AI Chargeback Prediction & Prevention

Status: Coming Soon - This case study is currently in planning.

The Problem

Payments platforms, BNPL providers, and e-commerce merchants face mounting chargeback challenges that directly impact profitability and customer experience:

Chargebacks erode margins and create operational overhead. The average chargeback costs merchants $3.60 per dispute in fees, plus the lost revenue from disputed transactions. For high-volume platforms processing millions of transactions, this translates to millions in annual losses.

Reactive dispute management is too late. By the time a chargeback is filed, the damage is done. Merchants lose revenue, face processing fees, and risk account termination if chargeback ratios exceed thresholds. Current systems only respond after disputes occur.

Prevention strategies are blunt instruments. Many platforms apply blanket fraud prevention measures that hurt conversion rates. Step-up authentication, manual review queues, and delayed fulfillment create friction for legitimate customers while missing actual fraud.

Risk assessment lacks precision. Traditional fraud scoring focuses on identity verification but doesn't account for merchant-specific patterns, product categories, or customer behavior that predict chargeback likelihood.

The industry needs AI-powered systems that predict chargeback risk before transactions complete, enabling targeted prevention strategies that reduce disputes without crushing conversion rates.

The Planned Solution

We're planning an AI-powered chargeback prediction and prevention system that demonstrates production-grade ML for dispute reduction:

• Predictive risk scoring using gradient boosting models trained on transaction patterns, customer behavior, and merchant metadata to identify high-risk orders before completion
• Dynamic prevention strategies with configurable risk bands that trigger appropriate interventions (step-up auth, delayed fulfillment, manual review) based on predicted chargeback probability
• Merchant-specific modeling that adapts to different product categories, customer segments, and business models to optimize risk-return tradeoffs
• Real-time decisioning with sub-500ms inference latency for seamless checkout experiences
• Explainable AI providing clear rationale for risk scores and prevention recommendations to build merchant trust and enable policy tuning

What We'll Demonstrate

Technical Capabilities

• Multi-Source Feature Engineering: Aggregating transaction data, customer history, device fingerprints, and merchant metadata to build comprehensive risk profiles
• Temporal Validation: Time-series cross-validation to ensure models generalize across different time periods and seasonal patterns
• Ensemble ML Models: Gradient boosting (LightGBM/CatBoost) with calibrated probability outputs for precise chargeback risk prediction across merchant segments
• Risk Band Optimization: Automated threshold tuning to balance prevention effectiveness with conversion impact
• Prevention Policy Engine: YAML-configurable rules that map risk scores to specific interventions (3DS, AVS, manual review, delayed capture)
• Uplift Modeling: Quantifying the net benefit of prevention strategies by measuring conversion impact vs. chargeback reduction
• Real-time API: FastAPI endpoints for instant risk scoring and prevention recommendations during checkout flows

Target Outcomes

Upon completion, this case study will demonstrate:

• 20-35% reduction in chargeback rates through early risk detection and targeted prevention
• Precision ≥80% at top decile for chargeback prediction on holdout validation sets
• Less than 2% conversion impact from prevention strategies through precise risk band targeting
• ROI-positive prevention with net savings exceeding intervention costs across merchant segments

Technology Stack Considerations

• ML Framework: Python with LightGBM/CatBoost for categorical features, scikit-learn for calibration, SHAP for explainability
• API Layer: FastAPI for real-time risk scoring with async processing and Redis for caching
• Policy Engine: YAML-based configuration with rule evaluation and action triggering
• Monitoring: Model performance tracking, drift detection, and prevention effectiveness dashboards
• Infrastructure: Containerized deployment with horizontal scaling for high-volume transaction processing

Industry Applications

This approach is applicable to:

• Buy-Now-Pay-Later (BNPL) platforms preventing disputes on installment payments and reducing merchant chargeback exposure
• Marketplace processors protecting sellers from fraudulent buyers and reducing platform liability
• Subscription commerce platforms identifying high-risk recurring payments before customer acquisition
• Digital goods merchants preventing chargebacks on intangible products where physical delivery verification isn't possible
• High-risk merchants in travel, electronics, and luxury goods where chargeback rates typically exceed industry averages
• Payment facilitators providing chargeback prevention as a value-added service to merchant clients

Prevention Strategy Framework

The system will implement a tiered prevention approach:

• High Risk (Top 5%): Step-up authentication (3DS), manual review, or transaction decline with clear customer communication
• Medium Risk (5-15%): Delayed capture, partial fulfillment, or enhanced verification (AVS, device fingerprinting)
• Low Risk (Bottom 85%): Pass through with lightweight logging and monitoring for pattern detection

Each strategy will be calibrated to merchant-specific conversion tolerance and chargeback cost structures.

Data & Research Approach

This reference implementation will use publicly available datasets and synthetic data to demonstrate production-grade ML engineering:

• IEEE-CIS Fraud Detection dataset for transaction pattern analysis and feature engineering techniques
• Synthetic chargeback data generated using agent-based simulation of known dispute patterns and merchant behaviors
• Public merchant category codes (MCC) for industry-specific risk modeling and prevention strategy optimization
• Simulated customer journey data including device fingerprints, session patterns, and behavioral signals

All models and techniques will be designed for easy adaptation to proprietary transaction and customer data in production deployments.

ROI and Business Impact

The case study will demonstrate measurable business value through:

• Loss Prevention: Direct savings from prevented chargebacks (transaction value + processing fees)
• Operational Efficiency: Reduced manual review volume and faster dispute resolution
• Merchant Retention: Lower chargeback ratios preventing account termination and processing restrictions
• Revenue Protection: Maintaining conversion rates while reducing fraud exposure

Expected ROI calculations will show payback periods under 6 months for merchants processing >$1M monthly volume.

Stay Updated

This case study is currently in development. We're designing the architecture, researching chargeback patterns, and planning the technical implementation.

Interested in AI-powered chargeback prevention for your business? Contact us to discuss your dispute management challenges, or subscribe to our newsletter to be notified when this case study launches.

About Devbrew

Devbrew partners with payments companies to design and deploy production-grade AI solutions.

From prototypes to full-scale systems, we help teams ship faster, improve decision accuracy, and unlock measurable ROI.

Ready to explore AI-driven chargeback prevention? Book a call to discuss how we can help optimize your dispute management strategy.