Teams

• Managed pipelines but lacked visibility into business impact

Design Challenge

How might we enable financial decision-makers to detect risks and opportunities early through simple, explainable, AI-driven insights?

UX Research

Methods

• Stakeholder workshops with product and finance leaders
• User interviews with risk, operations, and analytics teams
• Workflow mapping of financial decision cycles
• Usability testing of existing BI and reporting tools

Key Insights

• Financial leaders ignored dashboards that did not explain drivers behind changes.
• Risk signals existed in data but were buried in reports.
• Analysts were bottlenecks in the decision process.
• Users trusted insights only when data sources were visible.

Design Strategy

We repositioned PRYZM from a reporting tool to a Fintech Decision Intelligence System. ### Core Principles - Explainability - Risk-first prioritization - Decision-ready summaries - Transparency & trust - Actionable intelligence

Solution

1. AI-Powered Insight Feed

Instead of static dashboards, PRYZM surfaces: - Risk spikes - Performance drops - Customer behavior shifts - Revenue leakage indicators Each insight clearly answers: - What changed - Why it changed - Financial impact - Confidence level

2. Risk & Impact Prioritization

Insights ranked by: - Financial impact - Risk severity - Business urgency This helped leaders focus on what requires immediate action.

3. Explainable AI Layer

To address fintech trust requirements, we introduced: - Data lineage visibility - Contributing factors - Confidence indicators - Model reasoning summaries This significantly improved credibility among risk teams.

4. Guided Financial Exploration

Instead of complex filters, users got: - Step-based drill-downs - Contextual comparisons - Trend explanations This reduced dependence on analysts for everyday decisions.

5. Decision & Action Layer

Each insight connected to: - Recommended actions - Scenario simulations - Ongoing performance tracking PRYZM moved from insight display to decision enablement.

Outcomes

Business Impact

• Faster financial decision cycles
• Reduced manual reporting effort
• Improved proactive risk detection
• Better cross-functional alignment between business and data teams

User Impact

• Business users gained self-serve insights
• Analysts shifted to strategic work
• Leadership trusted AI recommendations more due to explainability

Challenges

Designing for Financial Risk Environments

Financial users require: - High accuracy - Transparency - Auditability Solution: We embedded data sources, timestamps, and confidence indicators into the experience.

Multi-User Ecosystem

Different mental models across: - Leadership - Risk teams - Analysts - Data teams Solution: Role-based views with a shared intelligence layer.

Key Learnings

• Fintech UX is about decision confidence, not visuals
• Explainability is essential for AI adoption
• Prioritization reduces cognitive overload
• Trust drives enterprise product adoption
• Systems thinking is critical in data-heavy environments

Case Study Closing

PRYZM transformed fragmented financial data into a proactive decision intelligence system, enabling financial institutions to detect risk earlier, respond faster, and operate with greater confidence.