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<p>Timely and reliable market intelligence is increasingly critical for business decision-making in environments characterized by rapid demand shifts and compressed decision cycles. In many Arab and emerging economies, however, firms and analysts continue to rely on lagged macroeconomic indicators and survey-based measures that offer limited visibility into short-term market dynamics. This creates a gap between the need for timely market signals and the limited availability of usable high-frequency economic indicators. At the same time, the expansion of real-time payment (RTP) infrastructures has generated high-frequency transaction data as a by-product of routine economic activity, creating new opportunities for market monitoring and business analytics.</p><p>This paper examines how RTP data can be repurposed as a practical source of market intelligence in data-constrained environments. Rather than treating transaction volumes as direct measures of economic output, the study conceptualizes RTP activity as a high-frequency signal that complements lower-frequency economic indicators when appropriately transformed and interpreted. To operationalize this idea, we propose an analytically tractable and interpretable framework that integrates RTP-derived indicators with conventional measures using a simple signal-integration approach, enabling continuous updates of underlying market conditions between official data releases.</p><p>To enhance operational usability, transaction series are expressed in growth-based and seasonally adjusted terms to mitigate distortions arising from digital adoption and platform diffusion. The framework emphasizes interpretability, robustness, and feasibility rather than predictive accuracy, reflecting the limited public availability of granular RTP datasets in many Arab economies. Illustrative applications show how RTP-derived indicators can support aggregate market monitoring, sectoral and regional analysis, and the early identification of market disruptions for firms and decision-makers operating in data-constrained contexts.</p><p>Overall, the paper positions real-time payment systems not only as financial infrastructure, but also as emerging analytics assets that can enhance business intelligence and operational decision-making across Arab economies and comparable emerging markets.</p>

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Real-time payments as market intelligence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月4日

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  • Senthilnathan Dhanasekaran

摘要

Timely and reliable market intelligence is increasingly critical for business decision-making in environments characterized by rapid demand shifts and compressed decision cycles. In many Arab and emerging economies, however, firms and analysts continue to rely on lagged macroeconomic indicators and survey-based measures that offer limited visibility into short-term market dynamics. This creates a gap between the need for timely market signals and the limited availability of usable high-frequency economic indicators. At the same time, the expansion of real-time payment (RTP) infrastructures has generated high-frequency transaction data as a by-product of routine economic activity, creating new opportunities for market monitoring and business analytics.

This paper examines how RTP data can be repurposed as a practical source of market intelligence in data-constrained environments. Rather than treating transaction volumes as direct measures of economic output, the study conceptualizes RTP activity as a high-frequency signal that complements lower-frequency economic indicators when appropriately transformed and interpreted. To operationalize this idea, we propose an analytically tractable and interpretable framework that integrates RTP-derived indicators with conventional measures using a simple signal-integration approach, enabling continuous updates of underlying market conditions between official data releases.

To enhance operational usability, transaction series are expressed in growth-based and seasonally adjusted terms to mitigate distortions arising from digital adoption and platform diffusion. The framework emphasizes interpretability, robustness, and feasibility rather than predictive accuracy, reflecting the limited public availability of granular RTP datasets in many Arab economies. Illustrative applications show how RTP-derived indicators can support aggregate market monitoring, sectoral and regional analysis, and the early identification of market disruptions for firms and decision-makers operating in data-constrained contexts.

Overall, the paper positions real-time payment systems not only as financial infrastructure, but also as emerging analytics assets that can enhance business intelligence and operational decision-making across Arab economies and comparable emerging markets.