Risks Associated with Innovation: The Case of Artificial Intelligence
Authors
Research teacher Higher School of Economic and Commercial Sciences University of Douala Laboratory of Architectural Finance and Management of Organizations (FARGO) (Cameroon)
Article Information
DOI: 10.47772/IJRISS.2025.910000417
Subject Category: Computer Science
Volume/Issue: 9/10 | Page No: 5085-5097
Publication Timeline
Submitted: 2025-10-14
Accepted: 2025-10-21
Published: 2025-11-13
Abstract
The digital transition is fostering the rapid rise of artificial intelligence, which, in turn, is further driving digitalization, leading to a profound and lasting transformation of society. The growing adoption of artificial intelligence in organizational activities as well as in various aspects of daily life raises the issue of potential associated dangers. This text highlights the emergence of artificial intelligence and examines various risks arising from progressive and disruptive innovations on the social fabric. It also encourages reflection on the expected benefits as well as the precautions to be considered in the context of this technological change.
Keywords
Artificial Intelligence, Risks, Innovation, Political Economy
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References
1. [AB 18] ASHTA A., BIOT-PAQUEROT, G., “FinTech evolution : Strategic value management issues in a fast changing industry”. Strategic Change 27.4 (2018), p. 301-311. [Google Scholar] [Crossref]
2. [AC 09] ALAKTIF J., CALLENS, S., “La gouvernance, ou la qualité des pouvoirs”. Marche et organisations 2 (2009), p. 15-30. [Google Scholar] [Crossref]
3. [AD 21] ASHTA A., DARLAS G., “The role of intersubjectivity and shared experience in regulating the dark side of human nature in entrepreneurial finance”. Entreprendre Innover 1 (2021), p. 58-65. [Google Scholar] [Crossref]
4. [ADA 19] ADAMS, R. “Artificial intelligence has a gender-bias problem-just ask siri”. (2019). [Google Scholar] [Crossref]
5. [AH 21] ASHTA A., HERRMANN H., “Artificial intelligence and fintech : An overview of opportunities and risks for banking, investments, and microfinance”. Strategic Change 30.3 (2021), p. 211-222. [Google Scholar] [Crossref]
6. [AI 21] ALT M.-A., IBOLYA, V., “Identifying Relevant Segments of Potential Banking Chatbot Users Based on Technology Adoption Behavior”. Market-Tržište 33.2 (2021), p. 165-183. [Google Scholar] [Crossref]
7. [ARS 21] ARSIC V.B., “Challenges of Financial Risk Management: AI Applications”. Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 26.3 (2021), p. 27-34. [Google Scholar] [Crossref]
8. [ASH 19] ASHTA A.. “Work Sharing: A Socioeconomic Perspective”. Journal of Cost Management 33 (nov. 2019), p. 17-21. [Google Scholar] [Crossref]
9. [BHH 21] BUCKMANN M., HALDANE A., HÜSER A.C., “Comparing minds and machines : implications for financial stability”. Oxford Review of Economic Policy 37.3 (2021), p. 479-508. [Google Scholar] [Crossref]
10. [BIR 11] BIRTCHNELL T., “Jugaad as systemic risk and disruptive innovation in India”. [Google Scholar] [Crossref]
11. Contemporary South Asia 19.4 (2011), p. 357-372. [Google Scholar] [Crossref]
12. [BLA 22] BLACK J., With financial tech and AI ethics expertise — what do I do next? | Financial Times. https://www.ft.com/content/d8c4b5f1-7cef-485d-affd-1ae6b683d86d. (Accessed on 11/09/2022). Fév. 2022. [Google Scholar] [Crossref]
13. [BNW 20] BERRUTI F., NEL P., WHITEMAN R., An executive primer on artificial general intelligence. McKinsey&Company. 2020. [Google Scholar] [Crossref]
14. [CC 11] CLAYTON M., CHRISTENSEN C.M., CURTIS L., JOHNSON W., Carmody. disrupting class: [Google Scholar] [Crossref]
15. how disruptive innovation will change the way the world learns. 2011. [Google Scholar] [Crossref]
16. [CHR 13] CHRISTENSEN C.M., The innovator’s dilemma: when new technologies cause great firms to fail. Harvard Business Review Press, 2013. [Google Scholar] [Crossref]
17. [CRM 13] CHRISTENSEN C.M., RAYNOR E., MCDONALD R., Disruptive innovation. [Google Scholar] [Crossref]
18. Harvard Business Review Brighton, MA, USA, 2013. [Google Scholar] [Crossref]
19. [CRO 19] CROSMAN P., Can AI help banks thwart elder abuse? | Technology At American Banker. https://www.americanbanker.com/news/can-ai-help-banks-thwart-elder-abuse. (Accessed on 11/09/2022). 2019. [Google Scholar] [Crossref]
20. [CSK 21] COUCHORO M., SODOKIN K., KORIKO M., “Information and communication technologies, artificial intelligence, and the fight against money laundering in Africa”. Strategic Change 30.3 (2021), p. 281-291. [Google Scholar] [Crossref]
21. [CU 18] CASADELLA V., UZUNIDIS D., “Innovation Capacities as a Prerequisite for Forming a National Innovation System”. In: Collective Innovation Processes: Principles and Practices 4 (2018), p. 177-199. [Google Scholar] [Crossref]
22. [CUL 21] CULLEY A., “Identifying and mitigating ‘conduct risk’in algorithmic FICC trading”. Journal of Financial Compliance 4.3 (2021), p. 267-281. [Google Scholar] [Crossref]
23. [DAV 15] DAVID H., “Why are there still so many jobs ? The history and future of workplace automation”. Journal of economic perspectives 29.3 (2015), p. 3-30. [Google Scholar] [Crossref]
24. [EBO 84] ETTLIE J., BRIDGES W.P., O’KEEFE R.D., “Organization strategy and structural differences for radical versus incremental innovation”. Management science 30.6 (1984), p. 682-695. [Google Scholar] [Crossref]
25. [ENG 22] ENGLAND A., Abu Dhabi wealth fund bets on scientific approach using quant experts [Google Scholar] [Crossref]
26. | Financial Times. https://www.ft.com/content/2c3065b6-7caf-4394-afe5-956ec5d4fe2c. (Accessed on 11/09/2022). 2022. [Google Scholar] [Crossref]
27. [FES 19] FESNAK M., “Sims, Christopher A. : Tech Anxiety : Artificial Intelligence and Ontological Awakening in Four Science Fiction Novels.” Journal of the Fantastic in the Arts 29.3 (2019), p. 458-462. [Google Scholar] [Crossref]
28. [FNV 21] FOSTER-MCGREGOR N., NOMALER O., VERSPAGEN B., “Job automation risk, economic structure and trade: a european perspective”. Research Policy 50.7 (2021), p. 104269. [Google Scholar] [Crossref]
29. [FRI 19] FRIND A., MIL-OSI Banking. https://foreignaffairs.co.nz/2019/10/28/mil-osi-banking-who-monitors-thebots/. (Accessed on 11/09/2022). 2019. [Google Scholar] [Crossref]
30. [GAV 18] GAVRILOVA T., et al., “Modeling methods for strategy formulation in a turbulent environment”. Strategic Change 27.4 (2018), p. 369-377. [Google Scholar] [Crossref]
31. [GGB 20] GARCIA-BEDOYA O., GRANADOS O., BURGOS J.C., “AI against money laundering networks : the Colombian case”. Journal of Money Laundering Control 24.1 (2020), p. 49-62. [Google Scholar] [Crossref]
32. [HG 21] HEDLEY T.P., GIRGENTI R.H., “The forensic professional’s perspective on fraud and fraud detection”. Journal of Financial Compliance 5.1 (2021), p. 85-93. [Google Scholar] [Crossref]
33. [HH 01] HOFSTEDE G.H., HOFSTEDE G., Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Sage, 2001. [Google Scholar] [Crossref]
34. [HIL 22] HILLE K. Forces driving semiconductor boom are far from over | Financial Times. https://www.ft.com/content/93366bc6-f2e9-492a-a33f-72652820a571. 2022. [Google Scholar] [Crossref]
35. [HOF 80] HOFSTEDE G., “Culture and Organizations”. International Studies of Management & Organization 10.4 (1980), p. 15-41. DOI : 10.1080/00208825.1980.11656300. eprint: https://doi.org/10.1080/00208825.1980.11656300. URL : https://doi.org/10.1080/00208825.1980.11656300. [Google Scholar] [Crossref]
36. [HRM 19] HUANG M.H., RUST R., MAKSIMOVIC V., “The feeling economy: Managing in the next generation of artificial intelligence (AI)”. California Management Review 61.4 (2019), p. 43-65. [IJS 13] ISIK O., JONES M.C., SIDOROVA A., “Business intelligence success: The roles of BI capabilities and decision environments”. Information & management 50.1 (2013), p. 13-23. [Google Scholar] [Crossref]
37. [JON 21] JONES A., Digital credit scoring for affordable housing finance: Syntellect and Reall in urban India. [Google Scholar] [Crossref]
38. https://practicalactionpublishing.com (Accessed on 11/09/2022). 2021. [Google Scholar] [Crossref]
39. [JS 22] JACOB A., SOUISSI S., “L’INTELLIGENCE ARTIFICIELLE DANS L’ADMINISTRATION PUBLIQUE AU QUÉBEC”. Cahiers de recherche sur l’administration publique à l’ère numérique, n° 5, Québec, 2022. [Google Scholar] [Crossref]
40. [KAS 21] Karina KASZTELNIK. “INNOVATIVE BANK MARKET RISK MEASUREMENT STRATEGIES USING A MODERN MACHINE LEARNING APPROACH: A NOVEL AGLOMERATIVE CLUSTERING MODEL [Google Scholar] [Crossref]
41. ANALYSIS”. Journal of Business and Accounting (2021), p. 16. [Google Scholar] [Crossref]
42. [KY 21] KAYIM F., YILMAZ A., “Financial Instrument Forecast with Artificial Intelligence”. EMAJ: Emerging Markets Journal 11.2 (2021), p. 16-24. [Google Scholar] [Crossref]
43. [LD 16] LEIPZIGER D., DODEV V., et al., “Disruptive technologies and their implications for economic policy: Some preliminary observations”. Institute for International Economic Policy Working Paper Series 13 (2016). [Google Scholar] [Crossref]
44. [LDA 22] LARKIN C., DRUMMOND OTTEN C., ÁRVAI J., “Paging Dr. JARVIS ! Will people accept advice from artificial intelligence for consequential risk management decisions ?” Journal of Risk Research 25.4 (2022), p. 407422. [Google Scholar] [Crossref]
45. [MA 21] MILANA C., ASHTA A., “Artificial intelligence techniques in finance and financial markets : a survey of the literature”. Strategic Change 30.3 (2021), p. 189-209. [Google Scholar] [Crossref]
46. [MAN 17] MANYIKA J., et al., “Jobs lost, jobs gained: Workforce transitions in a time of automation”. McKinsey Global Institute 150 (2017). [Google Scholar] [Crossref]
47. [MEN 19] MENDOZA-TELLO J.C., et al., “Disruptive innovation of cryptocurrencies in consumer acceptance and trust”. Information Systems and e-Business Management 17.2 (2019), p. 195-222. [Google Scholar] [Crossref]
48. [MLL 18] MILLAR C., LOCKETT M., LADD T., “Disruption: Technology, innovation and society”. Technological Forecasting and Social Change 129 (2018), p. 254-260. [Google Scholar] [Crossref]
49. [MUR 21] MURGIA M., Eric Schmidt creates $125mn fund for ‘hard problems’ in AI research [Google Scholar] [Crossref]
50. | Financial Times. https://www.ft.com/content/68a4ba34-9785-411c-b7f6-3a9ae2f37cd6. (Accessed on 11/09/2022). 2021. [Google Scholar] [Crossref]
51. [OCO 22] O’CONNOR S., Never mind Big Tech — ‘little tech’ can be dangerous at work too | Financial Times. https:// www. ft. com/ content/ 147bce5d- 511c- 4862 - b820-2d85b736a5f6. (Accessed on 11/09/2022). 2022. [Google Scholar] [Crossref]
52. [PCE 21] PÁLMAI G., CSERNYÁK S., ERDÉLYI Z., “Authentic and reliable data in the service of national public data asset”. PÉNZÜGYI SZEMLE/PUBLIC FINANCE QUARTERLY 66.Specia (2021), p. 52-67. [Google Scholar] [Crossref]
53. [POR 11] PORTER M.E., Competitive advantage of nations: creating and sustaining superior performance. Simon et Schuster, 2011. [Google Scholar] [Crossref]
54. [PRO 09] Processus de collecte de données : six étapes vers la réussite | Wageningen Portals. [Google Scholar] [Crossref]
55. http://www.gestionorienteeverslimpact.org/resource/processus-de-collecte-de-donn\%C3\%A9es-six-\%C3\%A9tapesvers-la-r\%C3\ [Google Scholar] [Crossref]
56. %A9ussite. (Accessed on 11/09/2022). 2009. [Google Scholar] [Crossref]
57. [QLG 21] QIU S., LUO Y., GUO H., “Multisource evidence theory-based fraud risk assessment of China’s listed companies”. Journal of Forecasting 40.8 (2021), p. 1524- 1539. [Google Scholar] [Crossref]
58. [RGP 18] RGPD. “Regulation (EU) 2016/679 of the European Parliament and of the Council”. Regulation (eu) 679 (2018), p. 2016. [Google Scholar] [Crossref]
59. [RIC 19] RICHARDS G., et al., “Business intelligence effectiveness and corporate performance management : an empirical analysis”. Journal of Computer Information Systems 59.2 (2019), p. 188-196. [Google Scholar] [Crossref]
60. [SAM 21] SAMMÉ A., “Work smarter, not harder : Artificial intelligence’s critical role in mitigating financial crime risk”. Journal of Financial Compliance 4.4 (2021), p. 344-352. [Google Scholar] [Crossref]
61. [SBV 19] SHRESTHA Y.R., BEN-MENAHEM S., VON KROGH G., “Organizational decision-making structures in the age of artificial intelligence”. California Management Review 61.4 (2019), p. 66-83. [Google Scholar] [Crossref]
62. [SHW 21] SHWAB K., AI has a Big Tech problem | Fast Company. https://www.fastcompanyco.za/technology/ai-has-abig-tech-problem-cf3c2a05-54a6-4fd8-850c-6a0690691a24. (Accessed on 11/09/2022). 2021. [Google Scholar] [Crossref]
63. [SIN 20] SINHA N., et al., “Robotics at workplace: An integrated Twitter analytics–SEM based approach for behavioral intention to accept”. International Journal of Information Management 55 (2020), p. 102210. [Google Scholar] [Crossref]
64. [SOV 18] SOVIANY C., “The benefits of using artificial intelligence in payment fraud detection: A case study”. Journal of Payments Strategy & Systems 12.2 (2018), p. 102-110. [Google Scholar] [Crossref]
65. [TCY 19] TAMBE P., CAPPELLI P., YAKUBOVICH V., “Artificial intelligence in human resources management: Challenges and a path forward”. California Management Review 61.4 (2019), p. 15-42. [Google Scholar] [Crossref]
66. [TUR 09] TURING A.M., “Computing machinery and intelligence”. Parsing the turing test. Springer, 2009, p. 23-65. [Google Scholar] [Crossref]
67. [UZU 20] UZUNIDIS D., “Introduction générale. De la systémique de l’innovation aux systèmes complexes”. Marché et organisations, 3 (2020), p. 9-15. [Google Scholar] [Crossref]
68. [VAR 19] VARTANIAN T.P., Regulators’ push for innovation shouldn’t come at expense of prudence | American Banker. https://www.americanbanker.com/opinion/regulators-push-for-innovation-shouldnt-come-at-expense-ofprudence. (Accessed on 11/09/2022). 2019. [Google Scholar] [Crossref]
69. [VEK 21] VEKIARIDES N., Deepfakes: An insurance industry threat | PropertyCasualty360. https://www.propertycasualty360.com/2021/09/14/deepfakes-an-insurance-industrythreat/?slreturn=20221009141252. (Accessed on 11/09/2022). 2021. [Google Scholar] [Crossref]
70. [VIL 18] VILLANI C., et al., Donner un sens à l’intelligence artificielle : pour une stratégie nationale et européenne. Conseil national du numérique, 2018. [Google Scholar] [Crossref]
71. [WR 09] WITT M.A., REDDING G., “Culture, meaning, and institutions: Executive rationale in Germany and Japan”. Journal of International Business Studies 40.5 (2009), p. 859-885. [Google Scholar] [Crossref]
72. [WWY 15] WAN F., WILLIAMSON P.J., YIN E., “Antecedents and implications of disruptive innovation: Evidence from China”. Technovation 39 (2015), p. 94-104. [Google Scholar] [Crossref]
73. [XU 22] XU L., et al., “Analysis on risk awareness model and economic growth of finance industry”. Annals of Operations Research (2022), p. 1-23. [Google Scholar] [Crossref]
74. [YW 21] YANG S., WU H., “The Global Organizational Behavior Analysis for Financial Risk Management Utilizing Artificial Intelligence”. Journal of Global Information Management (JGIM) 30.7 (2021), p. 1-24. [Google Scholar] [Crossref]
75. [ZAB21] ZHANG B.Z., ASHTA A., BARTON M.E., “Do FinTech and financial incumbents have different experiences and perspectives on the adoption of artificial intelligence ?” Strategic Change 30.3 (2021), p. 223-234. [Google Scholar] [Crossref]
76. [ZEN 20] ZENG J., “Artificial intelligence and China’s authoritarian governance”. International Affairs 96.6 (2020), p. 1441-1459. [Google Scholar] [Crossref]
77. [ZRM 21] ZHANG Y., RAMANATHAN L., MAHESWARI M., “A hybrid approach for risk analysis in e-business integrating big data analytics and artificial intelligence”. Annals of Operations Research (2021), p. 1-19. [Google Scholar] [Crossref]
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