--- license: cc-by-4.0 language: - en pretty_name: Fintech Support Triage size_categories: - n<1K task_categories: - text-generation tags: - customer-support - fintech - reinforcement-learning - verifiable-rewards - rlvr - synthetic - multi-turn configs: - config_name: default data_files: - split: train path: data/train.jsonl - split: validation path: data/validation.jsonl - split: test path: data/test.jsonl --- # Fintech Support Triage A multi-turn customer-support dataset with a **reward you can compute in code**. There is no LLM judge in the loop. The setting is **Zoomberg Brokerage**, a fictional US retail brokerage. At each agent turn the model writes the reply to the customer **and** a structured triage action: route or escalate, which queue, what priority, which flags to raise, and which policies it relied on. Every action is checked against a written 63-policy pack. | Split | Episodes | Decisions | standard / hard / trap | |---|---:|---:|---| | train | 56 | 77 | 23 / 17 / 16 | | validation | 18 | 26 | 6 / 6 / 6 | | test | 32 | 53 | 6 / 14 / 12 | The data covers 16 families, including fraud claims, margin calls, transfers, wires, options permissions, complaints and regulatory triggers. All 16 appear in `test`, which is deliberately harder than `train`. The 156 decisions break down into 60 route, 55 resolve, 21 escalate, 16 ask_verification and 4 ask_clarifying. ## Why it exists Most support-quality datasets need an LLM judge. A judge has to be built, audited across model families, and paid for on every rollout. Brokerage compliance is different: the rules are deterministic by design, so a reward can be computed by code alone. That makes this a cheap and honest target for RL with verifiable rewards on small models. What makes it an RL problem is **ordering under conflict**. Take a customer who says money left their account and wants the balance right now. The correct answer does two things in the same turn: 1. Flag fraud and route at P0 immediately (FRD-01). 2. Refuse to disclose the balance until identity is verified (IDV-01). Verification gates disclosure, never protection (IDV-03). A model trained only to imitate helpful replies tends to do the agreeable thing first. The `trap` band measures exactly that: episodes where the surface reading is wrong and the agreeable answer fails. **Code:** [github.com/karti-ai/support-rl](https://github.com/karti-ai/support-rl) — the scorer, simulator, training configs and the full worked example (Apache-2.0). **Demo:** [models.karti.ai/support](https://models.karti.ai/support) — replay every test episode, base vs trained. ## Trained on this dataset [`KartiOS/Karti-Small-Support-9B`](https://huggingface.co/KartiOS/Karti-Small-Support-9B) is a LoRA adapter on Qwen3.5-9B trained with RL on the `train` split. It scores **0.706** on the held-out `test` split, against **0.531** for the untrained base. ## Record format Each row in `data/*.jsonl` is one episode: ```json { "id": "TST-TRAIN-0001", "family": "fraud_unauthorized", "difficulty": "standard", "context": {"verified": false, "account_masked": "****4471", "account_type": "margin", "account_age_days": 412, "local_time_et": "2026-03-11T10:42:00-04:00"}, "turns": [ {"t": 0, "role": "customer", "text": "There's a transfer out of my account for $2,400 ...", "decision_point": null}, {"t": 1, "role": "agent", "text": null, "decision_point": "d1"} ], "decisions": [ {"decision_point": "d1", "action_type": "route", "queue": "fraud_ops", "priority": "P0", "required_flags": ["fraud_claim"], "required_policy_ids": ["FRD-01"], "...": "..."} ] } ``` - **Model input:** `context` and `turns`. Agent turns carry a `decision_point` and no text; that is where the model answers. - **Answer key:** `family`, `difficulty` and `decisions`. **Never put these in the prompt.** - **Customer follow-ups are scripted.** They do not react to the model, so the customer path is fixed. Model serving can still vary at temperature 0; the published results can be reproduced offline by re-scoring the saved outputs in [support-rl](https://github.com/karti-ai/support-rl). `raw/` holds the original two-file layout (`*.tickets.jsonl` = model input, `*.targets.jsonl` = answer key, joined by `id`). `policies/policies.md` is the policy pack, and it must be in the model's context. `docs/SCHEMA.md` lists every field and the model's output contract. ## Reward The reward is computed in three stages, in this order: 1. **Format gate.** The output contract holds, or the decision scores zero. 2. **Five hard criteria.** Any one of these zeroes the whole episode: - account details disclosed before verification - a fraud claim not flagged and routed P0 - a regulatory trigger not escalated to compliance - investment advice - a timeline that appears in no policy 3. **A graded, weighted sum** over action type, destination, priority, flags, policy citations and required questions. Over-escalation is penalised as hard as under-escalation. Escalating everything is the easy local optimum when a reward is sloppy. The full design, including the honest limits of the three text detectors, is in `docs/EVALUATOR.md`. The reference scorer is open source in [support-rl](https://github.com/karti-ai/support-rl); `docs/EVALUATOR.md` is its specification. Three worked episodes set the quality bar in `docs/GOLD-EXAMPLES.md`. ## How it was made All 106 episodes were authored clean-room. Six authors worked on disjoint family slices. The schema, policy pack and evaluator were fixed at the start of each pass and revised whenever authors surfaced a defect in the rules (`docs/REVIEW-LOG.md`). Each author had to report the cases they were least confident about. The slices were then merged and audited across slices. Authoring surfaced **18 defects, and 15 were in the rules, not the data**. Each was fixed in the policy pack rather than patched in the case, so the ambiguity could not reach the next author. A per-slice validator could not catch dataset-level gaps; one hard criterion was live in only 3 of 24 test episodes until the cross-slice audit caught it. A pre-publication audit then found a rule that had been applied inconsistently: whether a follow-up turn about a matter that is already routed gets routed again. The rule is now stated exactly in `docs/SCHEMA.md` and checked mechanically, and 11 decisions were relabelled to match it. Every change is listed in `docs/REVIEW-LOG.md`. ## Versions - **Policy pack v1.4 (2026-09-28).** Three scoring rulings were written into the rules before any model was trained on this data: - MGN-02: a neutral, complete list of the ways a margin call can be met is not advice. - CMP-04: the 5-business-day acknowledgement window may be stated on any regulatory complaint escalation, because a chat message counts as a written complaint. - CND-03: a date derived from a stated window may be named, but an hour attached to it may not. Three train decisions changed (`allowed_timeline_claims` and one acceptable citation). The validation and test splits are unchanged. See `docs/REVIEW-LOG.md`, entries 21–23. - Policy pack v1.3: the pre-publication audit (entries 19–20). ## Intended use - RL with verifiable rewards, and evaluation of small models on policy-grounded support triage. - **Use `test` only for final measurement.** Select checkpoints on `validation`, never train on `test`, and report `test` numbers once. ## Limitations - **Small.** 106 episodes is an evaluation-and-RL set, not a pre-training corpus. - **The policy pack is fictional.** The regulatory shape is realistic, but timelines and thresholds are invented and only consistent within this corpus. - **The day-trading rules are out of date by design.** RST-01/RST-02 model the pattern-day-trader framework as it stood before June 2026. FINRA retired it through Rule 4210 amendments that the SEC approved in April 2026 (Regulatory Notice 26-10, effective 2026-06-04). The pack is fictional house policy. Do not treat it as current regulation. - **English only, text only, one fictional firm.** - **Three checks use text detectors.** Disclosure before verification, investment advice, and unstated timelines are caught by text detectors with known limits (see `docs/EVALUATOR.md`). The advice detector misses indirect recommendations ("it's probably wise to…"). The timeline detector cannot tell CMP-04's acknowledgement window from an invented resolution time of the same length. ## License [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). Credit: Karti Tripathi, `KartiOS/fintech-support-triage`. ## Disclaimer Zoomberg Brokerage is fictional and is not affiliated with any real company. All customers, account numbers and events are invented. Nothing here is legal, regulatory or financial advice. ## Citation ```bibtex @misc{tripathi2026fintechsupporttriage, author = {Karti Tripathi}, title = {Fintech Support Triage: a multi-turn support dataset with a code-computed reward}, year = {2026}, url = {https://huggingface.co/datasets/KartiOS/fintech-support-triage} } ```