The use of business intelligence and predictive analytics in detecting and managing occupational fraud in Nigerian banks

IF 0.4 4区经济学 Q4 BUSINESS, FINANCE

Journal of Operational Risk Pub Date : 2019-09-04 DOI:10.21314/jop.2019.227

C. Nwafor, O. Nwafor, Chris Onalo

引用次数: 1

Abstract

The problem of occupational fraud is one of the most wide-reaching operational risk event types in the Nigerian banking system. This event type spans many departments, roles, processes and systems and causes significant financial and reputational damage to banks. As a result, fraud presents banks with a real challenge in terms of knowing where to start. One of the main aims of this paper is to use stochastic probability models to predict aggregate fraud severity and fraud frequency within the Nigerian banking sector using historical data. Another objective is to describe how banks can develop and deploy business intelligence (BI) outlier-based detection models to recognize internal fraudulent activities. As the volume of transaction data grows and the industry focuses more closely on fraud detection, BI has evolved to provide proactive, real-time insights into fraudulent behaviors and activities. We discuss the fraud analytic development process, since it is a central issue in real application domains.

查看原文本刊更多论文

商业智能和预测分析在尼日利亚银行检测和管理职业欺诈中的应用

职业欺诈问题是尼日利亚银行体系中影响最广泛的操作风险事件类型之一。这种事件类型跨越许多部门、角色、流程和系统，并对银行造成重大的财务和声誉损害。因此，欺诈给银行带来了真正的挑战，因为他们不知道从哪里开始。本文的主要目的之一是使用随机概率模型来预测尼日利亚银行业使用历史数据的总体欺诈严重程度和欺诈频率。另一个目标是描述银行如何开发和部署基于商业智能(BI)离群值的检测模型，以识别内部欺诈活动。随着交易数据量的增长和行业对欺诈检测的关注越来越密切，商业智能已经发展到能够主动、实时地洞察欺诈行为和活动。我们讨论欺诈分析的发展过程，因为它是一个核心问题，在实际应用领域。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Operational Risk BUSINESS, FINANCE-

CiteScore

1.00

自引率

40.00%

发文量

期刊介绍： In December 2017, the Basel Committee published the final version of its standardized measurement approach (SMA) methodology, which will replace the approaches set out in Basel II (ie, the simpler standardized approaches and advanced measurement approach (AMA) that allowed use of internal models) from January 1, 2022. Independently of the Basel III rules, in order to manage and mitigate risks, they still need to be measurable by anyone. The operational risk industry needs to keep that in mind. While the purpose of the now defunct AMA was to find out the level of regulatory capital to protect a firm against operational risks, we still can – and should – use models to estimate operational risk economic capital. Without these, the task of managing and mitigating capital would be incredibly difficult. These internal models are now unshackled from regulatory requirements and can be optimized for managing the daily risks to which financial institutions are exposed. In addition, operational risk models can and should be used for stress tests and Comprehensive Capital Analysis and Review (CCAR). The Journal of Operational Risk also welcomes papers on nonfinancial risks as well as topics including, but not limited to, the following. The modeling and management of operational risk. Recent advances in techniques used to model operational risk, eg, copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory. The pricing and hedging of operational risk and/or any risk transfer techniques. Data modeling external loss data, business control factors and scenario analysis. Models used to aggregate different types of data. Causal models that link key risk indicators and macroeconomic factors to operational losses. Regulatory issues, such as Basel II or any other local regulatory issue. Enterprise risk management. Cyber risk. Big data.