Manuscript Number : SHISRRJ236159

A Predictive Compliance Analytics Framework Using AI and Business Intelligence for Early Risk Detection

Authors(6) :-Adegbola Oluwole Ogedengbe, Solomon Christopher Friday, Temitayo Oluwaseun Jejeniwa, Maxwell Nana Ameyaw, Habeeb Olatunji Olawale, Oluchukwu Modesta Oluoha

This study presents a predictive compliance analytics framework that leverages artificial intelligence (AI) and business intelligence (BI) tools to enhance early detection of compliance risks in regulated industries. Amid growing regulatory complexity, organizations face challenges in maintaining adherence to compliance mandates while managing operational efficiency. Traditional compliance monitoring models often rely on retrospective audits and rule-based checks, which limit their responsiveness and predictive accuracy. To address these limitations, the proposed framework integrates machine learning algorithms, real-time data processing, and dynamic dashboards to identify potential compliance breaches before they materialize. The framework utilizes supervised and unsupervised learning models to detect anomalies and emerging patterns from structured and unstructured data sources such as transactional records, audit logs, employee behavior, communication records, and regulatory updates. Advanced analytics techniques including natural language processing (NLP) and decision tree classifiers are embedded to interpret regulatory texts and monitor behavioral red flags. Business intelligence tools provide visual insights, enabling compliance officers and risk managers to track key risk indicators (KRIs), generate automated alerts, and implement preventive controls proactively. A prototype of the framework was tested in the financial services sector, where real-time flagging of suspicious activities led to a 43% improvement in response time and a 27% reduction in compliance-related incidents over a six-month period. The system’s adaptability across diverse regulatory regimes and its ability to learn from historical violations make it a scalable solution for various industries, including healthcare, insurance, and energy. This research contributes to the evolving field of RegTech by demonstrating how AI-driven compliance intelligence can shift organizations from reactive to predictive risk postures. It also underscores the need for data governance, ethical AI use, and cross-functional collaboration to optimize the implementation of such frameworks. Future development will focus on refining the model’s precision, expanding its rule base with federated learning, and integrating explainable AI (XAI) for improved audit transparency and stakeholder trust.

Authors and Affiliations

Adegbola Oluwole Ogedengbe
Independent Researcher Alberta Canada.
Solomon Christopher Friday
PwC Nigeria
Temitayo Oluwaseun Jejeniwa
United Nations African Regional Centre for Space Science Technology Education English (UN-ARCSSTEE), Ife, Nigeria
Maxwell Nana Ameyaw
KPMG, USA
Habeeb Olatunji Olawale
Independent Researcher, University of Ilorin, Nigeria
Oluchukwu Modesta Oluoha
Independent Researcher Lagos, Nigeria

Predictive Analytics, Compliance Risk, Artificial Intelligence, Business Intelligence, Early Detection, Regtech, Anomaly Detection, Machine Learning, Risk Indicators, Governance.

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Publication Details

Published in : Volume 6 | Issue 4 | July-August 2023
Date of Publication : 2023-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 171-195
Manuscript Number : SHISRRJ236159
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

Adegbola Oluwole Ogedengbe, Solomon Christopher Friday, Temitayo Oluwaseun Jejeniwa, Maxwell Nana Ameyaw, Habeeb Olatunji Olawale, Oluchukwu Modesta Oluoha, "A Predictive Compliance Analytics Framework Using AI and Business Intelligence for Early Risk Detection", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 6, Issue 4, pp.171-195, July-August.2023
URL : https://shisrrj.com/SHISRRJ236159

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