Latest news with #RanganathTaware
Forbes
5 days ago
- Business
- Forbes
Revenue Assurance In The Age Of AI: Preventing Leakage Before It Happens
Ranganath Taware is Chief Architect at Capgemini America Inc. 24+ yrs in telecom & AI. Leads GenAI, Telecom B/OSS innovation. As the world rapidly embraces digital transformation, ensuring that every dollar a business earns is accurately billed, collected and reported has never been more important. Traditional revenue assurance methods have a hard time keeping up with the pace of change, especially as services become more virtual and data flows faster than ever. According to TM Forum, by utilizing AI technology, companies can identify potential revenue leaks preemptively and address issues proactively. This strategy represents a shift from merely recovering losses to preventing them altogether. Let's explore how AI is reshaping revenue assurance, enabling organizations to move from reactive loss recovery to proactive leakage prevention. Understanding Revenue Assurance Revenue assurance refers to the set of processes and tools used to ensure that all revenue due to a business is correctly billed, collected and reported. It encompasses ensuring billing accuracy, detecting fraud, fulfilling service obligations and adhering to regulatory compliance. In telecoms, utilities, finance and digital services, essentially, it's about maximizing revenue by minimizing losses. • Fragmented Data: Data dispersed across various systems complicates consolidation and analysis. • Data Quality Issues: Inaccurate or inconsistent data. • Billing System Complexity: Legacy systems may lack flexibility. • Inaccurate Billing: Calculation errors, incorrect charges. • Fraud Detection: Conventional systems may not handle evolving fraud schemes. • Network Security: Vulnerabilities in billing systems can be exploited. Regulatory Compliance • Evolving Regulations: Ensuring compliance with changing regulations (e.g., ASC 606) is challenging. AI's Role In Reinventing Revenue Assurance: 6 Leading Techniques In today's digital economy, revenue assurance has become a strategic priority. As businesses expand services and customer demands grow, preventing revenue loss is crucial. Artificial intelligence now plays a key role in detecting and addressing revenue leakage. Here are six innovative AI techniques reshaping revenue assurance across industries: 1. Anomaly Detection: AI can detect anomalies in billing and transaction data using clustering algorithms and autoencoders, identifying issues like duplicate charges or unauthorized activations early to reduce financial risk. 2. Predictive Modeling: Supervised learning models, such as Random Forest and XGBoost, use historical billing and usage data to forecast high-risk transactions or accounts, allowing timely action to mitigate revenue loss. 3. Time Series Forecasting: ARIMA, Prophet and LSTM models forecast revenue trends and spot deviations early, enabling timely interventions. 4. Natural Language Processing: NLP methods like sentiment analysis and topic modeling analyze support tickets, emails and chat logs to identify billing confusion or service dissatisfaction, helping address customer pain points efficiently. 5. Reinforcement Learning: Dynamic pricing strategies use reinforcement learning agents to simulate and adjust pricing based on real-time customer behavior and market conditions, improving revenue and reducing pricing errors. 6. Graph-Based AI: Graph neural networks and knowledge graphs visualize and analyze relationships among customers, services and transactions, detecting fraud rings, collusion or indirect leakage paths that traditional systems may miss. Industry Applications AI-driven RA platforms are helping telecom operators detect SIM box fraud, reconcile interconnect billing and monitor prepaid/postpaid usage in real time. For example, AI models can detect unusual call patterns or data usage spikes that indicate fraud or billing errors. A leading telecom operator implemented an AI-based RA system that eliminated revenue leakage from misconfigured accounts and billing mismatches and reduced billing errors from thousands to fewer than 40. It autonomously flagged discrepancies, such as unbilled services and duplicate discounts, and initiated workflows for resolution. Banks and fintechs use AI to ensure compliance with fee structures, detect unauthorized transactions and reconcile payment gateways. AI also helps in identifying revenue leakage from waived fees or misapplied interest rates. JPMorgan Chase (JPMC), the largest U.S. bank, has aggressively adopted artificial intelligence to modernize its operations and safeguard revenue. With over 450 AI use cases in development and a $17 billion tech budget in 2024, JPMC's AI strategy spans fraud detection, compliance, client advisory and operational efficiency. Streaming services, SaaS providers and marketplaces leverage AI to track subscription churn, enforce licensing terms and validate usage-based billing. AI ensures that monetization aligns with actual consumption. One of our clients, a major global streaming platform, implemented an AI-powered analytics system to optimize monetization and reduce revenue leakage. This solution addressed 90% of its data needs, providing real-time audience insights, improving ad targeting, reducing churn and ensuring accurate revenue matching with consumption patterns. Future Outlook AI will shift RA from a back-office function to a strategic capability embedded across the service lifecycle, from product design to customer support. Combining AI with blockchain can enhance transparency and automate revenue-sharing agreements, reducing disputes and ensuring accurate settlements. As AI takes on more decision making in RA, organizations must ensure transparency, fairness and compliance with data privacy regulations. The Bottom Line AI is not only advancing revenue assurance but fundamentally transforming it. With capabilities such as real-time detection and autonomous resolution, these technologies are positioning revenue protection as a distinct competitive advantage. For organizations aiming to excel in the digital economy, it is evident that the future of revenue assurance will be characterized by intelligence, predictiveness and proactivity. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
08-07-2025
- Business
- Forbes
Revolutionizing Wireless Networks: The Power Of AGI And Agentic AI
Ranganath Taware is Chief Architect at Capgemini America Inc. 24+ yrs in telecom & AI. Leads GenAI, Telecom B/OSS innovation. The combination of wireless networks, agentic AI and artificial general intelligence (AGI) represents a new era of intelligence and connectivity. These platforms have the potential to redefine wireless communication and offer a wide range of applications across numerous industries through network management, security and performance improvement. The leveraging of AGI and agentic AI in wireless networks will mean meeting ethical, technological and collaboration-based challenges along the way as we create a smarter, more connected world. Understanding Artificial General Intelligence AGI is AI that understands, learns and applies knowledge across various tasks at a human-like level. Unlike narrow AI focused on specific tasks, AGI can adapt and perform different functions autonomously, potentially revolutionizing multiple industries, including wireless communication. AGI learns from experience without requiring programming for every task. It employs learning in one context and generalizes it to others, showing adaptability. It also mimics human intellect to understand complex scenarios, make decisions and solve issues effectively. Comprehending Agentic AI Agentic AI is a highly advanced AI that can act on its own, make choices, optimize action and learn from feedback from environment changes—opposite the conventional AI, which is dependent on preprogrammed inputs and instructions. It can anticipate needs and operate independently, and it can optimize performance by learning from outcomes and remodeling approaches. The Role Of Wireless Networks Wireless networks are the bonding agent of today's communication, ensuring end-to-end device connection over long distances without cables. Wireless networks have become necessary to facilitate mobile communication, Internet of Things (IoT) devices and real-time data exchange, making our world today so applicable. However, more devices translate into increased bandwidth requirements, which present the risk of congestion and loss of performance. Wireless networks also expose themselves to risks such as unauthorized access, loss of data and cyberattacks. Additionally, real-time applications require low latency—something that is hard to achieve consistently within wireless networks. Integrating AGI And Agentic AI With Wireless Networks: Real-World Impact From my own experiences, integrating AGI and agentic AI into wireless networks has produced tangible benefits for people and businesses alike. I helped a telecom company use agentic AI to reallocate bandwidth during peak times. Congestion complaints dropped by 30% and throughput increased by 28%. I worked with a financial firm to deploy an AGI-based system that neutralized a credential-stuffing attack in three minutes, compared to the previous 17-minute average, without human intervention. A smart logistics hub I worked with used AGI to reroute data paths in real time, reducing latency by 35% and achieving under-50-millisecond response times for autonomous vehicles. Applications Of AGI And Agentic AI In Wireless Networks Advancements in AGI and agentic AI are leading to significant enhancements in wireless network applications, particularly within the telecommunications, healthcare and smart city sectors. AGI and agentic AI can help telecom operators in the form of efficient network management, predictive maintenance and improved customer service. AI can foresee failures for preventive maintenance to reduce downtime. AI-driven customer support can deliver personalized assistance and quick solutions to grievances, improving experiences. Intelligent resource and traffic management can ensure uninterrupted high-speed connectivity during peak hours. Number Analytics noted that "AT&T's implementation of AI-driven troubleshooting has reduced the average time to resolve network issues by 65%." T-Mobile is leveraging AI as it aims to reduce inbound customer contacts to its customer care department by 75%. According to CX Dive, Verizon Wireless's AI-based personal shopper and problem-solver platforms have reduced customer transaction times by two to four minutes. Some healthcare organizations using agentic AI have significantly improved efficiency and patient care. According to Commure, its users have reduced clinical documentation time by 81%, indicating that AI agents can help address real-world issues, enhance patient outcomes, expand access to care, reduce professional burdens and accelerate revenue growth. Smart cities can use AI and agentic AI to enhance urban efficiency and sustainability. These technologies can analyze large datasets related to traffic, energy and waste to optimize resource allocation. Looking ahead, AGI could play a crucial role by processing even more complex data and adapting to dynamic situations, enabling real-time adjustments for better resource management. For example, it could alter traffic lights to alleviate congestion or redirect resources based on real-time needs. Future Prospects And Challenges The confluence of AGI, agentic AI and wireless networks is full of great potential, but it also holds some challenges that must be addressed. Ethical concerns regarding privacy, usage of data and autonomy in decision making require the evolution of regulatory frameworks to ensure these systems operate under parameters that protect users' rights and interests. The compatibility, scalability and dependability of AGI and agentic AI-based wireless networks will be critical to enable their successful implementation. Collaboration between disparate disciplines like AI research, telecommunication, cybersecurity and ethics will be essential to harness the full potential of this technological synergy. Conclusion The convergence of AGI, agentic AI and wireless networks ushers in a new era of connectivity and intelligence. With enhanced network management, security and performance, such systems are capable of revolutionizing wireless communication and enabling a range of applications in different industries. As we transition into the future, the resolution of ethical, technological and collaborative problems will be key to unlocking the power of AGI and agentic AI within wireless networks, thereby contributing to a smarter and more connected world. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
