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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

Cross-platform financial data unification to strengthen compliance, fraud detection and risk controls

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  • Cross-platform financial data unification to strengthen compliance, fraud detection and risk controls

Adelakun Matthew Adebowale 1, * and Olayiwola Blessing Akinnagbe 2

1 Department of Business Administration, University of Lagos.
2 Prema Consulting, Department of Digital Banking Services and Research, Nigeria.
 
Review Article
World Journal of Advanced Research and Reviews, 2023, 20(03), 2326-2343
Article DOI: 10.30574/wjarr.2023.20.3.2459
DOI url: https://doi.org/10.30574/wjarr.2023.20.3.2459
 
Received on 20 October 2023; revised on 20 December 2023; accepted on 30 December 2023
 
The rapid digitization of financial services has led to a proliferation of data across diverse platforms, including banking systems, digital wallets, trading platforms, and fintech applications. While this data explosion offers new opportunities for financial insight and innovation, it also poses significant challenges in ensuring regulatory compliance, detecting fraudulent activities, and enforcing robust risk controls. Fragmented, siloed data environments hinder real-time oversight, reduce operational visibility, and create gaps exploitable by malicious actors. Cross-platform financial data unification enabled by advanced integration architectures and artificial intelligence (AI) is becoming essential for strengthening institutional resilience and trust in modern financial ecosystems. This paper presents a comprehensive framework for unifying multi-source financial data to improve compliance monitoring, fraud detection accuracy, and dynamic risk management. It explores the integration of structured and unstructured datasets from payment gateways, Know Your Customer (KYC) systems, transaction logs, and market feeds using AI-enhanced Extract, Transform, Load (ETL) pipelines and semantic data models. The unified data layer is then processed through machine learning algorithms and anomaly detection models to flag suspicious transactions, assess behavioral risk patterns, and ensure adherence to anti-money laundering (AML) and counter-terrorist financing (CTF) regulations. Case studies from global financial institutions and fintech firms illustrate how real-time cross-platform data integration has enabled early fraud detection, improved regulatory reporting, and enhanced customer due diligence. Additionally, the paper discusses data governance, privacy concerns, and compliance with international standards such as GDPR and FATF guidelines. By aligning digital transformation with regulatory expectations, this approach enables financial institutions to proactively manage emerging risks while maintaining agility in an evolving regulatory landscape.
 
Financial data integration; Compliance; Fraud detection; Risk management; AI in finance; Regulatory technology
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2023-2459.pdf

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Adelakun Matthew Adebowale and Olayiwola Blessing Akinnagbe. Cross-platform financial data unification to strengthen compliance, fraud detection and risk controls. World Journal of Advanced Research and Reviews, 2023, 20(3), 2326-2343. Article DOI: https://doi.org/10.30574/wjarr.2023.20.3.2459

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