Manuscript Number : SHISRRJ236161

Enhancing Compliance Risk Identification Through Data-Driven Control Self-Assessments and Surveillance Models

Authors(5) :-Adegbola Oluwole Ogedengbe, Temitayo Oluwaseun Jejeniwa, Habeeb Olatunji Olawale, Solomon Christopher Friday, Maxwell Nana Ameyaw

In an increasingly complex regulatory environment, financial institutions and corporate entities are under heightened pressure to proactively identify and manage compliance risks. Traditional methods of risk identification, often reliant on manual reviews and static checklists, have proven insufficient in detecting emerging threats and subtle control failures. This paper presents an advanced framework for enhancing compliance risk identification through the integration of data-driven Control Self-Assessments (CSAs) and surveillance models. By leveraging structured and unstructured data, machine learning algorithms, and real-time analytics, the proposed framework transforms compliance monitoring into a dynamic and predictive process.The study explores the limitations of conventional CSA practices and introduces a methodology that incorporates transactional data, behavioral analytics, and system-generated audit trails. These inputs are processed through supervised and unsupervised learning models to uncover anomalies, trend deviations, and potential compliance breaches. Furthermore, the paper evaluates the role of natural language processing (NLP) in interpreting textual data from emails, reports, and case files to detect risk indicators not typically captured by structured systems. The integration of such models within CSA processes enables a continuous, intelligence-driven approach to identifying gaps in control effectiveness, policy adherence, and regulatory compliance.Case studies from global financial institutions are examined to demonstrate the real-world application of data-driven CSA models and their impact on early risk detection, improved audit readiness, and reduced compliance incidents. The findings confirm that organizations employing data-centric surveillance in tandem with enhanced CSA frameworks achieve a measurable uplift in compliance oversight and operational resilience.This paper concludes by outlining the strategic implications of embedding these advanced models within enterprise risk management systems, emphasizing the need for cross-functional collaboration, data governance, and technology investment. The proposed approach not only addresses current compliance challenges but also prepares organizations to adapt to evolving regulatory landscapes with agility and precision.

Authors and Affiliations

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

Compliance Risk, Control Self-Assessment, Data-Driven Surveillance, Machine Learning, Predictive Analytics, Behavioral Analytics, Regulatory Compliance, Natural Language Processing, Risk Detection, Audit Readiness.

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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) : 224-248
Manuscript Number : SHISRRJ236161
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

Adegbola Oluwole Ogedengbe, Temitayo Oluwaseun Jejeniwa, Habeeb Olatunji Olawale, Solomon Christopher Friday, Maxwell Nana Ameyaw, "Enhancing Compliance Risk Identification Through Data-Driven Control Self-Assessments and Surveillance Models", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 6, Issue 4, pp.224-248, July-August.2023
URL : https://shisrrj.com/SHISRRJ236161

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