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Test a return recalculating the remaining instalments

A partial return has to reduce the payments that have not happened yet, without touching the one that already did.

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Returning one item from a buy now pay later order is where the arithmetic gets genuinely hard. Money has already changed hands, three payments are still scheduled, and the refund has to land somewhere sensible.

This use case registers a partial return on the Klarnah app and checks every figure the recalculation touches.

Failure modes in this flow

The correct behaviour here is specific, and several plausible implementations get it wrong in ways that still balance.

FigureBefore the returnAfter the return
Order total$180.00$120.00
Outstanding$135.00$75.00
Payment 1 (already paid)$45.00$45.00, unchanged
Payments 2, 3 and 4$45.00 each$25.00 each

The payment already taken stays at $45.00 and the remaining three absorb the whole reduction. An implementation that spreads the refund across all four produces a correct outstanding balance and an incorrect first payment, which is a reconciliation problem rather than a display one.

Note

Note: Asserting the state before and after a recalculation is what makes the change provable, which matters more here than on any display-only flow. Try it on TestMu AI. Try free!

Writing the objective

The second step captures the schedule before the return, which is what turns the fourth step from a set of numbers into a verified transition.

---
mode: testing
url: https://my-testing-repo-main.vercel.app/klarna-clone-app/checkout?reset=true
max_steps: 55
tags: [klarna, payments, returns]
---

# Klarnah 14.4: Return adjusts instalments

Catalog objective: register a return and verify the remaining instalments are recalculated.
Key assertion: the schedule updates to the reduced amount.

## Place the order and open the app
Type "Priya Nair" into "Full name", type "4417" into "Last 4 of ID number", click "Continue with Klarnah", click "Confirm and pay $45.00 today", then click "Open it in the Klarnah app".

## Verify the schedule before the return
Verify "Order total" reads "$180.00", "Outstanding" reads "$135.00" and payments 2, 3 and 4 are each "$45.00".

## Register the return
Tick "Warm LED Bulbs (4 pack) - $60.00" in "Register a return", click "Register return", and verify a green banner reads "Return registered - your remaining payments have been recalculated."

## Verify the remaining instalments were reduced
Verify "Order total" now reads "$120.00", "Outstanding" reads "$75.00", a badge reads "Refund applied: $60.00", payment 1 is still "$45.00" and "Paid", and payments 2, 3 and 4 are each "$25.00".

The step ceiling is 55, the highest in this batch, because the flow completes a checkout, opens the management app and then performs the return before it asserts anything about the recalculation.

The assertions in detail

Two steps of setup and baseline, then the action, then the verification.

StepWhat it proves
Place the order and open the appA live order with a schedule exists
Verify the schedule before the returnThe starting totals and the three equal remaining payments
Register the returnA specific item can be returned and the recalculation is confirmed
Verify the remaining instalments were reducedNew total, new outstanding, the refund badge and each payment

Seven separate values asserted against one action. That ratio is appropriate when the action redistributes money across a schedule the customer is contractually committed to.

Next-generation test execution with TestMu AI

Running it locally

kane-cli testmd run tests/klarna/return-adjusts-installments_test.md

This is the flow to run after any change to refund handling, since it is the one place where a rounding decision becomes a customer-visible instalment.

Money-moving recalculations deserve retained evidence. Evidence based QA for agentic runs covers what to keep, and deriving a suite from written requirements covers keeping the expected figures tied to their source.

Author

...

Bhawana

Blogs: 75

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Bhawana is a Community Evangelist at TestMu AI with over 3 years of experience creating technically accurate, strategy-driven content in software testing. She has authored 50+ blogs on test automation, cross-browser testing, mobile testing, and real device testing. She also serves as Product Marketing Manager for Kane CLI, the command-line tool that runs browser automation from the terminal using natural-language flows in a real Chrome browser. Bhawana is certified in KaneAI, Selenium, Appium, Playwright, and Cypress, reflecting her hands-on knowledge of modern automation practices. On LinkedIn, she is followed by 6000+ QA engineers, testers, AI automation testers, and tech leaders.

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