Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Loan servicing with daily accrual, 30/360 quirks, dues crediting and a payment waterfall

AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.01
Wall time: 1m
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Loan servicing: daily accrual and payment waterfall `input.json` (synthetic) gives a `loan` (`principal`, `start_date`, `annual_rate`, `day_count` "ACT/365" or "30/360", `installment`, `due_day`, `grace_days`, `late_fee`), `payments`, `rate_changes`, `fees` (each with ISO `date`), and `end_date`. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json`. Fresh inputs are generated (both day counts, with and without payoff). One early mistake changes every later number, so follow the order exactly. ## Rules 1. Use exact decimals (`decimal.Decimal` built from the JSON numbers' string form). "Round" = half up. 2. Simulate each day d from the day after `start_date` through `end_date` inclusive. Within a day, in this order: (a) rate changes dated d take effect; (b) interest accrues; (c) fees dated d are added; (d) if d is a due date, a due is created; (e) payments dated d are applied, in input order; (f) late-fee checks; (g) if d is a due date, a statement is written. 3. Accrual (only while not paid off), added to `accrued` (unrounded): - ACT/365: round(principal * rate / 365, 8 decimals) every day. - 30/360: round(principal * rate / 360 * n, 8 decimals) where n = 1 for a normal day, **0 on the 31st**, and on the last day of February n = 30 - day + 1 (Feb 28 = 3, leap Feb 29 = 2). 4. Due date each month: day `due_day`, or the month's last day if shorter. No due is created on `start_date`'s day itself or after payoff. Due amount = min(installment, principal + accrued rounded **down** to cents + outstanding fees). 5. A payment first **credits dues**: open dues dated on or before the payment date, oldest first, each up to its remaining unpaid amount. Independently, the whole payment goes through the **waterfall**: outstanding fees oldest first, then interest = min(rest, accrued rounded down to cents), then principal; anything left is `refunded`. Interest paid reduces `accrued` exactly (sub-cent fractions stay). 6. When principal reaches 0: `payoff_date` = that date, accrued is set to 0, and accrual stops. 7. Late fee: on the day due date + `grace_days`, after payments, if that due is still not fully credited, add a fee of `late_fee` (kind "late") once for that due and mark it late. 8. Statement on each due date (rule 4's days, including after payoff): `{"date", "principal", "accrued_interest" (rounded to cents), "fees_outstanding", "rate"}`. ## answer.json `{"statements": [...], "payoff_date": date or null, "final": {"principal", "accrued_interest", "fees_outstanding"}, "totals": {"interest_paid", "fees_paid", "principal_paid", "refunded", "late_fees" (count)}, "dues": [{"date", "due", "paid", "late"}, ...]}` (money as JSON numbers). ## Scoring Hidden checks (partial credit; all must pass; money within 0.001): `first_3_statements`, `all_statements`, `dues_and_late_flags`, `totals`, `final_and_payoff` on the given input; `fresh_run_1`, `fresh_run_2`, `fresh_payoff_30_360`, `fresh_payoff_act365` (whole answer) on fresh inputs. 30-second limit per run.

Eight-decimal daily accrual vs cent rounding, the 30/360 day-31 and end-of-February rules, the within-day event order, dues crediting separate from the waterfall, interest paid in whole cents only, and payoff. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Success criteria: 1. first_3_statements 2. all_statements 3. dues_and_late_flags 4. totals 5. final_and_payoff 6. fresh_run_1 7. fresh_run_2 8. fresh_payoff_30_360 9. fresh_payoff_act365

Setup notes: Use Case Scout batch 12 case m03-loan-servicing-waterfall (offline, synthetic fixture). Prepared with batch12/prepare.sh; hidden key/checker kept outside the agent's work dir (temp dir during the run). Prompt = TASK.md verbatim. Scored with the case's scorer: `python score.py WORKDIR KEYDIR` (python3, run from the case dir). Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 2.90 (8 cores). Claude Code's own reported cost for claude-haiku-5.5: $0.014482. Score: 9/9. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost: $0.01
Wall time: 1m
Human step-ins: 0
Turns: 6

Model usage
Model	Input tok	Output tok	Cost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on	173,196	15,778	$0.01

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Haiku 5.5 (anthropic/claude-haiku-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
It counts as a success only if it meets every success criterion: 1. first_3_statements 2. all_statements 3. dues_and_late_flags 4. totals 5. final_and_payoff 6. fresh_run_1 7. fresh_run_2 8. fresh_payoff_30_360 9. fresh_payoff_act365
Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Automated checks

Trust 80% comes from these checks (how Trust % works).

Run date is not in the future : passed
Model is in the dated pricing table : passed
Cost recomputed from tokens matches : passed
Cached tokens are consistent : passed
Output throughput is plausible : passed
Autonomy matches step-ins : passed
Step-ins do not exceed turns : passed
Retries agree with first-try result : passed
Evidence log SHA-256 matches : passed
Evidence log is substantial : passed

Evidence log

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  "result": "The solver is written at `solve.py` and runs on the given input in about 0.03 s. I did not have the hidden expected answers, so correctness rests on a hand-check of the first statement and a small 30/360 payoff case that I traced by hand.\n\n**Results on `input.json`** (written to `answer.json`):\n- 22 statements, no payoff.\n- Final: principal 11985.62, accrued interest 28.64, fees outstanding 0.\n- Totals: interest paid 3576.78, fees paid 592.16, principal paid 14414.38, refunded 0, late fees 20.\n\n**Checks:**\n- First statement (2027-04-01): 29 days at 26400 \u00d7 0.0941 / 365 per day gives 197.38, which matches.\n- Small 30/360 test with a day-31 due date, a Feb rate change, a fee, and a payoff on 2028-03-01: the day-31 accrual is zero, the 15 normal days give 5.00, the Feb due falls on the 29th, the late fee triggers on Feb 5 and is paid through the fee waterfall, and the sub-cent accrued remainder is discarded at payoff.\n\n**Interpretation choices to be aware of:**\n- Credits to dues are capped by the payment amount: one payment's credits across all dues total at most that payment.\n- `final.accrued_interest` and statement `accrued_interest` are both rounded half-up to cents. If the hidden check expects the unrounded final value, that field will be off by up to half a cent.\n- Rate changes dated on or before `start_date` and payments or fees outside the simulated days are ignored, since the spec only processes dates in the simulation window.\n- Money is written to JSON as floats, which is within the 0.001 tolerance.\n\nFiles are in `./usecase-runner/runs/m03-claude-haiku/work/`: `solve.py` and `answer.json`. The test case lives in `/tmp/solve_test`, outside the work directory.",
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