AI-Powered Financial Crime Prevention
Overview
SterlingBankfacedmountingfinanciallossesfromsophisticatedfraudattacks.Theirreactiveapproachmeantfraudwasonlyinvestigatedaftercustomerfundswerecompromised,resultinginsignificantlegalcosts,customerdissatisfaction,andregulatoryscrutiny.
IdesignedanddevelopedanAI-poweredfrauddetectionplatformthatmonitorscustomerbehaviorpatternsinreal-time,transformingthebank'sapproachfromreactiveinvestigationtoproactiveprevention—allwhilemaintainingaseamlesscustomerexperience.
Roles & Responsibilities
Role
Solo UX Engineer
Research, Design, Frontend Development
Team
Cross-functional (5)
Data analytics, fraud specialists, engineers
Market
Nigeria
Fintech, Banking Security
The Challenge
Rising Fraud Rates
Sophisticated fraud attacks were increasing in frequency and complexity, with traditional rule-based systems unable to detect evolving fraud patterns.
Reactive Approach
Fraud was only investigated after occurrence, leading to customer fund losses and damaged trust in the bank's security measures.
Analyst Overload
High false positive rates created alert fatigue, with analysts overwhelmed by investigating legitimate customer activities flagged as suspicious.
Limited Visibility
Fragmented data sources made it difficult for analysts to get a complete picture of customer behavior and transaction patterns.
How might we proactively prevent fraud while maintaining a frictionless experience for legitimate customers?
Solution
I designed a comprehensive fraud analytics platform that combines AI-powered behavioral analysis with human expertise. The system learns normal customer patterns and flags anomalies in real-time, enabling analysts to prevent fraud before it impacts customers.
Solution Overview: AI-Powered Detection Interface
Design Process
1. Research & Discovery
I conducted interviews with fraud analysts, reviewed hundreds of fraud cases, and analyzed the existing detection workflow to understand pain points and opportunities.
User Research: Analyst Interviews & Workflow Mapping
Key Insights
- Analysts spent 70% of their time investigating false positives
- Context switching between multiple systems reduced efficiency by 40%
- Lack of behavioral patterns made it difficult to distinguish fraud from normal activity
2. Ideation & Wireframing
Working closely with fraud analysts, I created wireframes that prioritized actionable insights and reduced cognitive load through progressive disclosure of information.
Early Wireframes: Dashboard Layout
Alert Detail View Wireframes
3. Testing & Iteration
I conducted usability testing with 8 fraud analysts, iterating on the design based on their feedback. The final design reduced average investigation time from 15 minutes to 4 minutes.
Key Features
Real-Time Behavioral Analysis
The AI engine monitors every transaction against learned behavioral patterns, instantly flagging anomalies while explaining the reasoning behind each alert.
Feature: Real-Time Detection Dashboard
Intelligent Alert Prioritization
Machine learning ranks alerts by risk score and confidence level, ensuring analysts focus on the most critical threats first.
Feature: Prioritized Alert Queue
Unified Investigation Workspace
All customer data, transaction history, and behavioral insights consolidated in a single view, eliminating context switching and reducing investigation time.
Feature: Investigation Workspace
Impact
The fraud analytics platform transformed Sterling Bank's approach to financial crime prevention, delivering measurable improvements across multiple dimensions while enhancing both analyst efficiency and customer trust.
Successful fraud attempts decreased dramatically through proactive detection and prevention.
Combined savings from prevented fraud, reduced investigation costs, and lower legal fees.
Analysts reported significantly improved workflow efficiency and job satisfaction.
Machine learning dramatically reduced alert fatigue by filtering out legitimate activities.
This platform has completely changed how we work. We're now preventing fraud before it happens, not just reacting to it. The time savings alone have been incredible.
Key Learnings
Design for Expertise, Not Simplification
Fraud analysts are domain experts who need powerful tools, not oversimplified interfaces. The key is organizing complexity through clear information hierarchy and progressive disclosure, not hiding it.
AI Transparency Builds Trust
Showing analysts why the AI flagged something as suspicious (behavioral patterns, risk factors) was crucial for adoption. Black box AI decisions were met with skepticism and resistance.
Context is Everything
Reducing context switching by consolidating information had a bigger impact on efficiency than any individual feature. Analysts needed to see the full picture in one place.
Iterate with Real Cases
Testing with actual fraud cases, not hypothetical scenarios, revealed critical edge cases and workflow needs that wouldn't have been discovered otherwise.