Toward business integrity modeling and analysis framework for risk measurement and analysis

Victor Chang, Raul Valverde, Muthu Ramachandran, Chung-Sheng Li

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
42 Downloads (Pure)


Financialization has contributed to economic growth but has caused scandals, misselling, rogue trading, tax evasion, and market speculation. To a certain extent, it has also created problems in social and economic instability. It is an important aspect of Enterprise Security, Privacy, and Risk (ESPR), particularly in risk research and analysis. In order to minimize the damaging impacts caused by the lack of regulatory compliance, governance, ethical responsibilities, and trust, we propose a Business Integrity Modeling and Analysis (BIMA) framework to unify business integrity with performance using big data predictive analytics and business intelligence. Comprehensive services include modeling risk and asset prices, and consequently, aligning them with business strategies, making our services, according to market trend analysis, both transparent and fair. The BIMA framework uses Monte Carlo simulation, the Black-Scholes-Merton model, and the Heston model for performing financial, operational, and liquidity risk analysis and present outputs in the form of analytics and visualization. Our results and analysis demonstrate supplier bankruptcy modeling, risk pricing, high-frequency pricing simulations, London Interbank Offered Rate (LIBOR) rate simulation, and speculation detection results to provide a variety of critical risk analysis. Our approaches to tackle problems caused by financial services and the operational risk clearly demonstrate that the BIMA framework, as the outputs of our data analytics research, can effectively combine integrity and risk analysis together with overall business performance and can contribute to operational risk research.

Original languageEnglish
Article number3145
JournalApplied Sciences
Issue number9
Publication statusPublished - 30 Apr 2020

Bibliographical note

Funding Information:
This work is supported by VC Research (VCR 0000018).

Publisher Copyright:
© 2020 by the authors.


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