What Is a Chargeback Pattern?
A chargeback pattern refers to the recurring appearance of chargeback transactions in a merchant’s bank statements.
In MCA and small business lending, chargebacks are problematic because they directly reduce available funds, often unexpectedly, and may indicate customer dissatisfaction or fraud.
These patterns typically appear during transaction review, where operators look for chargeback codes or negative adjustments tied to merchant services. By analyzing the frequency and scale of chargebacks, underwriters gain a clearer picture of revenue reliability.
How Does a Chargeback Pattern Work?
Chargeback pattern analysis involves spotting and evaluating chargeback activity across statements.
- Transaction parsing: Bank statements are scanned for chargeback transactions or negative adjustments.
- Frequency tracking: The number of chargebacks per cycle is counted.
- Severity review: The dollar amount and timing of chargebacks are assessed to see their impact on balances.
- Risk output: High or growing chargeback levels are flagged as a sign of unstable revenue.
In Heron, chargeback pattern detection is built into scrubbing and enrichment.
- Automated parsing: Transactions are extracted from bank statements automatically.
- Pattern surfacing: Heron flags recurring chargebacks and highlights their frequency and value.
- Structured outputs: Chargeback counts and totals are written into CRM fields for underwriting visibility.
- Next action: Deals with excessive chargebacks can be routed to exception queues or marked as out-of-appetite.
This provides underwriters with quick insight into whether revenue is dependable or undermined by disputes.
Why Is a Chargeback Pattern Important?
For brokers and funders, chargeback patterns are a key risk signal. Frequent or large chargebacks mean revenue may not be as strong as it appears, which increases the chance of repayment failure.
Automating chargeback detection reduces manual effort and makes sure every applicant is screened consistently. With Heron, teams can identify these risks early and avoid wasting time underwriting unstable businesses.
Common Use Cases
Chargeback pattern detection is applied in everyday MCA risk analysis.
- Flagging merchants with recurring chargebacks that eat into revenue.
- Identifying spikes in chargebacks that suggest customer disputes or fraud.
- Calculating the total dollar value of chargebacks per statement period.
- Writing chargeback frequency into CRM fields for underwriting checks.
- Escalating high-chargeback cases for exception review before funding.
FAQs About Chargeback Pattern
How does Heron detect chargeback patterns?
Heron scrubs bank statements for chargeback transactions, counts their frequency, and records their amounts. Results are written into CRM fields to support underwriting.
Why are chargeback patterns a risk for funders?
Chargebacks reduce actual cash available and may signal underlying issues such as fraud, poor customer satisfaction, or unstable business operations. These weaken repayment ability.
What outputs should teams expect from chargeback detection?
Teams receive structured fields with chargeback counts, total value, and frequency notes, all tied to the applicant’s record. This gives a clear view of the chargeback impact on cash flow.