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Highlights
- Predicting enterprise credit risk in the telecom is increasingly complex, driven by strategic deals, bespoke pricing, intricate product customisations, and diverse credit and payment frameworks that vary by region and customer segment.
- AI-powered, data-driven models empower organisations to anticipate credit risk, detect early fraud, and implement dynamic, flexible credit limit adjustments for enterprise customers.
- Integrated governance, intelligent automation, and AI-driven insights strengthen receivables management, minimise revenue leakage and optimise cash flow to support sustainable business growth.
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AI-powered risk control
Telecom operators serving enterprise customers face increasing exposure to credit risk and fraud. As service portfolios expand and digital transformation accelerates, traditional credit checks fall short. This whitepaper introduces an AI-driven framework for credit risk profiling, enabling telcos to stay ahead by predicting financial exposure, building customer trust, and driving sustainable growth.
Challenge
As telecom operators navigate an increasingly complex digital landscape, they face several persistent challenges that impact revenue, increased bad debt, customer trust, and operational efficiency:
Identity fraud during onboarding: The use of forged identities and manipulated documents makes it difficult to verify customer legitimacy and accurately assess creditworthiness.
High-risk contracts and bad debt: Large-value contracts are more vulnerable to defaults due to business uncertainty, competitive pressures, and inefficient receivables management—often resulting in uncollectible dues.
Complex contracts with limited credit insights: Customised contract terms and intricate billing structures, combined with limited visibility into credit profiles, increase the risk of long-term financial instability.
Fraudulent activities impacting revenue and trust: Incidents such as PBX hacking, Wangiri fraud, SIM swap, and acquisition fraud lead to financial losses, billing disputes, and customer dissatisfaction.
Unresolved billing disputes and churn risk: Aged and unresolved billing issues often delay payments and, in some cases, contribute to increased customer churn.
Strategic opportunity
By harnessing advanced data and AI capabilities, telcos can move beyond traditional reactive approaches and adopt predictive, proactive strategies.
This involves analysing behavioural signals from network usage, billing data, external credit scores, and macroeconomic indicators, alongside AI-driven techniques such as anomaly detection and user behaviour modelling.
This modern approach not only enhances customer relationships but also enables:
- Reduction in bad debts through predictive analytics
- Faster credit decisions with AI-assisted automation
- Early detection of potential defaulters and fraud cases using AI/ML models
- Better cash flow management
To operationalise this strategy, Telcos need to focus on key AI-driven use cases that directly address credit risk and fraud:
- Predict probable default customers and classify them into risk tiers—such as low, medium, and high—using AI/ML models.
- Generate predictive insights into potential fraud scenarios and build a protective layer using trained data models.
- Monitor aggregate exposure with predictive inputs to support more effective portfolio management.
Framework for credit risk profiling
A robust credit risk profile for B2B customers can be developed by integrating both internal and external data sources into a structured, multi-step evaluation process. This approach strikes a balance between financial prudence and a data-driven methodology, supporting more accurate and proactive decision-making.
| Data type | Source | Description |
| Internal | Billing and payments | Days sales outstanding (DSO), overdue invoices, write-offs |
| Usage and behaviour | Historical usage pattern, consumption volatility | |
| Contract data | Contract tenure, renewals, and downgrades | |
| Customer service | Dispute frequency, customer sentiment |
| External | Credit bureaus | Payment history, trade references |
| Financials | Liquidity, revenue trends, and profitability | |
| Public records | Litigation history, bankruptcy filings | |
| Industry data | Sectoral risks, regional risk indexes | |
| Legal and regulatory compliance | Compliance status, international credit regulations |
Credit risk model structure
In a credit risk model, two broad categories of factors: Internal and external (market), should be evaluated across the key dimensions below. Each dimension must be assigned a weighted score to calculate the overall risk rating.
| Risk dimension | Example |
| Financials | Conversational AI customer support |
| Payment pattern |
Personalised product recommendations |
| Usage pattern | Automated quote generation Smart contracts |
| Dunning history | No. of instances of service suspension due to non-payment |
| Regulatory, statutory risks and external inputs | Compliance, industry volatility, disputes, credit agency ratings |
| Age on network (AoN) | No. of months active in network |
These risk dimensions change based on the type of customer, as per the illustration given below.
New customer:
| Dimension | Weightage | |
| A | Financials - (Liquidity, Cashflow, Profitability, Debt-Equity ratio, Delinquency rate) | 50% |
| B | External credit agency inputs | 40% |
| C | Regulatory / Statutory risks | 10% |
*If financials are not available, then B and C to be considered overall. Illustration only.
Based on the calculated risk score, customers are assigned ratings such as low risk, medium risk, or high risk (as illustrated in Table 1.0 below for reference). These ratings serve as the basis for determining credit limits and determining whether collateral deposits or upfront payments are required.
Table 1.0
| Sl. No | Score | Customer type | Risk category |
| 1 | >75 | High potential | Low risk |
| 2 | 50-75 | Medium potential | Medium risk |
| 3 | 35-50 | Low potential | High risk |
| 4 | <35 | Reject | Reject |
The credit limit for new customers will be assigned based on the monthly plan value * n factor per service instance, plus any security deposit collected. N factor is a multiplier factor depending on the risk category and product segment selected.
| Low | Mediumr | High |
| 2.5 | 2 | 1.5 |
*Illustrative purpose
Existing customer:
| Pt | Dimension | Weightage |
| A | Payment trend | 25% |
| B | Usage behaviour | 20% |
| C | AON | 20% |
| D | Dunning history | 25% |
| E | External market inputs (any non-compliance, industry volatility, disputes observed) | 10% |
* The above weightage is for illustration purposes
The average of the last 3-6 months' billing value will be the basis for revising the credit limit, and a multiplier can also be defined for existing customers.
| Score | Category |
| 1-50 | High risk |
| 50-75 | Medium risk |
| >75 | Low risk |
By generating credit risk models at regular intervals, telecom providers can identify potential payment defaults and detect anomalies that may indicate fraud or operational issues. This continuous, data-driven approach ensures timely updates to customer risk profiles, supporting accurate and responsive credit risk management, which ultimately improves receivables and cash flow.
Solution strategy
A converged credit risk management solution strategy ensures a holistic approach by integrating advanced technology solutions with robust organisational processes and policies. This alignment enables proactive risk detection, consistent decision-making, and improved governance. By combining automation, AI insights, and policy-based controls, organisations can achieve both agility and compliance in managing B2B credit risk.
Robust policies and processes for periodic risk profiling
Telcos should have a robust governance framework to continuously monitor the enterprise from onboarding to retirement, as follows:
- Risk assessment at the time of onboarding and at regular intervals
- Periodic assessment of compliance with contractual terms such as usage thresholds and payment timelines
- Contractual safeguards such as bank guarantees, security deposits, payment terms based on customer creditworthiness, and suspension of services upon payment default
- Robust customer lifecycle management (CLCM) process aligned with TM Forum recommended framework.
AI / ML model
Modern technology solutions play a vital role in providing more accurate, proactive, and near real-time insights for effective decision-making, safeguarding receivables and reducing bad debts.
- AI-based KYC and identity verification at onboarding to validate business credentials and prevent fraudulent sign-ups
- Analyse customer-specific data trends in usage and payment patterns to train ML models that generate automated credit risk scores based on the weightage assigned.
- Build predictive AI models that analyse industry-specific risks, sector default probabilities, external credit references and regulatory compliance and assign a credit risk score
- Continuously retrain models using dashboards, early warning signals and refine the credit risk score
- Natural language processing (NLP) based solutions can be used to analyse unstructured data from customer complaints, emails, and social media mentions. By identifying patterns, phrases, or keywords associated with fraud, telcos can minimise potential threats before they escalate
- AI-driven adaptive two-factor authentication process for higher privilege services activation
Fraud management
- Use anomaly detection models to analyse transaction patterns and behaviours in real time, enabling proactive fraud detection
- Implement robust identity verification and KYC automation to confirm customer legitimacy and prevent identity-related fraud
- Enable cross-operator data sharing and blacklisting of fraudulent identities to create a network-wide defence against repeat offenders
Conclusion
A modern, AI/ML-enabled credit risk profiling system fortifies telcos’ ability to reduce bad debts, protect revenues, enhance customer relationships, and optimise working capital.
By leveraging advanced analytics and predictive insights, telcos can transition from reactive approaches to proactive credit risk control, achieving sustainable growth and stronger financial resilience.