Revenue Assurance through Root-Cause Analytics: A Machine Learning Model for Commercial Banks in Emerging Markets

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@article{1710190,
      author = {Adeoluwa Eweje, Francis Ohaegbu},
      title = {Revenue Assurance through Root-Cause Analytics: A Machine Learning Model for Commercial Banks in Emerging Markets},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {6},
      pages = {257-283},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710190.pdf},
      abstract = {Revenue assurance has emerged as a critical operational imperative for commercial banks operating in emerging markets, where complex regulatory environments, technological infrastructure challenges, and diverse customer bases create multiple revenue leakage points (Fagbore et al., 2020). This research presents a comprehensive framework for implementing machine learning-based root-cause analytics to enhance revenue assurance capabilities in emerging market banking institutions. The study addresses the fundamental challenge of revenue loss identification and prevention through advanced analytical methodologies that can adapt to the unique characteristics of developing financial ecosystems (Akinbola et al., 2020).The research methodology employed a mixed-methods approach, incorporating quantitative analysis of revenue leakage patterns across multiple emerging market banks and qualitative assessment of implementation challenges (Nwani et al., 2020). Data collection encompassed transactional records, operational metrics, and system performance indicators from commercial banks in Brazil, India, Nigeria, and South Africa over a three-year period from 2017 to 2020. The machine learning model development utilized ensemble methods combining decision trees, random forests, and gradient boosting algorithms to identify patterns indicative of revenue leakage causes (Woods & Babatunde, 2020).Key findings demonstrate that machine learning-enabled root-cause analytics can reduce revenue leakage by an average of 34% when implemented comprehensively across core banking operations (Akpe et al., 2020). The model successfully identified previously undetected patterns in transaction processing failures, pricing discrepancies, and fee calculation errors. Particularly significant was the discovery that 67% of revenue leakage incidents in emerging markets stem from system integration failures and data quality issues, rather than fraudulent activities as traditionally assumed (ILORI et al., 2020).The developed framework addresses five critical areas: transaction monitoring and anomaly detection, pricing accuracy validation, fee calculation verification, regulatory compliance tracking, and customer billing reconciliation (Gbenle et al., 2020). Implementation results showed varying degrees of success across different emerging markets, with technology-advanced markets like Brazil and India achieving higher effectiveness rates compared to markets with limited technological infrastructure (Odofin et al., 2020).Challenges identified include data quality inconsistencies, limited technical expertise, regulatory compliance complexities, and integration difficulties with legacy banking systems (Gbenle et al., 2020). The research provides practical recommendations for overcoming these barriers through phased implementation approaches, staff training programs, and strategic partnerships with technology providers (Abisoye et al., 2020).The implications for banking practice are substantial, offering a scalable solution for revenue protection that adapts to local market conditions while maintaining global best practices. Future research directions include exploring real-time analytics capabilities, expanding the model to include cryptocurrency transactions, and developing predictive maintenance applications for revenue assurance systems.},
      keywords = {revenue assurance, machine learning, root-cause analytics, emerging markets, commercial banking, financial technology, risk management, operational efficiency},
      month = {December},
  }