How AI helps identify problem gamblers

Introduction: why AI is needed in Responsible Gaming

The idea is simple: the earlier you recognize risky behavior, the softer and more effective the intervention. Artificial intelligence allows you to see non-trivial patterns in millions of events: a change in the rhythm of bets, nightly "binges," canceling conclusions, "race for a loss." The goal is not to "ban everyone," but to minimize harm and support a conscious game, observing the law, privacy and ethics.

1) Data and signals: what is really useful

Event sources:

Sessions (time, duration, spin/bet intervals)
Transactions (deposits/withdrawals, cancellations, payment methods)
game metrics (volatility of games, transitions between them, frequency of bonuses);
UX behavior (reaction to Reality Check, limits, self-exclusion, timeouts);
communications (opening letters, clicks, unsubscribes, complaints);
support service (categories of cases, escalation);
devices/geo (anomalies, VPN/proxy).

Feature hints:

increase in the frequency of deposits when the result worsens (negative trend + more top-ups);
chasing: replenishment within ≤15 minutes after a major loss;
withdrawal cancellation and re-deposit in one session;
share of night activity (00: 00-05: 00) in the weekly window;
betting jumps (stake jump ratio), "sticking" in highly volatile games;
Ignoring time/budget notifications
speed of re-entry after a loss.

2) Markup and target: what do we teach the model

Purpose (label): not "dependence," but an operational definition of the risk of harm, for example:

voluntary self-exclusion in the next 30/60 days;
contacting the hotline/support with a control problem;
forced pause according to the operator's decision;
composite outcome (weighted sum of harm events).

Problems and solutions:

Event rarity → class balancing, focal loss, oversampling.
The label lag → use the mark on the horizon (T + 30), and the input features are behind the T-7...T-1.
Transparency → store a map of signs and justifications (explainability).

3) Model stack: from rules to hybrid solutions

Rules (rule-based): start layer, explainability, basic coverage.

Supervised ML: gradient boosting/logreg/trees for tabular features, probability calibration (Platt/Isotonic).

Unsupervised: clustering, Isolation Forest for anomalies → signals to manual review.

Semi-supervised/PU-learning: when there are few positive cases or labels are incomplete.

Sequence/temporal models: time patterns (rolling windows, HMM/transformers - as mature).

Uplift models: who is most likely to reduce the risk with intervention (the effect of the action, not just the risk).

Hybrid: the rules form "red flags," ML gives speed, the ensemble gives a general risk score and explanations.

4) Interpretability and fairness

Local explanations: SHAP/feature importance on the case card → why the flag went off.

Bias checks: comparison of precision/recall by country/language/attraction channel; excluding sensitive attributes.

Policy guardrails: prohibition of actions if the explanation relies on prohibited signs; manual check of border cases.

5) Action Framework: what to do after detection

Risk-rate → intervention levels (example):

Level	Speed range	Actions
L1 (soft)	0. 2–0. 4	Unobtrusive Tips: Limits, Reality Check, Learning Content
L2 (medium)	0. 4–0. 6	Timeout offer, limit promo/crash campaigns, CS contact
L3 (high)	0. 6–0. 8	Temporary limit, mandatory check up, call/chat with a trained agent
L4 (critical)	≥0. 8	Pause, help with self-exclusion, referral to hotlines/NGOs

Principles: minimally sufficient intervention, transparent communication, recording of consents.

6) Embedding in product and processes

Real-time inference: scoring in the event flow; "cold start" - according to the rules.

CS panel: player card with session history, explanations, suggested actions and checklist.

CRM orchestration: banning aggressive promos at high risk; educational scenarios instead of reactivations.

Audit trail: event-sourcing of all solutions and limit changes.

7) Privacy and compliance

Data minimization: store aggregates, not raw logs, where possible; pseudonymization.

Consent: clear purpose of processing (RG and compliance), understandable user settings.

Access and retention: RBAC, retention, access log.

Regular DPIA/audits: assessment of processing risks and protection measures.

8) Quality of models and MLOps

Online metrics: AUC/PR-AUC, calibration (Brier), latency, drift feature/predictions.

Business KPIs:

decrease in the proportion of canceled conclusions;
an increase in the share of players who set limits;
early appeals for help;
reduced night "binges."

Processes:

canary releases, monitoring and alerts;
retraining on a schedule (4-8 weeks) or when drifting;
offline/online tests (A/B, interleaving), guardrails for censorship errors.

9) Bugs and anti-patterns

Over-blocking: excessive false positives → CS burnout and player dissatisfaction. Solution: threshold calibration, cost-sensitive learning.

Black box without explanation: it is impossible to protect solutions before the regulator → add SHAP and rule overlays.

Target leaks: the use of features after the occurrence of a harm event → strict time windows.

Data leakage between users: shared devices/payments → de-duplication and device graphs.

"Quick but powerless" detection: no action playbooks → formalize the Action Framework.

10) Implementation Roadmap (10-12 weeks)

Weeks 1-2: data inventory, target definition, feature scheme, basic rules.

Weeks 3-4: prototype ML (GBM/logreg), calibration, offline assessment, explanation design.

Weeks 5-6: real-time integration, CS panel, limiters in CRM.

Weeks 7-8: Pilot 10-20% traffic, A/B intervention tests, threshold setting.

Weeks 9-10: rollout, drift monitoring, retraining regulations.

Weeks 11-12: external audit, feature correction, launch of uplift models.

11) Launch checklists

Data and features:

Raw Session/Transaction/UX Events
Time windows, aggregates, normalizations
User/device anti-leaks and de-duplication

Model and quality:

Baseline rules + ML scoring
Probability Calibration
Explainability (SHAP) in the case card

Operations:

Action Framework with Intervention Levels
CS panel and CRM policers
Event sourcing

Compliance:

DPIA/Privacy Policies
RBAC/Access Log
Retention periods and deletion

12) Player Communication: Tone and Design

Honestly and specifically: "We noticed frequent deposits after losing. We offer a limit and a pause."

No stigma: "out-of-control behaviour" instead of labels.

Selection and transparency: buttons for limit/timeout/help, understandable consequences.

Context: Bankroll guides and hotlines links.

AI is not a "punishing sword," but an early radar: it helps to offer soft support and self-control tools in time. Success is a combination of quality data, explainable models, thoughtful UX and clear playbooks. When detection is associated with correct actions and respect for privacy, harm is reduced, trust and business stability grow - players, the operator and the entire market win.