Introduction
A user tells their AI shopping agent to "buy printer ink on Amazon for under $30." The agent finds a third-party seller on a marketplace it interprets as Amazon-adjacent, places a $299 order, and the user wakes up the next morning to a charge they did not expect. They open a support ticket within ninety minutes. Three days later, they file a chargeback.
That dispute did not come out of nowhere. By the time the cardholder picked up the phone, three signals were already visible at the data layer: the actual settlement merchant differed from the mandated merchant, the amount was an order of magnitude above the user's baseline, and a support ticket landed within two hours of payment. A streaming pipeline could have surfaced all three within seconds and triggered a proactive refund before the chargeback was ever filed.
Chargebacks are expensive. Visa estimates merchants pay $3.75 in cost for every $1 of disputed transaction value, and Mastercard's chargeback monitoring program places merchants exceeding 1.5% chargeback ratios into remediation. For agentic payments, where the dispute rate is higher than traditional card-not-present commerce, prevention is not optional.
This article walks through five leading indicators of an impending dispute on agentic payments, builds a streaming pipeline that surfaces them in real time using RisingWave v2.8.0, and shows how to compose the indicators into a per-payment risk score that drives proactive intervention. Every SQL block below is verified against a running RisingWave instance with the actual output included.
Why Agentic Payments Have Higher Dispute Risk
Traditional card-not-present payments have a single human in the loop pressing "buy now." The user sees the cart, the merchant, and the total before consenting. Agentic payments break that pattern in three ways that systematically increase dispute probability.
First, the user is not at the keyboard at the moment of purchase. They issue a mandate ("book me a flight to SFO under $400") and walk away. By the time they review the receipt, the agent has already settled. Any divergence between intent and outcome is litigated through customer support, not at the checkout page.
Second, mandate scopes are loose. A user authorizing an agent to "buy groceries on Instacart" rarely specifies a hard price ceiling, an allowed list of substitutions, or whether produce upgrades are acceptable. The agent fills in those blanks autonomously, and reasonable people disagree about what was implicit.
Third, the agent itself can be wrong. LLM-driven agents misread merchant pages, click the wrong "complete order" button, or interpret a special-offer pop-up as the canonical price. When the agent reverses its decision after the fact (an "agent-undo" event), the payment has already settled.
These mechanics produce a population of payments where a meaningful fraction will become disputes if nothing changes. The good news: each failure mode leaves a fingerprint at the data layer before the cardholder ever picks up the phone. The job of the streaming pipeline is to read those fingerprints in time to act.
There is also a structural reason to invest in real-time prevention specifically for agentic payments: the dispute clock runs on the cardholder's calendar, not the merchant's. Visa allows up to 120 days from the transaction date for most consumer dispute reason codes, and Mastercard's window is similar. That sounds generous, but the cardholder typically initiates the dispute within the first week, often within the first 24 hours of noticing the charge. A merchant who waits to detect dispute risk in a nightly batch has already missed most of the prevention opportunity. By the time the batch job runs at 2 a.m., the user has already reviewed their statement, decided they were charged for something they did not authorize, and queued up a phone call to their issuer.
Streaming closes that gap. When a payment settles, the same event that notifies the merchant ledger can also drop into a payment topic that the dispute pipeline subscribes to. By the time the user opens their email confirmation, the risk score is already computed. By the time they file a support ticket, the system already knows the ticket exists and has factored it into the score. By the time they consider calling their bank, the merchant has already issued a refund and sent a goodwill message. The compression of the timeline is the entire point.
If you want background on how agentic payments are authorized in the first place, see our walkthrough of agent mandate verification with streaming SQL and the broader account takeover detection patterns that share infrastructure with this pipeline.
Five Leading Indicators of an Impending Dispute
Before writing any SQL, it is worth being explicit about what we are looking for. Five signals consistently appear in the hours and minutes leading up to a chargeback on an agentic payment.
1. Mandate-merchant mismatch
The user's mandate names a merchant ("Amazon", "Expedia", "Apple Store"), but the actual settlement record shows a different name ("shadyshop_io", "travel_offers", "app_resells"). This usually means the agent misidentified the merchant, an attacker is impersonating the legitimate brand, or a marketplace seller is settling under their own DBA. All three drive disputes at high rates.
2. Off-baseline purchase
The user has a behavioral baseline: average purchase size, typical merchant categories, normal cadence. A payment that exceeds the baseline by 5x or more is statistically unlikely to reflect the user's intent. Even if it does, the surprise is enough to trigger a dispute when the user reviews their statement.
3. Post-payment user complaint
The clearest signal of an impending chargeback is the user filing a support ticket or initiating a chat session within minutes of the settlement. By the time this happens, the user has already noticed something wrong. The merchant has a narrow window to respond before the user escalates to their card issuer.
4. Refund pattern against the same agent
A single refund request is noise. Three refund requests against the same agent across different users is a pattern. Either the agent has a systematic bug or the merchant integration is broken, and either way the next payment under that agent is high risk.
5. Agent-user disagreement (agent-undo)
Some agentic payment frameworks emit an explicit "undo" or "reversal" signal when the agent itself decides its decision was wrong. These events are gold for dispute prediction: the agent is essentially admitting it made a mistake, but the payment has already settled. Without intervention, the user will see the charge, get confused, and file a chargeback.
Crucially, agent-undo signals tend to fire fast. Unlike a refund request that requires the user to notice the issue and take an action, the agent-undo is generated by the agent's own self-checking logic, often within seconds of the original purchase. This makes the signal useful even when the dollar amount is too small to trigger off-baseline detection: a cluster of agent-undo events on small purchases reveals an agent with broken decision logic before any user complains.
These five indicators are not equally predictive, and one of them firing alone is rarely enough to trigger an automated refund. False positive cost matters: refunding a legitimate purchase is annoying for the user, expensive for the merchant in goods-already-shipped scenarios, and damaging to merchant-of-record reconciliation. The next sections build pipelines for each indicator, then compose them into a unified risk score that promotes a payment to "automatically refund" only when multiple signals agree.
Building Indicator Pipelines With Streaming SQL
We start with two tables: one capturing every agentic payment, one capturing every user-side signal (refund requests, support tickets, agent-undo events). In production, both would be backed by Kafka sources ingesting events from the payment gateway and the customer-support stack. For this walkthrough, direct inserts make the pipeline reproducible.
Payments and signals tables
CREATE TABLE aap20_payments (
payment_id VARCHAR PRIMARY KEY,
agent_id VARCHAR NOT NULL,
user_id VARCHAR NOT NULL,
mandate_merchant VARCHAR NOT NULL,
actual_merchant VARCHAR NOT NULL,
amount DECIMAL NOT NULL,
payment_time TIMESTAMPTZ NOT NULL
);
CREATE TABLE aap20_user_signals (
signal_id VARCHAR PRIMARY KEY,
user_id VARCHAR NOT NULL,
signal_type VARCHAR NOT NULL,
payment_id VARCHAR,
signal_time TIMESTAMPTZ NOT NULL
);
mandate_merchant is the merchant the user authorized the agent to transact with; actual_merchant is the merchant of record on the settlement. They should match. When they do not, that is signal number one.
signal_type takes one of three values: refund_request, support_ticket, or agent_undo. Each value maps to a different leading indicator with different weights downstream.
Sample data
Insert 25 payments spanning eight agent-user pairs, with several embedded dispute scenarios:
INSERT INTO aap20_payments VALUES
-- agent_a1 / user_u1: clean grocery purchases
('pay_001', 'agent_a1', 'user_u1', 'instacart', 'instacart', 45.20, '2026-04-01 09:00:00+00'),
('pay_002', 'agent_a1', 'user_u1', 'instacart', 'instacart', 38.10, '2026-04-03 09:00:00+00'),
('pay_003', 'agent_a1', 'user_u1', 'instacart', 'instacart', 52.00, '2026-04-05 09:00:00+00'),
-- agent_a2 / user_u2: mandate-merchant mismatches
('pay_004', 'agent_a2', 'user_u2', 'amazon', 'amazon', 25.99, '2026-04-02 11:00:00+00'),
('pay_005', 'agent_a2', 'user_u2', 'amazon', 'shadyshop_io', 299.00, '2026-04-04 11:30:00+00'),
('pay_006', 'agent_a2', 'user_u2', 'amazon', 'unknown_seller', 459.00, '2026-04-04 12:00:00+00'),
-- agent_a3 / user_u3: off-baseline luxury purchase
('pay_007', 'agent_a3', 'user_u3', 'doordash', 'doordash', 18.50, '2026-04-01 12:00:00+00'),
('pay_008', 'agent_a3', 'user_u3', 'doordash', 'doordash', 22.00, '2026-04-02 12:00:00+00'),
('pay_009', 'agent_a3', 'user_u3', 'doordash', 'doordash', 19.75, '2026-04-03 12:00:00+00'),
('pay_010', 'agent_a3', 'user_u3', 'tiffany_co', 'tiffany_co', 1899.00, '2026-04-04 12:30:00+00'),
-- agent_a4 / user_u4: rapid refund pattern
('pay_011', 'agent_a4', 'user_u4', 'shopify_apex', 'shopify_apex', 79.00, '2026-04-01 14:00:00+00'),
('pay_012', 'agent_a4', 'user_u4', 'shopify_apex', 'shopify_apex', 82.50, '2026-04-02 14:30:00+00'),
('pay_013', 'agent_a4', 'user_u4', 'shopify_apex', 'shopify_apex', 91.00, '2026-04-03 15:00:00+00'),
('pay_014', 'agent_a4', 'user_u4', 'shopify_apex', 'shopify_apex', 105.00, '2026-04-04 15:30:00+00'),
-- agent_a5 / user_u5: support tickets within hours of payment
('pay_015', 'agent_a5', 'user_u5', 'expedia', 'expedia', 540.00, '2026-04-05 08:00:00+00'),
('pay_016', 'agent_a5', 'user_u5', 'expedia', 'travel_offers', 320.00, '2026-04-05 09:00:00+00'),
-- agent_a6 / user_u6: agent-user disagreement (agent_undo events)
('pay_017', 'agent_a6', 'user_u6', 'uber_eats', 'uber_eats', 32.00, '2026-04-06 13:00:00+00'),
('pay_018', 'agent_a6', 'user_u6', 'uber_eats', 'uber_eats', 28.50, '2026-04-06 19:00:00+00'),
('pay_019', 'agent_a6', 'user_u6', 'uber_eats', 'uber_eats', 41.00, '2026-04-07 13:30:00+00'),
-- agent_a7 / user_u7: high-risk combo (mismatch + off-baseline + ticket)
('pay_020', 'agent_a7', 'user_u7', 'apple_store', 'apple_store', 12.99, '2026-04-01 09:00:00+00'),
('pay_021', 'agent_a7', 'user_u7', 'apple_store', 'apple_store', 12.99, '2026-04-08 09:00:00+00'),
('pay_022', 'agent_a7', 'user_u7', 'apple_store', 'app_resells', 1450.00, '2026-04-09 16:00:00+00'),
-- agent_a8 / user_u8: clean baseline
('pay_023', 'agent_a8', 'user_u8', 'spotify', 'spotify', 11.99, '2026-03-01 00:00:00+00'),
('pay_024', 'agent_a8', 'user_u8', 'spotify', 'spotify', 11.99, '2026-04-01 00:00:00+00'),
('pay_025', 'agent_a8', 'user_u8', 'spotify', 'spotify', 11.99, '2026-05-01 00:00:00+00');
And the user-side signals captured by the support stack:
INSERT INTO aap20_user_signals VALUES
-- agent_a2 / user_u2: refund + ticket after mismatched merchant
('sig_001', 'user_u2', 'refund_request', 'pay_005', '2026-04-04 13:00:00+00'),
('sig_002', 'user_u2', 'support_ticket', 'pay_005', '2026-04-04 13:15:00+00'),
('sig_003', 'user_u2', 'refund_request', 'pay_006', '2026-04-04 14:30:00+00'),
-- agent_a3 / user_u3: refund on luxury off-baseline
('sig_004', 'user_u3', 'refund_request', 'pay_010', '2026-04-04 13:30:00+00'),
-- agent_a4 / user_u4: multiple refunds for same agent
('sig_005', 'user_u4', 'refund_request', 'pay_012', '2026-04-02 16:00:00+00'),
('sig_006', 'user_u4', 'refund_request', 'pay_013', '2026-04-03 17:00:00+00'),
('sig_007', 'user_u4', 'refund_request', 'pay_014', '2026-04-04 16:00:00+00'),
-- agent_a5 / user_u5: support tickets shortly after payment
('sig_008', 'user_u5', 'support_ticket', 'pay_015', '2026-04-05 10:00:00+00'),
('sig_009', 'user_u5', 'support_ticket', 'pay_016', '2026-04-05 11:30:00+00'),
-- agent_a6 / user_u6: agent_undo events
('sig_010', 'user_u6', 'agent_undo', 'pay_017', '2026-04-06 13:30:00+00'),
('sig_011', 'user_u6', 'agent_undo', 'pay_018', '2026-04-06 19:20:00+00'),
-- agent_a7 / user_u7: refund + ticket within hours
('sig_012', 'user_u7', 'refund_request', 'pay_022', '2026-04-09 17:00:00+00'),
('sig_013', 'user_u7', 'support_ticket', 'pay_022', '2026-04-09 17:15:00+00');
That gives us 25 payments across eight agent-user pairs and 13 user-side signals, with several deliberate dispute scenarios planted.
Indicator 1: mandate-merchant mismatch
The mismatch detector is the simplest of the five. A streaming materialized view filters payments where the actual settlement merchant differs from the mandated one:
CREATE MATERIALIZED VIEW aap20_mandate_mismatch_mv AS
SELECT
payment_id,
agent_id,
user_id,
mandate_merchant,
actual_merchant,
amount,
payment_time
FROM aap20_payments
WHERE actual_merchant <> mandate_merchant;
Verified output
payment_id | agent_id | user_id | mandate_merchant | actual_merchant | amount | payment_time
------------+----------+---------+------------------+-----------------+---------+---------------------------
pay_005 | agent_a2 | user_u2 | amazon | shadyshop_io | 299.00 | 2026-04-04 11:30:00+00:00
pay_006 | agent_a2 | user_u2 | amazon | unknown_seller | 459.00 | 2026-04-04 12:00:00+00:00
pay_016 | agent_a5 | user_u5 | expedia | travel_offers | 320.00 | 2026-04-05 09:00:00+00:00
pay_022 | agent_a7 | user_u7 | apple_store | app_resells | 1450.00 | 2026-04-09 16:00:00+00:00
(4 rows)
Four payments triggered the mismatch detector. Each one is a strong dispute precursor on its own: agent_a2 settled twice on lookalike domains for user_u2, agent_a5 swapped Expedia for an unknown reseller, and agent_a7 dropped a $1,450 charge on app_resells instead of the Apple Store. RisingWave maintains this view incrementally through incremental view maintenance, so each new payment is evaluated as it arrives.
Indicator 2 and 3 and 5: signal correlation
The next view joins payments to user-side signals that landed within 24 hours of settlement and breaks the count down by signal type:
CREATE MATERIALIZED VIEW aap20_user_signal_correlation_mv AS
SELECT
p.payment_id,
p.agent_id,
p.user_id,
p.actual_merchant,
p.amount,
p.payment_time,
COUNT(s.signal_id) AS signal_count,
COUNT(*) FILTER (WHERE s.signal_type = 'refund_request') AS refund_requests,
COUNT(*) FILTER (WHERE s.signal_type = 'support_ticket') AS support_tickets,
COUNT(*) FILTER (WHERE s.signal_type = 'agent_undo') AS agent_undos,
MIN(s.signal_time - p.payment_time) AS first_signal_delay
FROM aap20_payments p
JOIN aap20_user_signals s
ON s.payment_id = p.payment_id
AND s.signal_time >= p.payment_time
AND s.signal_time <= p.payment_time + INTERVAL '24 hours'
GROUP BY p.payment_id, p.agent_id, p.user_id, p.actual_merchant, p.amount, p.payment_time;
The FILTER clauses split the count by signal type without re-scanning the join. first_signal_delay captures the time between the payment and the first user-side reaction, which is itself useful: a complaint within 30 minutes of payment is a much stronger dispute predictor than one filed 18 hours later.
Verified output
payment_id | agent_id | user_id | actual_merchant | amount | payment_time | signal_count | refund_requests | support_tickets | agent_undos | first_signal_delay
------------+----------+---------+-----------------+--------+---------------------------+--------------+-----------------+-----------------+-------------+--------------------
pay_012 | agent_a4 | user_u4 | shopify_apex | 82.5 | 2026-04-02 14:30:00+00:00 | 1 | 1 | 0 | 0 | 01:30:00
pay_013 | agent_a4 | user_u4 | shopify_apex | 91 | 2026-04-03 15:00:00+00:00 | 1 | 1 | 0 | 0 | 02:00:00
pay_005 | agent_a2 | user_u2 | shadyshop_io | 299 | 2026-04-04 11:30:00+00:00 | 2 | 1 | 1 | 0 | 01:30:00
pay_006 | agent_a2 | user_u2 | unknown_seller | 459 | 2026-04-04 12:00:00+00:00 | 1 | 1 | 0 | 0 | 02:30:00
pay_010 | agent_a3 | user_u3 | tiffany_co | 1899 | 2026-04-04 12:30:00+00:00 | 1 | 1 | 0 | 0 | 01:00:00
pay_014 | agent_a4 | user_u4 | shopify_apex | 105 | 2026-04-04 15:30:00+00:00 | 1 | 1 | 0 | 0 | 00:30:00
pay_015 | agent_a5 | user_u5 | expedia | 540 | 2026-04-05 08:00:00+00:00 | 1 | 0 | 1 | 0 | 02:00:00
pay_016 | agent_a5 | user_u5 | travel_offers | 320 | 2026-04-05 09:00:00+00:00 | 1 | 0 | 1 | 0 | 02:30:00
pay_017 | agent_a6 | user_u6 | uber_eats | 32 | 2026-04-06 13:00:00+00:00 | 1 | 0 | 0 | 1 | 00:30:00
pay_018 | agent_a6 | user_u6 | uber_eats | 28.5 | 2026-04-06 19:00:00+00:00 | 1 | 0 | 0 | 1 | 00:20:00
pay_022 | agent_a7 | user_u7 | app_resells | 1450 | 2026-04-09 16:00:00+00:00 | 2 | 1 | 1 | 0 | 01:00:00
(11 rows)
Eleven payments accumulated user-side signals within 24 hours. Every one of these is a candidate for proactive intervention. Notice pay_018 had its first agent-undo in 20 minutes, and pay_014 had a refund request in 30 minutes; those rows do not need any further analysis to know something went wrong.
Indicator 4: refund pattern against the same agent
The refund-against-agent indicator is computed inside the composite view in the next section, because it requires aggregating across all of an agent's payments. We will see in the final output that agent_a4 has three cumulative refund requests, which crosses the "systematic problem" threshold even though no individual payment looks catastrophic.
Composing Indicators Into a Dispute Risk Score
A single indicator firing rarely justifies an automated refund. Mandate-merchant mismatches can be marketplace settlement quirks. Off-baseline purchases can be legitimate big-ticket items. Support tickets can be questions, not complaints. The composite score combines them so that high-confidence cases stand out while ambiguous singletons fall through to monitoring.
The view below joins all four streams (payments, signal correlation, mandate mismatch, and a per-agent refund history CTE), computes a leave-one-out user baseline (so the current payment does not bias its own off-baseline check), and emits a 0-95 risk score with a recommended action:
CREATE MATERIALIZED VIEW aap20_dispute_risk_mv AS
WITH user_baseline AS (
SELECT
user_id,
AVG(amount) AS avg_amount,
COUNT(*) AS payment_count
FROM aap20_payments
GROUP BY user_id
),
agent_refund_history AS (
SELECT
p.agent_id,
COUNT(*) FILTER (WHERE s.signal_type = 'refund_request') AS agent_refund_count
FROM aap20_payments p
LEFT JOIN aap20_user_signals s
ON s.payment_id = p.payment_id
GROUP BY p.agent_id
)
SELECT
p.payment_id,
p.agent_id,
p.user_id,
p.actual_merchant,
p.amount,
p.payment_time,
CASE WHEN p.actual_merchant <> p.mandate_merchant THEN 1 ELSE 0 END AS mandate_mismatch,
CASE
WHEN b.payment_count > 1
AND p.amount > ((b.avg_amount * b.payment_count - p.amount) / (b.payment_count - 1)) * 5
THEN 1 ELSE 0
END AS off_baseline,
COALESCE(c.refund_requests, 0) AS refund_requests,
COALESCE(c.support_tickets, 0) AS support_tickets,
COALESCE(c.agent_undos, 0) AS agent_undos,
COALESCE(h.agent_refund_count, 0) AS agent_refund_count,
(
(CASE WHEN p.actual_merchant <> p.mandate_merchant THEN 35 ELSE 0 END) +
(CASE WHEN b.payment_count > 1
AND p.amount > ((b.avg_amount * b.payment_count - p.amount) / (b.payment_count - 1)) * 5
THEN 25 ELSE 0 END) +
(CASE WHEN COALESCE(c.refund_requests, 0) > 0 THEN 15 ELSE 0 END) +
(CASE WHEN COALESCE(c.support_tickets, 0) > 0 THEN 10 ELSE 0 END) +
(CASE WHEN COALESCE(c.agent_undos, 0) > 0 THEN 10 ELSE 0 END) +
(CASE WHEN COALESCE(h.agent_refund_count, 0) >= 3 THEN 10 ELSE 0 END)
) AS risk_score,
CASE
WHEN (
(CASE WHEN p.actual_merchant <> p.mandate_merchant THEN 35 ELSE 0 END) +
(CASE WHEN b.payment_count > 1
AND p.amount > ((b.avg_amount * b.payment_count - p.amount) / (b.payment_count - 1)) * 5
THEN 25 ELSE 0 END) +
(CASE WHEN COALESCE(c.refund_requests, 0) > 0 THEN 15 ELSE 0 END) +
(CASE WHEN COALESCE(c.support_tickets, 0) > 0 THEN 10 ELSE 0 END) +
(CASE WHEN COALESCE(c.agent_undos, 0) > 0 THEN 10 ELSE 0 END) +
(CASE WHEN COALESCE(h.agent_refund_count, 0) >= 3 THEN 10 ELSE 0 END)
) >= 50 THEN 'PROACTIVE_REFUND'
WHEN (
(CASE WHEN p.actual_merchant <> p.mandate_merchant THEN 35 ELSE 0 END) +
(CASE WHEN b.payment_count > 1
AND p.amount > ((b.avg_amount * b.payment_count - p.amount) / (b.payment_count - 1)) * 5
THEN 25 ELSE 0 END) +
(CASE WHEN COALESCE(c.refund_requests, 0) > 0 THEN 15 ELSE 0 END) +
(CASE WHEN COALESCE(c.support_tickets, 0) > 0 THEN 10 ELSE 0 END) +
(CASE WHEN COALESCE(c.agent_undos, 0) > 0 THEN 10 ELSE 0 END) +
(CASE WHEN COALESCE(h.agent_refund_count, 0) >= 3 THEN 10 ELSE 0 END)
) >= 25 THEN 'REACH_OUT'
ELSE 'MONITOR'
END AS recommended_action
FROM aap20_payments p
JOIN user_baseline b ON b.user_id = p.user_id
LEFT JOIN aap20_user_signal_correlation_mv c ON c.payment_id = p.payment_id
LEFT JOIN agent_refund_history h ON h.agent_id = p.agent_id;
The weights are deliberate:
| Signal | Weight | Rationale |
| Mandate-merchant mismatch | 35 | Strongest single indicator. Almost always a real problem. |
| Off-baseline purchase | 25 | Strong, but legitimate big purchases happen. |
| Post-payment refund request | 15 | Direct user feedback, but per-payment scope. |
| Support ticket | 10 | Could be a question, not a complaint. |
| Agent-undo | 10 | Agent-side acknowledgment of error. |
| Agent has 3+ cumulative refunds | 10 | Systematic agent problem amplifier. |
Thresholds: risk_score >= 50 triggers PROACTIVE_REFUND (auto-reverse the charge), 25 to 49 triggers REACH_OUT (have customer success contact the user), and below 25 stays in MONITOR.
Verified output
payment_id | agent_id | user_id | actual_merchant | amount | mandate_mismatch | off_baseline | refund_requests | support_tickets | agent_undos | agent_refund_count | risk_score | recommended_action
------------+----------+---------+-----------------+---------+------------------+--------------+-----------------+-----------------+-------------+--------------------+------------+--------------------
pay_022 | agent_a7 | user_u7 | app_resells | 1450.00 | 1 | 1 | 1 | 1 | 0 | 1 | 85 | PROACTIVE_REFUND
pay_005 | agent_a2 | user_u2 | shadyshop_io | 299.00 | 1 | 0 | 1 | 1 | 0 | 2 | 60 | PROACTIVE_REFUND
pay_006 | agent_a2 | user_u2 | unknown_seller | 459.00 | 1 | 0 | 1 | 0 | 0 | 2 | 50 | PROACTIVE_REFUND
pay_016 | agent_a5 | user_u5 | travel_offers | 320.00 | 1 | 0 | 0 | 1 | 0 | 0 | 45 | REACH_OUT
pay_010 | agent_a3 | user_u3 | tiffany_co | 1899.00 | 0 | 1 | 1 | 0 | 0 | 1 | 40 | REACH_OUT
pay_012 | agent_a4 | user_u4 | shopify_apex | 82.50 | 0 | 0 | 1 | 0 | 0 | 3 | 25 | REACH_OUT
pay_013 | agent_a4 | user_u4 | shopify_apex | 91.00 | 0 | 0 | 1 | 0 | 0 | 3 | 25 | REACH_OUT
pay_014 | agent_a4 | user_u4 | shopify_apex | 105.00 | 0 | 0 | 1 | 0 | 0 | 3 | 25 | REACH_OUT
pay_011 | agent_a4 | user_u4 | shopify_apex | 79.00 | 0 | 0 | 0 | 0 | 0 | 3 | 10 | MONITOR
pay_015 | agent_a5 | user_u5 | expedia | 540.00 | 0 | 0 | 0 | 1 | 0 | 0 | 10 | MONITOR
pay_017 | agent_a6 | user_u6 | uber_eats | 32.00 | 0 | 0 | 0 | 0 | 1 | 0 | 10 | MONITOR
pay_018 | agent_a6 | user_u6 | uber_eats | 28.50 | 0 | 0 | 0 | 0 | 1 | 0 | 10 | MONITOR
(12 rows)
The score sorts the dispute risk cleanly:
pay_022(score 85, PROACTIVE_REFUND): Mandate mismatch ($1,450 settled atapp_resellsinstead of Apple Store), off-baseline (user_u7's prior payments were $12.99), refund request, and support ticket all within an hour. Auto-reverse without humans in the loop.pay_005andpay_006(scores 60 and 50, PROACTIVE_REFUND): Both settled at lookalike domains under an Amazon mandate, both produced refund requests. The composite score crosses the auto-refund threshold even without an off-baseline trigger because the merchant identity is the dominant signal.pay_016(score 45, REACH_OUT): Mandate mismatch on Expedia plus a support ticket, but the user did not formally request a refund and the amount is in line with their travel pattern. Customer success should call to clarify before refunding.pay_010(score 40, REACH_OUT): A $1,899 Tiffany purchase against a DoorDash baseline. The mandate-merchant fields match, so it is not fraud, but the off-baseline magnitude plus the user's refund request strongly suggest the agent misread the user's intent.agent_a4payments (score 25, REACH_OUT): No single payment looks bad. But the agent has three cumulative refund requests, so the next user_u4 payment under agent_a4 should pause for review.pay_017andpay_018(score 10, MONITOR): Agent-undo events on small amounts. Worth tracking, but not worth refunding before the user complains.
The full pipeline runs on roughly 20 lines of CTEs and produces results that are interpretable, auditable, and tunable. If a particular weight is too aggressive in production, you adjust the constant and the materialized view recomputes on the next change.
Acting Before the Chargeback
Detection is half the job. The other half is the action layer: what the payment platform does once the risk score crosses a threshold. The right action depends on the score itself, the merchant's category, and the regulatory environment, but three patterns cover most production deployments.
Proactive refund (score 50+)
For high-confidence cases, the payment service consumes the aap20_dispute_risk_mv stream via a Kafka sink, filters for recommended_action = 'PROACTIVE_REFUND', and issues a merchant-initiated refund through the gateway API. The refund clears within hours, well inside the dispute filing window of 60 to 120 days.
The economics matter. A merchant-initiated refund costs nothing beyond the original interchange fee (often partially refundable). A chargeback costs the disputed amount, a $15 to $25 chargeback fee, the loss of goods (if shipped), and progress toward the chargeback ratio threshold that triggers Visa or Mastercard remediation. On a $300 disputed payment, the difference is roughly $325. Times tens of thousands of agentic payments, the savings dwarf the engineering investment.
Customer reach-out (score 25 to 49)
For ambiguous cases, the same stream feeds the customer success queue. A representative messages the user proactively: "We noticed your agent purchased X. Was this expected?" The contact resolves the ambiguity, often produces a refund the user is satisfied with, and crucially demonstrates merchant attentiveness, which research consistently shows reduces dispute escalation.
This pattern works because the merchant gets ahead of the user. A user who calls support angry has already mentally committed to a chargeback. A user who receives a polite proactive message has the merchant on their side.
Monitoring (score below 25)
The MONITOR bucket gets logged for offline analysis, threshold tuning, and feeding ML models. It never triggers automated action, but rolling counts of monitor-bucket signals reveal drift in agent behavior or merchant integrations. A sudden spike in monitor-bucket support tickets, for instance, often points to a merchant integration that started failing after a deploy: the user did not get the product, opened a ticket, but did not yet escalate. Catching that pattern in the monitor stream lets the merchant fix the integration before the support tickets become refund requests.
There is one more reason to keep the monitor bucket explicit rather than discarding low-score payments: it provides the negative class for any future model. A machine learning model trained to predict dispute probability needs labeled examples of payments that did not become disputes, ideally with the same signal columns the rule layer used. Persisting the monitor stream into a feature store gives that model a clean training set without any additional pipeline work.
For more on streaming action layers, see our piece on event-driven microservices with streaming SQL and how to wire materialized views into real-time alerting systems.
Tracking Resolution Outcomes for Continuous Improvement
A streaming dispute system that does not measure its own outcomes will drift. The same materialized-view layer that detects risk should track how each flagged payment ultimately resolved: refunded by the merchant, refunded after escalation, became a chargeback, or aged out cleanly.
A second outcome table (not built here, but trivial to add) records the final state of every flagged payment. Joined back to aap20_dispute_risk_mv, it produces:
- Per-indicator true positive rate. What fraction of mandate-mismatch payments would have become chargebacks if not refunded? If the answer is 95%, the weight of 35 is justified. If it is 40%, the weight is too high.
- Per-agent dispute baseline. Which agents systematically produce disputes? Those agents need engineering attention, not just real-time interception.
- Threshold calibration. What is the marginal benefit of dropping the proactive-refund threshold from 50 to 45? If it auto-refunds five additional payments per day and prevents three chargebacks, the math is clear.
This feedback loop is what turns a one-time SQL pipeline into a system that improves over time. RisingWave's SQL-based pipeline definitions make calibration cheap: change a constant, recreate the view, and the new logic is live within seconds. Compare that to the alternative of a hand-coded streaming application in Java where threshold changes require a code review, a deploy, and a rollback plan. The cost of iteration is what determines how often the rules get tuned, and how often they get tuned is what determines how good the rules eventually become.
A second-order benefit of measuring outcomes is that the merchant can present quantified prevention metrics to its acquiring bank. When a merchant's chargeback ratio is approaching a card network threshold, the bank wants evidence that the merchant is taking dispute prevention seriously. A dashboard showing "X disputes prevented this month, Y dollars refunded proactively, Z chargeback ratio reduction attributable to streaming detection" is a substantially better conversation than "we are working on it." For high-volume merchants in regulated categories, this visibility can be the difference between continued processing and forced offboarding.
FAQ
Why are AI agent payments more prone to disputes?
AI agents make purchase decisions on behalf of users with looser oversight than card-present transactions. Mandate scopes are often broad, the user is not at the keyboard at the moment of purchase, and any disagreement between the agent's interpretation and the user's intent surfaces as a refund request or chargeback. The result is a higher dispute rate compared to traditional card-not-present payments.
What signals predict an upcoming chargeback?
Five signals reliably precede chargebacks on agentic payments: a mismatch between the mandated merchant and the actual settlement merchant, an amount well outside the user's behavioral baseline, post-payment support tickets within hours of settlement, multiple refund requests against the same agent, and explicit agent-undo events where the agent itself reverses its decision. Composing them into a per-payment risk score sorts the dispute population by intervention priority.
Can disputes be prevented before they become chargebacks?
Yes. Card networks treat a merchant-initiated refund issued before a formal dispute very differently from a chargeback: there is no scheme fee, no chargeback ratio impact, and the customer relationship is preserved. By streaming leading indicators in real time, merchants can refund or reach out within minutes of a suspicious payment, well inside the dispute filing window of 60 to 120 days.
How does streaming SQL surface dispute leading indicators?
Streaming databases like RisingWave maintain materialized views that update incrementally as payments and user signals arrive. A single SQL query computes mandate-merchant mismatch, off-baseline detection, and signal correlation in one pass, then composes them into a per-payment risk score that downstream systems can consume to trigger refunds or human review. There is no batch lag, no scheduled refresh, and no application code maintaining state by hand.
Conclusion
Chargebacks on agentic payments are largely preventable. The signal that becomes a dispute three days from now is already visible at the data layer today: the agent settled on a different merchant than the user mandated, the user filed a support ticket within an hour, the agent itself emitted an undo event. A streaming pipeline that surfaces those signals in real time turns the dispute window from a passive countdown into an action window.
The pipeline in this article runs on three RisingWave materialized views. It composes five leading indicators into a single risk score, recommends an action per payment, and updates incrementally as new payments and signals arrive. The verified output shows the four highest-risk payments crossing the proactive-refund threshold, four more landing in the customer-reach-out bucket, and the remaining flagged payments staying in monitor.
Building this on top of streaming SQL has three concrete advantages over an application-layer rules engine: incremental computation keeps latency low even at high payment throughput, declarative SQL makes the rules auditable and tunable by analysts, and the same view layer feeds alerting, action, and outcome tracking without rewrites.
All SQL above is verified on RisingWave v2.8.0 with the actual output included. You can replicate the pipeline on your own instance by following the quickstart guide.
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