CASE STUDY

Chargeback Process Workflow Automation for a US-Based Alcohol Importer

Industry

Transport & Logistics

Work Done

Business intelligence,

Executive Summary

A mid-market US-based alcohol importer company managing chargebacks across customer disputes, vendor overbilling, inventory adjustments, and internal cost reallocations was processing every case manually.

There were challenges, including these:

Approvals bottlenecked at individual desks
Duplicate invoices slipped through.
Policy violations went undetected until audits surfaced them weeks later.

By partnering with Growexx, the company implemented an AI-driven chargeback processing framework. It involved a 3-stage system that enforces mandatory business rules first, applies AI risk scoring second, and routes decisions intelligently to auto-approve, auto-reject, or manual review.

The result? Reduced manual workload, faster turnaround, higher-confidence approvals, and a continuous learning engine that increased automation from approximately 60% in Month 1 to 85% by Month 6.

The Challenge: Chargeback Processing at Enterprise Scale

An ERP chargeback is a financial adjustment raised after a transaction dispute, error, return, or cost correction. In alcohol distribution, chargebacks are constant — promotional pricing differences, retailer deductions for damaged shipments, vendor disputes over freight and tariff charges, inventory write-offs from breakage, and internal cost reallocations across brands and warehouses.

The client managed five chargeback types: customer chargebacks from retailer invoice disputes, vendor chargebacks from supplier overbilling, inventory chargebacks from short or damaged stock, banking chargebacks from card disputes, and internal chargebacks from brand-level cost allocation.

Customer chargebacks from invoice disputes
Vendor chargebacks from overbilling or damaged goods
Inventory chargebacks from short or damaged stock
Banking chargebacks from card payment disputes
Internal chargebacks from IT and shared cost allocation

A chargeback document was created and linked to the original transaction. It entered a role-based approval queue: Finance, Manager, Compliance, and status was tracked manually across systems. Once approved, accounting entries were posted. The case was closed with whatever documentation existed at the time.

The volume made this unsustainable. Business triggers included incorrect pricing, payment disputes, returned or damaged goods, vendor overbilling, inventory shortfalls, and internal cost reallocations. Each trigger generated chargebacks that competed for the same manual review bandwidth.

The hidden risks compounded the problem. Duplicate invoices were processed because no pattern detection existed. Policy violations went undetected until periodic audits. Approval bottlenecks delayed resolution by days or weeks. No system learned from past decisions — the same types of disputes required the same manual effort every time.

The business needed a fundamental redesign of its chargeback processing, not just incremental fixes.

The Solution: Chargeback Process Optimization by Growexx

1. Unified Chargeback Data Foundation

In alcohol distribution where promotional deductions, volume-based pricing, and seasonal adjustments generate hundreds of chargebacks monthly, static rules miss patterns that matter.

Growexx approached chargeback processing as a system: designing a 3-stage AI chargeback decision architecture that separates governance from intelligence from routing. The chargeback approval logic follows a clear hierarchy.

2. Regulatory Intelligence and Rule Mapping (TTB + State Laws)

The system enforces a clear 6-step workflow: Original Transaction Posted → Issue Identified → Chargeback Document Created → Role-Based Approval → Financial Posting → Settlement and Closure.

Every chargeback carries a status: Created, Under Review, Approved, Rejected, or Closed. Upon approval, accounting entries post automatically: accounts receivable adjusted, revenue or expense corrected, inventory updated, general ledger reconciled. No manual journal entries. No reconciliation gaps.

3. Mandatory Rule Engine (Stage 1 — Gatekeeper)

Before any AI logic runs, the system enforces non-negotiable business rules. Five mandatory checks gate every chargeback: allocation must be set, invoice reference must exist, chargeback amount must be positive, required documents must be attached, and amount must fall below the configured auto-approval ceiling.

If any rule fails, the chargeback is auto-rejected or routed to manual review immediately. AI never overrides mandatory business rules. This is the governance foundation: policy compliance is enforced before intelligence is applied.

4. AI Risk Scoring (Stage 2 — Brain of the System)

Once mandatory rules pass, the chargeback risk scoring engine calculates a score from 0 to 100. Higher scores indicate higher risk. The model evaluates six input dimensions: vendor approval versus rejection history, similar historical chargebacks, chargeback amount versus normal range, vendor dispute frequency, brand and market behavior, and time-based patterns such as month-end spikes or audit-period anomalies.

Risk bands drive routing: 0–30 is Low Risk, 31–60 is Medium Risk, 61–100 is High Risk. The scoring is fully explainable: finance teams see exactly why a chargeback scored the way it did.

Based on rules plus risk score plus amount thresholds, the system determines one of three paths:

Auto-Approve (Low Risk): All mandatory rules passed, risk score 30 or below, amount within configured limit, vendor has strong approval history, no prior disputes. The chargeback is approved automatically, payment method selected, accounting entries posted, and a complete audit trail logged.
Auto-Reject (Clear Violations): Duplicate invoice detected, policy violation identified, invalid allocation, missing documentation, or vendor with high historical rejection pattern. The chargeback is rejected automatically with a generated rejection reason. Stakeholders are notified. The case closes with a full audit trail.
Manual Review (Human-in-the-Loop): Risk score between 31 and 60, high amount, new or unverified vendor, conflicting historical outcomes, or partial data. The chargeback routes to a finance reviewer. The AI provides its risk score, a recommendation, and similar historical cases for context. The reviewer makes the final call.

6. Configurable Controls and Client Governance

Every decision parameter is configurable without code changes. Auto-approval amount thresholds, risk score cutoffs, vendor blacklists and whitelists, mandatory manual review cases, and market-specific or region-specific rules — all controlled by the client through configuration.

This is critical for enterprise adoption. AI enhances decision quality without replacing governance. The client retains full authority over how chargebacks are processed, who reviews them, and what thresholds trigger escalation.

7. Continuous Learning Loop

Every manual decision is captured and fed back into the AI models. When a finance reviewer approves or rejects a chargeback that the AI routed for review, that outcome trains the system. Risk scoring accuracy improves continuously.

The automation maturity curve is measurable: approximately 60% automation in Month 1, 75% by Month 3, and 85% by Month 6. The system gets smarter with every chargeback it processes — without additional configuration or manual tuning.

8. Scalability Across States and Distribution Channels

Every new state is a configuration addition, i.e., new rules, new license requirements, new tax rates layered onto the existing framework. Our compliance engine that validates a wine shipment to California validates a spirits order routed through Pennsylvania’s state liquor authority as well. As the business grows into DTC or marketplace channels, the compliance foundation scales with it.

Measurable Business Impact

Reduced Manual Workload: Automated approvals and rejections handled routine chargebacks without human intervention. Finance teams focused on high-value exceptions instead of processing every case manually.

Faster Chargeback Turnaround: Intelligent routing eliminated approval bottlenecks. Low-risk chargebacks resolved in minutes, not days.

Higher-Confidence Approvals: AI risk scoring backed every decision with historical context, pattern analysis, and explainable reasoning. Reviewers made faster, better-informed calls on complex cases.

Eliminated Duplicate Payments: Mandatory rule checks caught duplicate invoices and policy violations before approval — proactively, not during audits.

Improved Audit Readiness: Every chargeback carried a complete trail — original transaction reference, approval chain, risk score, AI recommendation, and accounting entries. Audit preparation dropped from days to minutes.

Continuous Improvement: Automation rates increased over time without additional configuration. The system learned from every decision, compounding efficiency gains month over month.

Financial Impact

Revenue protection is the most immediate benefit. Duplicate payment detection and policy enforcement prevent financial leakage that compounds silently across thousands of transactions. The mandatory rule engine catches errors that manual review consistently misses.

Cost containment follows naturally. Each chargeback that resolves automatically reduces the per-case operational cost. As automation maturity increases from 60% to 85%, the cost curve flattens even as transaction volumes grow.

Predictable chargeback operations give leadership confidence. Dispute volumes and resolution capacity become forecastable. Finance teams operate within known parameters instead of reacting to backlogs.

Scaling without proportional headcount is the strategic outcome. Transaction volume grows. Chargeback complexity increases. But the operational team does not need to grow linearly — the system absorbs routine volume while humans handle exceptions.

Organizational Transformation

The initiative changed how the company approaches chargeback processing at every level.

The finance team shifted from reactive firefighting to proactive dispute management. The system flags policy violations before approval, catches duplicates before payment, and routes complex cases with AI-backed context before a reviewer opens the file.

Critical chargeback knowledge was codified into enforceable rules and scoring models. Vendor patterns, risk thresholds, approval logic, and exception handling no longer live in individual heads — they are embedded in the system. When experienced staff transition out, the intelligence stays.

The system augments human judgment rather than replacing it. For ambiguous cases — new vendors, high amounts, conflicting histories — AI provides recommendations and similar historical cases. The reviewer decides. Technology handles volume. Humans handle nuance.

The 80/20 shift is real: routine chargebacks process automatically, and the team invests its expertise where it matters most.

Industry-Wide Implications for Finance Operations

Chargeback processing is becoming a strategic capability — not a back-office task that leadership tolerates.

Rising transaction volumes and dispute complexity demand automation-first approaches. Enterprises processing thousands of chargebacks monthly cannot sustain manual workflows without escalating headcount, error rates, and turnaround times.

Enterprise finance teams that systematize chargeback processing outperform those managing it manually. The difference is compounding: continuous learning systems improve their accuracy and automation rates over time, creating a widening operational advantage.

The competitive gap is measurable. Companies with intelligent chargeback processing resolve disputes faster, retain more revenue through proactive duplicate detection, maintain audit readiness continuously, and scale operations without burnout.

The lesson is clear: build the decision system first, then scale operations on top of it. Technology without governance creates risk. Governance without intelligence creates bottlenecks. The enterprises that thrive combine both — mandatory business rules enforced automatically, AI scoring applied intelligently, and human judgment preserved where it matters.

Conclusion

Chargeback processing does not scale manually. Every duplicate payment missed, every policy violation undetected, and every approval bottleneck tolerated compounds into financial leakage, audit exposure, and operational drag.

Disputes are a systems problem — not an ops problem. The enterprises that treat chargeback processing as a strategic capability build decision frameworks that enforce governance, apply intelligence, and preserve human judgment where it matters.

Growexx builds resilient, scalable chargeback processing systems. Not tools that track tasks — but decision architectures that enforce rules, score risk, route intelligently, and learn continuously.