This title slide is titled "Fraud Detectors: A Design Thinking Approach." Its subtitle describes applying the stages—Empathize, Define, Ideate, Prototype, and Test—to enhance fraud detection systems.

Fraud Detectors: A Design Thinking Approach

Applying Empathize, Define, Ideate, Prototype, and Test to Enhance Systems

Source: Classroom Lecture on Design Thinking for Fraud Detection

This slide, titled "Empathize: Understanding the Human Side," advocates interviewing users, victims, and stakeholders to grasp fraud's human impact. It calls for mapping pain points, highlighting ethical dilemmas in detection, and focusing on emotional costs.

Empathize: Understanding the Human Side

• Interview users, victims, and stakeholders
• Map pain points from fraud impact
• Highlight ethical dilemmas in detection
• Focus on emotional and human costs

Source: Design Thinking for Fraud Detection

This slide outlines key challenges in fraud detection, such as evolving fraud tactics outpacing systems and data overload hindering analysis. It also addresses balancing privacy versus security demands and the lack of clear problem statements.

Define: Key Challenges in Fraud Detection

• Evolving fraud tactics outpace detection systems
• Data overload hinders effective analysis
• Balancing privacy versus security demands
• Lack of clear problem statements

The "Ideate: Brainstorming Solutions" slide features a grid of four fraud prevention ideas: AI/ML predictive models to forecast threats, behavioral analytics to spot user deviations, anomaly detection for unusual activities, and cross-sector collaboration for shared intelligence. These tools aim to proactively anticipate, detect, and collectively defend against evolving fraud risks.

Ideate: Brainstorming Solutions

{ "features": [ { "icon": "🧠", "heading": "AI/ML Predictive Models", "description": "Forecast fraud using machine learning to anticipate evolving threats." }, { "icon": "📊", "heading": "Behavioral Analytics", "description": "Track user patterns to spot deviations indicating fraud attempts." }, { "icon": "🔍", "heading": "Anomaly Detection", "description": "Algorithms identify unusual activities signaling potential fraud risks." }, { "icon": "🤝", "heading": "Cross-Sector Collaboration", "description": "Share intel across industries for stronger, collective fraud defense." } ] }

The slide outlines a four-step workflow for prototyping and refining fraud detection models, beginning with sketching system architecture and developing MVP simulations using synthetic data. It then covers gathering feedback from stakeholders on usability and ethics, followed by iterating to enhance accuracy, reduce biases, and improve adaptability.

Prototype: Building & Refining Models

{ "headers": [ "Step", "Key Activities", "Fraud Detection Focus" ], "rows": [ [ "Sketch System Architecture", "Diagram high-level components: data ingestion, ML models, alerting.", "Outline fraud signals like anomalous transactions and user behaviors." ], [ "Develop MVP Simulations", "Build basic prototypes with sample data for real-time detection.", "Simulate fraud scenarios using synthetic datasets." ], [ "Gather User Feedback", "Share prototypes with stakeholders, victims, and experts.", "Assess usability, false positives, and ethical concerns." ], [ "Iterate on Prototypes", "Refine models based on input, retrain algorithms.", "Improve accuracy, reduce biases, enhance adaptability." ] ] }

Source: Fraud Detection Workflow - Design Thinking Prototype Stage

The "Test: Evaluating Performance" slide highlights 95% detection accuracy with a <2% false positive rate. It also reports a 9.1/10 adaptability score and 92% user trust rating from surveys.

Test: Evaluating Performance

• 95%: Detection Accuracy

Achieved 95%+ precision rate

• <2%: False Positive Rate

Minimized erroneous alerts

• 9.1/10: Adaptability Score

Effective against evolving threats

• 92%: User Trust Rating

From satisfaction surveys

The conclusion slide summarizes key takeaways: Design Thinking transforms fraud detection with applications in banking and e-commerce, while empathy drives innovation. It recommends next steps like real-world pilots and ends with "Thank you!"

Conclusion: Key Takeaways

• Design Thinking transforms fraud detection

• Applications: banking, e-commerce
• Reflect: Empathy drives innovation
• Next steps: Real-world pilots!

Thank you!

Source: Fraud Detectors through Design Thinking