The Human Judgment Behind Smarter Chargebacks
October 9, 2026 · 5 min read
How chargeback analyst Adriana Stranges combines deep deduction-management experience with ROIAI One to move from evidence gathering to better-informed decisions.
“Roy did its homework and it pulled all the data for me within 10, 15 minutes.”
120 → 20 DAYS
Historical DDO improvement she helped achieve
Context for the benchmark above: the 120-to-20-day DDO improvement comes from Adriana’s prior enterprise experience; it is not presented as a current ROIAI One result.
Chargebacks are evidence problems, and judgment problems
A retailer deduction may look like a simple transaction, but resolving it requires a chain of proof: the purchase order, EDI data, invoice, shipment record, bill of lading, retailer claim, and the commercial terms behind the sale. Adriana’s role is to connect that evidence to the business context and decide whether to dispute, write off, or route the case for validation.
That context matters. A price discrepancy may require Sales to confirm an agreed rebate. A shortage claim may turn on whether the ERP shows the SKU shipped and whether the carrier accepted the cartons. Disputing everything can recover dollars in the short term while damaging buyer relationships and the company’s credibility.
“If somebody didn’t have the knowledge or if I didn’t have the experience that I do, I would have been lost.”
From claim to decision
A Faster Path Through the Evidence
ROIAI One compresses the document-search and evidence-assembly work. Adriana then applies the expertise that automation cannot supply on its own.
- 01Intake the claimStart with the retailer deduction or claim PDF and identify the reason code, amount, deadline, and supporting document requirements.
- 02Assemble the proofMatch the PO or EDI 850 to the invoice, ERP shipment data, bill of lading, packing information, and retailer records.
- 03Apply business contextDetermine whether the evidence supports a dispute, whether Sales must validate pricing or rebates, and whether an exception reflects a real operational issue.
- 04Decide, document, and teachDispute, write off, or route the case, record unusual scenarios, and share the reasoning so ROIAI One and the broader team improve over time.
One live example: a pricing discrepancy
In one case, the purchase order reflected a $5 price while the retailer’s deduction asserted that the correct price was $4.50. Roy pulled all the data in roughly 10 to 15 minutes and routed the pricing question to Sales, the team that confirms what was actually agreed.
A deliberate division of labor
ROIAI One brings speed and structure
It pulls case data, checks purchase-order and shipment records, surfaces documentation, tracks deadlines, and reduces the repetitive work of gathering and following up.
Adriana brings accountable judgment
She interprets exceptions, knows when to involve Sales, weighs the customer relationship, protects audit integrity, and turns edge cases into feedback for the system.
The impact
More Analyst Capacity Without Lowering the Standard
Before automation, chargeback work meant repeatedly searching for documents, chasing colleagues, enforcing response expectations, and manually stitching together the case. Adriana describes the change simply: the work is increasingly automated, while the exceptions still receive expert attention.
“Gathering documents, following up with people, making sure you’re on top of it.”
Quality over raw throughput
Adriana estimates that someone could mechanically push through about 50 cases a day, but she is equally clear that volume alone is the wrong target. Every deduction should be reviewed, including small-dollar cases that help train the system. The real measures are dollars resolved, speed of resolution, recovered cash, root-cause insight, and whether the decision was right.
Her operating principle: customer service and company integrity come before indiscriminate disputing.
Why the human-in-the-loop model matters
Scale the work
Adriana’s earlier enterprise roles show the staffing and cycle-time burden of deduction management. Automation gives one experienced analyst greater reach by removing low-value search and coordination work.
Improve the system
Her job is not simply to use ROIAI One. By documenting exceptions and refining the programmed workflow, she helps the product become more accurate, useful, and aligned with real chargeback operations.
What comes next
Adriana’s vision is a one-stop chargeback workspace: cleaner source data, stronger retailer-portal connections, complete supporting documents, clear KPIs, and a learning loop that reveals recurring root causes. The goal is not to replace the analyst. It is to let the analyst spend more time on judgment, prevention, and the cases where expertise changes the outcome.
“Roy automates all the manual stuff and can help me find the root-cause.”
Sources: Chargeback Analyst Q&A: Completed; “Armando / Adriana: Intro and Q&A” call, August 18, 2026. Prepared as an internal employee case study; quotations lightly punctuated for readability without changing meaning.
Some studies name the customer; others are anonymized. Figures quoted are illustrative of the case described and are not a promise of recovery.