Risk Data

The Risk Data section of the Accounts page provides a focused view of model-driven risk signals across all evaluated accounts. It is designed to help risk managers, analysts, and trading supervisors identify accounts exhibiting suspicious betting behaviour, financial exposure risks, or model-specific anomalies in real time.

What it shows

Each account listed in this section has been assessed by one or more Insight Tech risk models. The table layout highlights key behavioural and scoring metrics that directly contribute to risk assessment and ticket validation processes.

The following data points are prominently shown for each account:

• CCF (Customer Confidence Factor): The core risk score used in MTS. It (generally) ranges from 0.00 to 10.00 and reflects the operator's confidence in the account’s betting integrity and value. Lower scores indicate reduced confidence and trigger stricter validation rules in products like Cash-Out and Acceptance. Default CCF is 1.00 for new accounts.

  • sCCF (Suggested Confidence Factor): Machine-generated confidence scores for every customer based on their historical betting behaviour.

• LBS (Late Bet Score): Measures the frequency and context of late bets placed relative to market latency, suspension, and bet placement time. Higher values indicate higher suspicion.

• Marker Score: Evaluates bet patterns to identify high-performing bettors who consistently places winning bets before markets close, constantly beating closing odds.

• Bot Score: Indicates the likelihood of non-human behaviour (e.g. rapid bet patterns, consistent placement timeframes, stake irregularities, etc.).

• Category: Automatically assigned based on risk score thresholds (e.g. Very Bad, Bad, Borderline, Good, Excellent).

• Linked: Displays whether the account is connected to other accounts via shared device or IP, indicating possible multi-accounting or syndicate behaviour.

Other contextual fields shown include device and IP address, account status, and P&L—these support the interpretation of model scores and are especially useful when investigating suspicious accounts.

Use cases

The Risk Data section is primarily used by:

• Trading and Risk Analysts: To monitor high-risk accounts flagged in real time and assess their potential impact on active tickets.

• Integrity Teams: To investigate behavioural anomalies based on model outputs.

• Operations: To determine whether model scores should trigger restrictions or monitoring flags in external systems.

CCF thresholds are operator-configurable, allowing for custom workflows (e.g. alerting, suspension, manual review) to be triggered based on score outputs.