Chapter 1: Bridging Developed and Emerging Financial Systems in Fraud Detection Cyber Risk and Forensic Finance | Zenodo Skip to main Communities My dashboard Log in Sign up Published April 16, 2026 | Version v1 Book chapter Open Chapter 1: Bridging Developed and Emerging Financial Systems in Fraud Detection Cyber Risk and Forensic Finance Authors/Creators Magangeni, Lloyd Description Abstract Financial systems in developed economies (USA, UK) and emerging markets (exemplified by South Africa) face overlapping yet distinct threats from fraud, cybercrime, and forensic financial weaknesses. Developed markets benefit from advanced technological infrastructure, mature regulatory frameworks, and high forensic capacity, but remain vulnerable to sophisticated synthetic identity fraud, ransomware targeting payment systems, and cross-border money laundering. Emerging markets, while often leapfrogging in mobile and digital finance, struggle with weaker institutional oversight, higher insider fraud prevalence, and limited forensic accounting resources. This chapter argues that meaningful bridging between these two worlds requires: (i) regulatory harmonisation without imposing developed-country standards that ignore local realities; (ii) shared threat intelligence platforms that respect data sovereignty; and (iii) capacity-building in forensic finance through South–North partnerships. Using South Africa as a case study, we compare fraud typologies, cyber risk landscapes, and forensic readiness with those of the USA and UK. We propose a “bilateral forensic reciprocity framework” that enables cross-jurisdictional evidence sharing and joint cyber-incident response. The chapter concludes with actionable recommendations for policymakers, financial institutions, and forensic practitioners. Files Files (27.6 kB) Name Size Download all Chapter 1 Bridging Developed and Emerging Financial Systems in Fraud Detection Cyber Risk and Forensic Finance.docx md5:8871076032f52d45100e8b4d94e0b035 27.6 kB Download Additional details Dates Copyrighted 2026-04-16 References • Bhardwaj, A., & Sharma, A. (2021). Machine learning for fraud detection in US banking. Journal of Financial Crime, 28(3), 456–472. • BoE (Bank of England). (2023). CBEST threat intelligence-led testing: Annual report. • Brenner, S. (2022). Ransomware-as-a-service and financial systems. Stanford Journal of Law, Science & Policy, 15(1), 44–71. • Button, M., Shepherd, D., & Blackbourn, D. (2020). Fraud prevention in the UK and US: A comparative analysis. Security Journal, 33, 189–208. • FCA (Financial Conduct Authority). (2023). Authorised push payment scams: Performance of the contingent reimbursement model. FCA Report. • FinCEN. (2024). Ransomware trends in 2023: Financial intelligence analysis. • Jurgens, J., & Svetlova, E. (2022). Synthetic identity fraud: Challenges for ML detection. Journal of Money Laundering Control, 25(4), 712–728. • Kaseer, N., & Mlambo, L. (2023). State capture and financial fraud in South Africa: A forensic analysis. African Journal of Financial Crime, 11(2), 34–59. • Kount. (2024). 2024 synthetic identity fraud report. Kount Inc. • Levi, M., & Smith, R. (2021). Comparative financial crime governance: US, UK and emerging economies. Crime, Law and Social Change, 75, 301–325. • Moyo, T., & Pather, S. (2022). Cyber and fraud risks in South African banking: A survey. South African Journal of Economic and Management Sciences, 25(1), a4567. • Nacha. (2024). ACH network volume and value statistics. Nacha Publications. • NCSC (National Cyber Security Centre, UK). (2023). Annual review 2023: Cyber resilience of the financial sector. • Pather, S., & Mkhize, N. (2022). Forensic readiness in South African banks. Journal of Financial Regulation and Compliance, 30(4), 398–415. • Payment Systems Regulator. (2023). APP scams performance report. PSR, UK. • Romanosky, S., & Ablon, L. (2023). Delayed breach disclosure in the US financial sector. Journal of Cybersecurity, 9(1), tyad005. • SABRIC (South African Banking Risk Information Centre). (2024). Annual crime statistics 2023. • Savage, A., & Johnson, H. (2022). Cross-border money laundering: UK–SA corridors. Journal of Financial Crime, 29(2), 301–318. • State Capture Commission. (2022). Final report of the Judicial Commission of Inquiry into Allegations of State Capture. Government Printing Works, Pretoria. • Sullivan, C., & Burger, E. (2021). State-level fragmentation in US cyber incident reporting. Harvard National Security Journal, 12, 211–256. • Woods, D., & Simpson, A. (2022). Cyber insurance in the UK: Mandated controls • World Bank. (2023). South Africa financial sector assessment program. World Bank Group. 53 Views 12 Downloads Show more details All versions This version Views Total views 53 53 Downloads Total downloads 12 12 Data volume Total data volume 330.7 kB 330.7 kB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Keywords and subjects Keywords Fraud detection; cyber risk; forensic finance; South Africa; USA; UK; financial crime; emerging markets; regulatory bridging Details DOI DOI Badge DOI 10.5281/zenodo.19615376 Markdown [](https://doi.org/10.5281/zenodo.19615376) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.19615376.svg :target: https://doi.org/10.5281/zenodo.19615376 HTML <a href="https://doi.org/10.5281/zenodo.19615376"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.19615376.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.19615376.svg Target URL https://doi.org/10.5281/zenodo.19615376 Resource type Book chapter Publisher LOMAG PTY Languages English Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more Copyright 2026 Lloyd Magangeni Citation Export Technical metadata Created April 16, 2026 Modified April 16, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. Find out more on how we use cookies Accept all cookies Accept only essential cookies