Dragon's Den: Exercise 3

The Opportunity

Implement AI-powered fraud detection across online transactions, returns, and loyalty program abuse.

The Business Case

Problem

• $8M annual loss to various fraud types:
  • Transaction fraud: $3M
  • Return fraud (wardrobing, receipt fraud): $4M
  • Loyalty program abuse: $1M
• Manual review catches only 30% of fraud
• False positives frustrate legitimate customers (5% of transactions flagged)
• Organised retail crime increasing

Solution

• Real-time ML models analysing transaction patterns
• Multi-signal fraud detection (purchase, return, account behaviour)
• Automated risk scoring and blocking
• Investigation case management

Investment Requirements

Total Cost: $650,000

• Fraud detection platform: $200,000/year
• Implementation & model training: $250,000
• Integration (payment, CRM, return systems): $150,000
• Operations & tuning: $50,000/year

Timeline: 6 months to deploy, 12 months to optimize

Expected Returns

Risk Reduction (Primary)

• Improve fraud detection from 30% to 55% (conservative target)
• Prevented fraud losses: $8M × (55% - 30%) = $2M/year

Cost Reduction

• Reduce manual review time by 45% (conservative)
• Labour savings: $150,000/year

Revenue Protection

• Reduce false positives from 5% to 2% (conservative)
• Recovered legitimate sales: $1M/year
• Margin on recovered sales (20%): $200K/year

Net Financial Impact

• Year 1: $1.2M net benefit (partial year, post data-prep)
• Year 2: $2.35M (full deployment)
• Year 3: $2.35M
• 3-Year total benefit: $5.9M
• 3-Year total cost: $1.05M + ongoing $260K/year × 3 = $1.83M
• 3-Year ROI: 169% ($5.9M benefit - $1.83M cost = $4.07M net / $1.83M investment)
• Payback period: 18 months (but delayed by 6-month data prep)

Strategic Positioning

• Customer trust and brand protection
• Foundation for broader risk management AI

AI Transformation Matrix Position

Quadrant: ENHANCE (Strategic + Incremental)

• Improves existing fraud prevention capability
• Strategically important for brand protection
• Doesn't fundamentally change processes

Three Horizons Position

Horizon 1: Core Business Protection

• Immediate impact on bottom line
• Protects existing revenue
• Low implementation risk

Data Requirements

• Transaction history (3+ years)
• Labeled fraud cases
• Customer account behaviour
• Return patterns
• Data Readiness: MEDIUM (fraud labels need cleaning)

Risks & Mitigation

Risk 1: False positives harm customer experience

• Mitigation: Gradual threshold adjustment, easy appeal process

Risk 2: Fraudsters adapt to detection patterns

• Mitigation: Continuous model retraining, anomaly detection

Risk 3: Privacy concerns with behaviour tracking

• Mitigation: Transparent policies, compliance review

Success Metrics

• Fraud detection rate increase to 70%
• False positive rate reduction to <1%
• $3M+ in prevented fraud losses Year 1
• Investigation time reduction by 50%

AI-Specific Evaluation Criteria

1. Data Readiness Score: 6/10 ⚠️

• ✅ 3+ years of transaction data available
• ⚠️ Fraud labels inconsistent (some fraud undetected/unlabeled)
• ❌ Organised fraud rings not well-documented
• ⚠️ Return fraud patterns need better categorisation
• Action required: 3-6 month data labeling project ($75K) BEFORE AI development can start

2. Continuous Learning Plan: Real-time Learning (CRITICAL)

• Fraudsters adapt constantly - must retrain continuously
• New fraud patterns emerge weekly
• Ongoing cost: 40% of initial investment annually ($260K/year)
• Non-negotiable: Static model will be obsolete in months

3. Accuracy & Risk Tolerance

• Risk level: HIGH (two-sided risk)
  • Miss fraud = $$ loss
  • False positive = angry customer, lost sale
• Target accuracy: Need 99% specificity (1% false positive rate)
• Current capability: 95-97% achievable (gap exists)
• Human oversight: Medium/high-risk transactions reviewed by team
• Failure cost: Lost revenue (false positive) OR fraud loss (false negative)

4. Explainability Requirements: HIGH ⚠️

• Legal requirement: Must explain why customer was flagged/blocked
• Investigation need: Fraud team needs evidence for prosecution
• Customer service: Must explain declined transactions
• Solution: Use interpretable models + audit trails
• Budget add: +$100K for explainability + audit system

5. Ethical Risk Assessment: HIGH ⚠️

• Bias risk: Could discriminate against protected demographics
• Historical bias: Past fraud detection may have targeted minorities unfairly
• Disparate impact: AI could perpetuate or amplify existing biases
• Legal risk: Discrimination lawsuits if biased
• Mitigation:
  • Prohibit demographic features in model
  • Test for disparate impact across customer segments
  • External bias audit before launch
  • Ongoing monitoring of false positive rates by demographic
• Budget add: +$125K for bias testing, external audit, monitoring