Commissions payouts processes are one of the key processes in the telecommunication industry, representing key functions to generate revenue. AI can address challenges in commissioning space today such as tackling fraud, predicting future payout development, and attune commissioning payouts to sales performance.
1. Commission payment triggers in telecom
Commission payments in telecom are typically triggered by sales events or performance milestones:
- New sales/activations – commissions are triggered upon new customer acquisitions, such as a contract or service activation.
- Contract renewals – reps earn commissions when existing customers renew contracts, incentivizing retention.
- Performance thresholds – bonuses are awarded for exceeding sales targets or quotas. For instance, staff may receive commissions only if store-wide sales exceed a predefined threshold.
- Product shipping – for instance, upon submission of an logistics order.
- Upsells and cross-sells – selling additional products or upgrades (e.g., increasing a data plan) triggers commissions.
- Customer retention actions – agents may earn commissions for preventing customer churn, such as convincing a subscriber to stay instead of canceling their contract.
- Residual commissions – if a performance-based threshold is achieved continuously, then the store or agent may be entitled to residual commissions (e.g. at least X activation for 3 consecutive months).
2. AI use cases in commission payments
Telecom providers leverage AI to optimize commission workflows, ensuring accuracy, efficiency, and fraud prevention. Key areas include:
- Fraud detection – machine learning models identify anomalies in sales transactions to prevent fraudulent commission claims. Fraudulent dealer activity costs telecoms $3.1 billion annually (Mobileum. (2022). Impact of AI on Incentive Compensation & Fraud Prevention.). AI prevents revenue leakage by flagging abnormal payout requests.
- Performance-based incentive optimization – AI analyzes sales data to recommend dynamic incentive structures, ensuring rewards align with behaviors that drive revenue. AI may suggest increasing commissions for upselling data plans increasing customer lifetime value.
- Predictive analytics – AI predicts future sales trends and commission costs by analyzing historical sales, seasonality, and customer behavior. This allows telecoms to adjust quotas and plan incentive budgets effectively.
- Dispute resolution and support – AI-powered chatbots and anomaly detection tools handle commission disputes efficiently, reducing administrative workload. Sales reps can query AI assistants for instant explanations of commission discrepancies.
3. Key data sources for AI models
To optimize commissions, AI relies on various data sources, including:
- CRM systems –CRM data helps AI validate commission claims based on actual sales activities.
- Sales transaction records (billing/ERP) – AI uses billing and ERP systems to verify completed sales transactions, ensuring commissions are based on actual revenue.
- Customer behavior and usage data – AI models leverage usage statistics and customer engagement data to refine commission structures.
- Contract management system data – tracks contract terms, renewal dates, and tenure, ensuring commissions align with contract milestones.
- External market intelligence – AI integrates market trends and competitor pricing to optimize incentive structures.
Figure 1 - Key data sources for commissioning payments
4. High-level architecture
An AI-driven commission system integrates multiple components to ensure seamless automation and optimization.
AI can bring the data into context, without consuming human resources via the traditional BI process.
- Data ingestion & integration – AI ingests real-time data from CRM, ERP, and contract systems into a centralized repository.
- Data processing & preparation – data is cleaned, structured, contextualized and validated to align with commission rules.
- AI/ML analytics layer – AI models perform fraud detection, commission optimization, and predictive analysis.
- Integration with commission payment systems – AI-driven insights are fed into commission payment engines, ensuring accurate payouts and compliance with incentive structures.
- Payment engine – computes comissioning payments.
Figure 2 - HL architecture
4.1 AI processing
Data ingestion considerations
- In order to rely on real time data, message streaming is preferred to ETL tools.
Data preparation considerations
- In order to leverage the data model standardization function of TMF, TMF data model is to be used for event data structure.
- TMF shall follow entities corresponding to SID model sources, and trigger types above.
Figure 3 - Data standardisation example
AI / ML layer considerations
Feature engineering layer:
- Converts raw data into structured inputs.
- Uses pipelines to optimize and store AI feature vectors.
- Employs a feature store for caching and retrieval.
Model deployment & inference:
- Deploys trained models.
- Ensures automated re-training via MLOps to address data drift.
Explainability & interpretability:
- Depending on the model complexity, it may be possible to apply SHAP and LIME to provide model transparency and compliance to model results.
- It is essential to make sure the model is not overfitted to the data it was trained on but can generalize its results to actual population and avoid bias.
- CGI calls this “Responsible AI”
Conclusion
AI can support the commisison processing, fraud prevention, incentive management, predictive analytics and dispute support, while minimising human interaction.
Client teams can utilise the results, without the need of manual analysis. This approach brings traditional data driven architecture to a whole new level, while utilising existing data pipeline architecture.
Michal Usak, CGI
