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Proposal: Auditable “High-Risk Gambling Indicators” (Regulatory Definition)
Companion to Model Bill 03
This document proposes a regulatory definition and an
initial, auditable indicator set for identifying patrons at materially elevated
risk of gambling-related harm in mobile sports wagering.
1) What “High-Risk Gambling Indicators” Should Mean (Proposed Rule Text)
Proposed definition (for agency rulemaking)
"High-Risk Gambling Indicator" means an objective, quantifiable condition or event that is
calculated from auditable operator records and that, when met, indicates a materially elevated
risk that a patron is experiencing or is likely to imminently experience gambling-related harm,
including financial distress.
High-Risk Gambling Indicators shall:
(1) be specified as clear thresholds and time windows (e.g., "more than N deposits in 24 hours");
(2) rely on data fields that are available to the operator as part of ordinary account, payment,
and wagering operations;
(3) be designed to detect harmful patterns such as loss-chasing, rapid escalation in spend,
unusually long play sessions, repeated failed deposits, and repeated cancellation of withdrawals;
(4) be documented in a public technical specification that includes a data dictionary, formulas,
and examples; and
(5) be enforceable through audit, including by reproducing indicator calculations from raw event logs.
2) What “Auditable” Should Require (Minimum Technical/Audit Spec)
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Deterministic calculations: indicators are rule-based (or have a rule-based
“bright line” trigger) so an auditor can reproduce the result from raw logs.
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Event-level logging: operators retain a tamper-evident log of deposits,
wagers, wins/losses, withdrawals, cancellations, limit changes, and RG-tool use.
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Indicator event log: when an indicator is met, the operator records an
entry with (a) indicator ID, (b) timestamp, (c) inputs/values (e.g., deposit_count=5),
(d) time window, and (e) action taken.
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Uniform definitions: key computed terms (e.g., “net loss”) use a single
statewide definition; “promotional credits” are excluded from loss calculations unless the
rule explicitly includes them.
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Cross-operator option: for the strongest protections, indicators can be
calculated at a statewide layer across all operators (especially net loss and deposit
escalation), but the indicator definitions should work per-operator if statewide
integration is not yet implemented.
3) Proposed Initial Indicator Set (Sports Wagering–Relevant)
These are starting-point indicators suitable for rulemaking. Threshold values should be
calibrated using state-specific data and revised periodically.
Financial/Payment Markers
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HR-01: Rapid deposit frequency — ≥
[N] deposits in a rolling
24-hour period.
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HR-02: Rapid deposit escalation — the patron’s total deposits over the last
7 days are ≥ [X]× the patron’s typical 7-day deposits
baseline (e.g., median of prior 8 weeks).
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HR-03: Large net loss in short window — net loss exceeds
[$X]
in [7] days (or exceeds [Y%] of typical monthly discretionary income,
if the jurisdiction adopts an affordability framework).
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HR-04: Repeated failed/denied deposits — ≥
[N] failed deposit
attempts within [24] hours.
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HR-05: Multiple payment instruments — ≥
[N] distinct payment
methods added or used within [7] days.
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HR-06: Withdrawal reversal / cancellation — ≥
[N] cancelled or
interrupted withdrawals within [30] days (or cancellation rate ≥ [Z%]).
Behavioral/Session Markers
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HR-07: Extended active wagering sessions — active wagering time exceeds
[T] hours in a day on [M] or more days in a rolling [14]-day
period.
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HR-08: Overnight wagering pattern — ≥
[P%] of wagers placed between
[12:00 a.m.–6:00 a.m.] on [M] or more nights in [14] days.
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HR-09: In‑play / micro‑bet intensity — ≥
[N] in‑play wagers in a
single session or ≥ [N] wagers within [60] minutes, sustained over
[K] sessions in [7] days.
Loss-Chasing Markers
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HR-10: Deposit-after-loss (“chasing”) pattern — within
[10] minutes
after a net losing sequence of ≥ [$X], the patron makes a new deposit, and this
pattern repeats ≥ [N] times in [7] days.
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HR-11: Stake escalation after losses — average stake size in the 24 hours after a
net losing day is ≥
[X]× the patron’s baseline stake size.
Responsible-Gambling Tool Signals
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HR-12: Repeated limit increases — ≥
[N] increases to deposit/loss limits
within [30] days, or any increase above a high threshold set by rule.
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HR-13: Repeated timeouts / previous self-exclusion — repeated short timeouts
(≥
[N] in [30] days) or any prior self-exclusion within [24] months.
4) Calibration, Governance, and Safeguards (Proposal)
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Calibration approach: start with conservative thresholds; evaluate false positives
and missed harms quarterly; revise through transparent rule updates.
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Tiering option: define Tier 1 (friction + messaging), Tier 2 (mandatory limits/timeouts),
Tier 3 (hard stop). If the statute mandates a hard stop, reserve it for the most predictive indicators
or for multi-indicator combinations.
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Equity/fairness review: require periodic disparate-impact analysis and publish aggregate
results; adjust indicators that create unjustified bias.
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Privacy: avoid collecting sensitive external data unless strictly necessary; use minimization,
retention limits, and purpose limitation (no marketing use).
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Auditability: require third-party system audits and regulator access to event logs sufficient to
replicate indicator triggers and operator responses.
5) Research & Regulatory Anchors (Selected)
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UK Gambling Commission, Customer interaction formal guidance (remote) (July 2019; archived / “not in effect”),
which enumerates categories like time/spend, cancelled withdrawals, failed deposits, use of tools, chasing losses, and
in-play intensity.
Source PDF
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Swedish Gambling Authority (Spelinspektionen), Guidance: Duty of care, including example indicators such as
active gambling time, chasing losses, type of game, raising deposit limits, interrupted withdrawals, and deposit denials.
Source PDF
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Edson et al., An Empirical Attempt to Operationalize Chasing Losses in Gambling Utilizing Account-Based Player
Tracking Data (open access on PubMed Central), supporting the idea that “chasing losses” can be operationalized
using account-based tracking data.
Source