Meta-Synthesis of Organisational Barriers to Decision Tree Data Analytics Adoption in Payment-Fraud Operations

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@article{1708860,
      author = {Pankajkumar Tejraj Jain, Ashok Ghimire, Reshma Leslie, Sandeep Shrestha},
      title = {Meta-Synthesis of Organisational Barriers to Decision Tree Data Analytics Adoption in Payment-Fraud Operations},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {12},
      pages = {26-33},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708860.pdf},
      abstract = {Increased incidences of payment fraud in the financial sector thus saw the increased use of advanced data analytic tools to detect and prevent fraudulent transactions. Among these tools, Decision Tree models are highly valued for their interpretability, simplicity in implementation, and their ability to classify and predict fraudulent behavior. However, from a practical point of view, and despite the technical prowess of Decision Tree analytics, adoption of these in payment-fraud operations remains scant. This study presents a meta-synthesis of existing qualitative studies to investigate the organizational barriers that hinder the adoption of Decision Tree data analytics in fraud detection processes. It synthesizes findings from diverse industry and academic sources and reveals the principal challenges these organizations face in integrating these technologies into their fraud operations. The meta-synthesis brings out some of the main barriers to adoption, among which are: cultural resistance against machine learning tools; organizational resistance against embracing algorithmic decision-making; and distrust towards any automated system. Meanwhile, there are huge skill and knowledge deficits, since many organizations have a hard time locating personnel sufficiently trained in both implementing and interpreting Decision Tree models. Other data-related issues emerged as key challenges that hindered the construction of good models: data silos fragmented within organizations and poor data quality. Moreover, legacy infrastructure and expensive integration thwarted were substantial hindrances in organizations with legacy systems. Strategic misalignment, where fraud analytics goals are not sufficiently tied to larger business objectives, inhibits the more successful adoption of analytics. It supports the view that in overcoming these barriers, organizations should nurture a data-driven culture, encourage cross-functional collaboration, and commit resources to technical infrastructure and talent development. In addition, these insights fit well within technology adoption frameworks, describing how the interference of organizational, culture, and strategic issues may affect uptake of Decision Tree analytics. Providing actionable recommendations, this study should provide fertile ground for institutions, fintech entities, and payment processors willing to reach further in fraud detection. The research further calls for additional work, especially longitudinal and sector-specific, to lay the adoption issues and opportunities within fraud prevention in a broader light throughout its metamorphosis.},
      keywords = {Decision Tree analytics, payment fraud detection, organizational barriers, technology adoption, machine learning, fraud operations, meta-synthesis, data analytics, organizational culture, infrastructure challenges, strategic alignment.},
      month = {June},
  }