Fraud Ops / AI Monitoring / FinTech UX

AI Fraud Detection & Transaction Monitoring

I designed a risk operations workbench that cuts through alert fatigue, prioritizes high risk anomalies, and accelerates complex fraud investigations for a high volume payments platform.

-60%

False Positive Noise

Faster Triage

€4.5M

Fraud Prevented

100%

SLA Compliance

Role

Lead Product Designer

Timeline

5 Months

Focus Area

Risk Ops, Interaction Design, Prototyping

Risk operations

Investigation lens: from alert noise to a single narrative

Analysts do not need more charts—they need a sequenced story that connects entities, velocity, and model rationale. This module previews how the workbench compresses five tools into one guided surface.

Signal graph Explainable scores SLA clock

Triage queue

Dynamic ranking surfaces the next best case to open based on model confidence, customer impact, and regulatory clock—not arrival order.

Entity fabric

Devices, IPs, beneficiaries, and counterparties collapse into one graph so analysts spot rings without tab gymnastics.

Live preview

Prioritized alert stack

Within SLA

ATO · velocity + device drift 94

Layering · shared beneficiary 88

Low signal · recurring merchant 41

Illustrative ordering for storytelling; production scoring combined rules, graph features, and analyst feedback loops.

The Challenge: The Alert Fatigue Crisis

When I joined the risk operations team, our payments platform processed millions of transactions daily. The legacy rules based monitoring system generated thousands of alerts every shift, but 85% of them were false positives.

Drowning in Noise

Fraud analysts suffered from severe alert fatigue. To investigate a single suspicious transaction, they had to jump between five different legacy tools to piece together a user login history, device IDs, and transaction patterns.

This fragmented workflow caused massive investigation bottlenecks and created a high risk of missing actual account takeovers or money laundering layering. I needed to design a system that understood the relationship between variables, not just rigid thresholds.

The Cost of Fragmented Tools

False Positive Rate 85%

Time Spent Gathering Data 70%

Time Spent Analyzing Risk 30%

Strategic Vision: Signal Over Noise

The Gap

Machine learning models identified complex fraud rings, but analysts could not interpret the raw scores. The UI lacked the narrative needed to explain why an action was risky.

The Strategy

I designed a centralized workbench that translated black box AI scores into transparent, actionable insights. This empowered analysts to make fast, accurate decisions with full context.

Contextual Data Explainable AI Rapid Disposition

UX Research: The Fragmented Investigation

To understand the root cause of the triage delays, I embedded myself with the risk operations team, shadowing senior fraud analysts during live shifts.

Research Scale

Analyst Shadowing Sessions

Workflow Iterations

Data Science Workshops

Discovery Phases

Analyst Workflow Mapping

I mapped their exact investigation workflow, noting every time they had to switch tabs, copy paste an IP address, or manually cross reference a device ID against a blocklist.

Model Transparency Workshops

I collaborated heavily with the data science team to understand how the new anomaly detection models worked and how to surface explainable risk signals.

Prototyping and Validation

Evaluated low fidelity layouts with analysts to ensure the AI risk scoring was transparent and mapped directly to UI components.

The cognitive load on the risk ops team was immense. I observed that analysts spent the majority of their time hunting for data rather than analyzing it. The investigation bottlenecks were a direct result of poor information architecture.

We needed to ensure the AI risk scoring was transparent. By mapping machine learning outputs directly to UI components, I enabled analysts to instantly see why a transaction was flagged, turning a black box into a clear narrative.

Key Research Insight

"Analysts are not struggling to make decisions; they are struggling to find the data required to make them. If we can surface the AI reasoning alongside the user historical timeline, we can cut triage time in half."

- Senior Fraud Analyst

This insight drove the core design philosophy: prioritize data synthesis over data presentation.

[SCREEN SLOT 02: Analyst workflow map or user journey]

AI Strategy: Designing for Transparent Risk

I shifted the experience from a chronological alert feed to a dynamic, AI assisted prioritization queue. The system scored alerts from 1 to 100, floating high risk items to the top.

Dynamic Prioritization

Instead of first in, first out, I designed the queue to dynamically reorder based on real time risk scores, ensuring analysts always tackled the highest threat items first.

Explainable Tags

I included clear risk tags for each row, allowing analysts to instantly understand the context before opening the case.

Entity Resolution

I mapped connections between accounts sharing IP addresses or funding sources, grouping related alerts to uncover entire fraud rings at once.

Final Design: The Risk Operations Workbench

I designed a unified workbench that brought all necessary context into a single view, allowing analysts to focus entirely on decision making.

Machine Learning

Signal Weighting

I designed visual indicators that mapped directly to the model signal weights, showing analysts exactly which variables drove the high risk score.

Analytics

Behavioral Baselines

I implemented comparative charts that highlighted deviations from the user historical baseline, making anomalies instantly recognizable.

Automation

Auto Generated Notes

I created a system that translated the AI findings into draft case notes, saving analysts valuable time on documentation and data entry.

[SCREEN SLOT 04: Prioritized alert queue]

The Smart Alert Queue

I redesigned the dashboard to replace the noisy chronological feed. By utilizing a split pane layout, analysts could click an alert and immediately see the AI confidence score and the primary risk factors without losing their place in the queue.

Severity based sorting ensures high risk items are handled first.
Inline quick actions for obvious false positives.

Suspicious Transaction Timeline

To help analysts spot account takeover patterns, I designed a visual timeline. It plotted the sequence of user events, making it immediately obvious when a password reset was followed by a new device login and a sudden large crypto purchase.

Visualizes the user journey leading up to the anomaly.
Highlights deviations from the user historical baseline.

[SCREEN SLOT 05: Suspicious transaction timeline]

[SCREEN SLOT 06: Customer risk profile and linked signals]

Linked Signals & One-Click Disposition

I integrated an anomaly panel that mapped connections between accounts. I streamlined the case review workflow by adding one click disposition buttons. To save time on documentation, I designed the system to auto generate draft case notes based on the AI findings.

Auto generated case notes reduce manual data entry.
Graph visualization uncovers hidden fraud rings.

Engineering Handover

Bridging Design and Data Science

To ensure the explainable risk signals were technically feasible, I collaborated closely with the data science and engineering teams. We established clear API contracts to define how model weights would be passed to the frontend components without impacting performance.

API Contracts Component Specs Data Mapping Storybook Assets

Zero

Implementation Delays

Results & Achievements

Empowered Analysts, Protected Revenue