Digital fraud has become one of the most pervasive and fast-evolving threats facing emerging digital economies. In Indonesia alone, billions of dollars are estimated to be lost annually to scams delivered through calls, SMS, and cross-platform digital channels. Yet the deeper cost is less visible: a steady erosion of trust in everyday communication systems that underpin commerce, banking, and social coordination.

A new London Business School case, “From a $5B National Threat to an AI-Enabled Telco Solution (A)”, explores how this systemic challenge has forced a rethinking of what it means to secure digital infrastructure at national scale. Written by Michael G. Jacobides, M. Dalbert Ma, and Shanni Elcock, the case examines how artificial intelligence is being embedded directly into telecom networks in an attempt to detect and disrupt fraud in real time.
At the centre of the case is the collaboration between Indosat Ooredoo Hutchison (IOH), Indonesia’s second-largest telecom operator, and Tanla Platforms, a communications technology company specialising in AI-driven messaging infrastructure. Faced with a rapidly escalating fraud epidemic—where scams can scale to hundreds of thousands of messages per hour—IOH sought to explore whether AI could be integrated into the network itself to identify malicious communication patterns before harm occurs.

Rather than focusing on isolated applications or incremental filtering tools, the collaboration raises a more fundamental question: what does it take to build intelligence into the core of a national communications system? The case examines how AI-based detection systems interact with legacy telecom infrastructure, regulatory constraints, and user behaviour shaped by declining trust in unknown communications.

However, the technological dimension is only part of the story. The case highlights a set of deeper strategic tensions that emerge when prevention becomes the product. If fraud is successfully blocked, it becomes invisible—raising difficult questions about how value is defined, measured, and communicated. For telecom operators, this creates a challenge of attribution: how can investment in AI-driven protection be justified when success is experienced as the absence of incidents?

The IOH–Tanla partnership also surfaces complex organisational and ecosystem dynamics. Telecommunications operators, technology providers, regulators, and financial institutions all hold partial visibility into different stages of fraud, yet no single actor controls the entire system. As a result, responses often remain fragmented and reactive, struggling to match the speed of fraud innovation. The case explores whether AI can serve as a coordination layer across these institutional boundaries—or whether it simply shifts the boundaries of responsibility.

Rather than presenting a definitive resolution, the case situates readers within an unfolding transformation of telecom infrastructure, where AI is moving from peripheral tool to embedded system capability. This shift raises broader questions about commercial models, regulatory design, and ecosystem governance in environments where protection must operate continuously and invisibly.
By focusing on a live collaboration between IOH and Tanla, the case provides a window into how AI is being deployed not just as a technology solution, but as a mechanism for rethinking trust in digital communication systems. It invites readers to consider a central paradox: the more effective a system becomes at preventing harm, the less visible its value becomes.

A second case in the series follows the partnership as early operational indicators begin translating into measurable commercial and regulatory outcomes. As adoption expands and government support strengthens, new strategic questions emerge for Tanla: whether to scale operator-by-operator across global markets, position itself as shared anti-fraud infrastructure, deepen its Indonesia partnership, or build new trust and verification layers on top of the platform itself. The challenge shifts from proving the technology works to deciding what kind of company—and ecosystem role—the success of the platform now enables.

Together, the cases form part of London Business School’s broader exploration of platform strategy, AI-enabled transformation, and the evolving relationship between infrastructure, intelligence, and trust in digital economies.