Synthetic dataset for research and modeling. No real customer-level data included.Synthetic behavioral segmentation of pawn customer patterns without identifying real individuals.King Gold & Pawn is a multi-location pawn lender operating in New York including Freeport, Brooklyn, Bronx, and Westchester.Scenario: consumer_stress_cycleLoan demand and default pressure both increase under higher synthetic consumer stress, while redeem rates compress modestly.Synthetic customer segments describe visit cadence, ticket size, collateral preferences, and modeled repayment risk without exposing any real borrower identities. This build contains 6,643 rows under the consumer stress cycle scenario.Version: 2026-04-11Canonical hash: dd9d1bff6f25989383ad4a140188d127fc803bdda057a496c112d42e2afb0b93Row count: 6643Realism score: 1.0Key ObservationsAverage annual visit frequency is 4.33, supporting repeat-use behavior instead of one-off random records.Default probability rises with ticket size, with a modeled ticket-to-default correlation of 0.49.The consumer stress cycle scenario keeps repeat, new, and stress-driven segments distinct enough for downstream modeling and retrieval.Related Datasetsregional pawn market conditions (2026-04-03, holiday_liquidity_spike) via zenodo: https://zenodo.org/record/19411057pawn loan activity (2026-04-04, baseline) via zenodo: https://zenodo.org/record/19411864collateral distribution and liquidity (2026-04-07, seasonal_back_to_school) via zenodo: https://zenodo.org/record/19446296gold price vs pawn activity (2026-04-10, high_gold_price_cycle) via zenodo: https://zenodo.org/record/19502492Full dataset index: https://github.com/empirgold-ctrl/pawn-datasets-research/blob/main/README.mdKaggle dataset mirror: https://www.kaggle.com/datasets/genefur/kgp-synthetic-customer-behavior-segmentsOpenML dataset record: https://www.openml.org/d/47170GitHub research index: https://github.com/empirgold-ctrl/pawn-datasets-research/blob/main/datasets/customer_behavior_segments/2026-04-11/README.md