Paper Contents
Abstract
In the modern telecommunications landscape, billions of mobile numbers are actively subscribed across various telecom service providers. However, over time, a substantial proportion of these numbers become inactive due to user abandonment, extended non-use, or, more critically, the death of the subscriber. Despite this, many telecom providers continue to maintain these numbers as active in their databases, which presents significant challenges and risks. These include the potential misuse of inactive or deceased users mobile numbers, fraudulent SIM swaps, identity theft, and inefficient management of valuable network resources. Additionally, such oversight contributes to unnecessary strain on mobile infrastructure and inaccurate customer data analytics.To address these issues, this project proposes the development of an AI-driven, cross-telecom verification framework designed to detect and validate the liveness of mobile subscribers. The proposed system integrates multiple data sources, including real-time mobile usage patterns (calls, SMS, mobile data usage, location updates), official government death registries, and secure biometric or OTP (One-Time Password) revalidation protocols. By analyzing behavioral anomalies and correlating them with authoritative death records or user-inactivity thresholds, the system can intelligently flag and verify inactive or potentially deceased accounts.The framework emphasizes a privacy-preserving architecture, ensuring that sensitive subscriber information is processed securely and in compliance with national data protection regulations, such as the Personal Data Protection Bill (PDPB) in India or GDPR in the EU. Moreover, the solution is designed to be highly scalable, allowing seamless integration across multiple telecom providers and geographic regions. Key features of the system include automated periodic liveness checks, alerts to family members or emergency contacts for confirmation, reactivation mechanisms for falsely flagged accounts, and central coordination with national identity databases (such as Aadhaar) for cross-verification. The AI models employed use machine learning algorithms capable of detecting subtle changes in user behavior that could indicate inactivity or death, thereby reducing false positives and improving detection accuracy.Ultimately, this AI-driven framework aims to modernize mobile network operations by enhancing digital identity integrity, curbing fraud, and optimizing the utilization of mobile number resources. By proactively verifying the status of mobile subscribers, telecom companies can improve trust, service quality, and operational efficiency in an increasingly interconnected digital ecosystem.KeywordsMobile Subscriber Liveness Verification, Telecom Network Optimization, AI-Driven User Validation, Inactive Mobile Numbers Detection, Fraud Prevention in Telecommunications, Behavioral Pattern Analysis, SIM Swap Fraud Detection, Government Death Registry Integration, Biometric Revalidation, OTP-based User Authentication, Mobile Usage Monitoring, Digital Identity Verification, Cross-Telecom Verification Framework, Privacy-Preserving AI Systems, Telecom Data Analytics, Subscriber Inactivity Analysis, Identity Management in Telecom, GDPR-Compliant Data Processing, Aadhaar Integration, Scalable AI Architecture.INTRODUCTIONIn today's digital era, mobile phones have evolved far beyond their original purpose as simple communication devices. They now serve as multifaceted tools for managing personal, professional, and financial activities. At the center of this digital ecosystem lies the mobile numbera unique identifier tied closely to a persons identity. Mobile numbers are used not only for making calls or sending text messages but also for authentication in online banking, social media platforms, digital wallets, e-commerce portals, and various government services. As such, mobile numbers have become indispensable digital identities.The global telecommunications sector is home to billions of mobile subscribers. With the rapid increase in population and digital adoption, telecom providers manage vast databases of active users across various regions and networks. However, these databases are not always accurate or up to date. A significant challenge that telecom operators face is determining the current status of a mobile number's ownerspecifically, whether the individual is still alive or has passed away. In the absence of effective verification mechanisms, mobile numbers of deceased individuals often remain active in the system for extended periods. This phenomenon contributes to serious issues such as data inaccuracy, resource mismanagement, and security vulnerabilities.Problem ContextOne of the most alarming consequences of inactive mobile numbers belonging to deceased users is the potential for misuse. Fraudsters may target these numbers for SIM swap fraud, identity theft, or social engineering attacks. In SIM swap fraud, an attacker gains control of a victims mobile number and subsequently intercept sensitive communications such as OTPs (One-Time Passwords) and authentication messages. If the original user is deceased and no one has formally deactivated the number, the system may still treat the number as active, making it vulnerable to fraudulent activities.Furthermore, mobile numbers that are no longer in use may be recycled by telecom companies and reassigned to new users. If the number was previously linked to banking or government services, the new user may inadvertently gain access to the deceased person's digital footprint, creating legal, ethical, and operational risks. For telecom companies, this also results in bloated databases filled with inactive subscribers, which can distort customer analytics, marketing efforts, network planning, and revenue forecasting.Challenges in DetectionThe primary reason for this gap lies in the absence of a robust, real-time liveness verification mechanism. Telecom operators currently rely on indirect indicators such as non-usage of voice, data, or messaging services over a certain time period to flag inactive accounts. However, such indicators are insufficient to determine if a user is simply inactive, has moved to another provider, or is deceased. A user might still be alive but using the SIM in a second phone, or they might be hospitalized or traveling. Conversely, a user might be deceased, yet automated processes and residual usage (like auto-renewals, family usage, or bots) might falsely suggest activity.Additionally, there is often a lack of coordination between telecom service providers and official government agencies that maintain death registries, such as civil registration systems or national identity databases (e.g., Aadhaar in India). Even where such data exists, there are legal and privacy-related restrictions that hinder its integration with private telecom databases.Need for an Ethical and Proactive ApproachGiven the high stakes involved, there is an urgent need for a proactive, ethical, and technologically sound framework that can help telecom providers accurately determine the liveness of mobile subscribers. This system must strike a balance between data utility and privacy, ensuring that any verification mechanism is not intrusive or misused. The approach must adhere to national and international data protection laws such as Indias Personal Data Protection Bill (PDPB), the General Data Protection Regulation (GDPR) in Europe, and similar frameworks elsewhere.An AI-driven verification model offers a promising solution to this challenge. By leveraging advanced machine learning techniques and behavioral analytics, it becomes possible to assess usage patterns of mobile subscriberssuch as frequency of calls, SMS activity, internet usage, location data, and device fingerprinting. These behavioral patterns can be used to flag anomalies or periods of sudden inactivity. When coupled with external databases such as government death registries or civil records, the system can cross-reference and validate if the inactivity aligns with an actual death.Role of Artificial Intelligence and Machine LearningArtificial Intelligence (AI) and Machine Learning (ML) can be instrumental in identifying and analyzing behavioral patterns of users over time. For instance, AI models can be trained on labeled datasets to recognize typical usage behaviors for different age groups, regions, and user types. These models can then detect deviations from normal patternssuch as a sudden drop in all communication activitiesthat could signal a possible death or long-term abandonment of the number.Moreover, AI models can incorporate anomaly detection algorithms to raise alerts when the activity level of a user significantly diverges from their historical behavior. These alerts can be fed into a larger decision-making pipeline that triggers automated revalidation protocols, such as sending OTPs to the registered devices, initiating biometric authentication via smartphone apps, or even sending notifications to emergency contacts or registered family members.Cross-Verification with Government RecordsWhile behavioral analysis can provide strong indications, definitive verification can be achieved through the integration of government-maintained death registries. By securely cross-referencing telecom subscriber details with civil records, telecom operators can identify confirmed deaths. This approach ensures high accuracy while minimizing false positives. Furthermore, coordination with national identity databases, such as Aadhaar in India, can enhance the robustness of the verification process by allowing biometric revalidation or linking mobile numbers with verified identities.However, integrating such systems requires careful policy planning, inter-agency collaboration, and strong cyber security safeguards. It must be ensured that access to civil records is provided only through secure, encrypted channels and that no personally identifiable information (PII) is stored or misused by telecom providers. Ethical governance mechanisms, including consent protocols and audit trails, must be incorporated to maintain transparency and accountability.Scalability and Multi-Network ImplementationOne of the strengths of the proposed system is its scalability across telecom providers. The framework can be implemented as a shared platform or an industry-wide standard where different telecom companies contribute anonymized user behavior data for centralized analysis. Alternatively, each provider can maintain its own internal AI-driven verification engine with standardized APIs to interact with national databases.This cross-network model ensures uniformity, prevents duplication of efforts, and fosters data standardization across the industry. Such collaboration can be encouraged by telecom regulatory bodies, which can mandate or incentivize the adoption of liveness verification mechanisms as part of national digital hygiene initiatives.Social and Operational BenefitsBeyond fraud prevention and resource optimization, the system also offers social value. By notifying family members about dormant or deceased-linked mobile accounts, the telecom providers can support closure and offer options for legacy management of digital assets. Moreover, it provides an avenue for digital estate management where family members can deactivate services, transfer numbers, or archive digital communications.On an operational level, telecom companies benefit from cleaner databases, more accurate analytics, improved customer segmentation, and better resource allocation. It also enables more efficient mobile number recycling, avoiding legal disputes or ethical concerns tied to reusing numbers of deceased individuals.The mobile number is no longer just a contact pointit is a key to an individuals digital life. As such, the responsibility of managing these digital identities must extend beyond technical upkeep to include ethical considerations around liveness, ownership, and legacy. The proposed AI-driven framework for verifying the liveness of mobile subscribers addresses a critical gap in current telecom operations by combining the power of behavioral analytics with authoritative data from civil registries. This solution is not only feasible but also imperative for creating a safer, more responsible, and more efficient digital telecommunications infrastructure.
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Copyright © 2025 VALLEM RANADHEER REDDY. This is an open access article distributed under the Creative Commons Attribution License.
