FinTech Innovation and Competitive Advantage: Examining the Role of Data Analytics Capabilities
DOI:
https://doi.org/10.66592/xb8ymk71Abstract
FinTech firms compete on innovation, yet innovation alone does not explain which firms sustain advantage: many launch products that incumbents or rivals copy within months. This paper argues that data analytics capability (DAC), the organisational capacity to acquire, process and act on data, is the resource that makes FinTech innovation both faster and harder to imitate, and that regulatory sandboxes amplify the advantage innovation confers by lowering the cost of experimentation. A research model with DAC as an antecedent of innovation, innovation as a mediator to competitive advantage, and sandbox participation as a moderator is examined in a simulation of 400 FinTech firms. DAC has a standardised effect of 0.79 on innovation; innovation carries 0.47 to competitive advantage and DAC a direct 0.40, so that DAC’s total effect (0.77) is split almost evenly between the innovation route and the direct route; the sandbox–innovation interaction is 0.26, meaning innovation yields more advantage for sandbox participants. The paper interprets DAC as a dynamic capability, discusses why its direct effect persists beyond innovation,References
1. 2. 3. 4. 5. 6. 7. Kaur, M., AlZubi, A. A., Jain, A., Singh, D., Yadav, V., & Alkhayyat, A.
(2023). DSCNet: deep skip connections-based dense network for
ALL diagnosis using peripheral blood smear images. Diagnostics,
13(17), 2752.
Devulapalli, N. (2019). Post-quantum cryptography for database
security: Preparing for the impact of quantum computing on
encrypted data storage and secure queries. Journal of Critical
Reviews, 6(1), 614–620. https://doi.org/10.69980/jcr.v6:i1.13396
Jain, Jaishree, Arpit Jain, Saurabh Kumar Srivastava, Chaman Verma,
Maria Simona Raboaca, and Zoltán Illés. “Improved security of
e-healthcare images using hybridized robust zero-watermarking
and hyper-chaotic system along with RSA.” Mathematics 10, no. 7
(2022): 1071.
Mahadasu, S. S. (2025). AI-enhanced service management and
orchestration for rApp and xApp lifecycle automation in Open RAN.
Communications on Applied Nonlinear Analysis, 32(2s), 794–807.
https://doi.org/10.52783/cana.v32.6716
Kumar, S., Jain, A., & Moparthi, N. R. (2022). Enhanced method
of object tracing using extended Kalman filter via binary search
algorithm.
K han, S. (2022). The use of ar tif icial intelligence in ESG
(environmental, social, and governance) investing: A study
on AI models for sustainability analytics in asset and wealth
management. International Journal of Multidisciplinary and
Scientific Emerging Research, 10(1), 431–439. https://doi.
org/10.15662/IJMSERH.2022.1001046
Ahuja, R. (2022). Application of static application security
testing (SAST) in CI/CD pipelines for early detection of insecure
coding practices through syntax tree analysis and custom rule
sets. International Journal on Recent and Innovation Trends in
Computing and Communication, 10(2), 80–87. https://www.ijritcc.
org/index.php/ijritcc/article/view/11922
8. Talasila, D. (2023). Implementation of cloud-based disaster
recovery models for minimizing business downtime and ensuring
operational continuity through geographically distributed backup
systems. International Journal on Recent and Innovation Trends
in Computing and Communication, 11(8), 867–874. https://www.
ijritcc.org/index.php/ijritcc/article/view/11934
9. Devulapalli, N. (2020). Implementation of compliance-as-code
in regulated DevSecOps environments for automating audit
readiness and reducing human error through scripted control
validation. Journal of Critical Reviews, 7(1), 2651–2657. https://
doi.org/10.69980/jcr.v7:i1.13405
10. Mahadasu, S. S. (2025). Distributed AI-RAN inference placement
for joint radio optimization and edge workload acceleration.
International Journal of AI Electronics and Nexus Energy, 1(1),
39–44. https://doi.org/10.64751/7ep35m50
11. Bhanushali, B. (2023). Blockchain for trade finance: Enhancing
cross-border payment transparency, reducing processing time, and
mitigating fraud through distributed ledger systems. International
Journal on Recent and Innovation Trends in Computing and
Communication, 11(9), 5690–5697. https://www.ijritcc.org/index.
php/ijritcc/article/view/11932
12. Anchala, S. (2023). Utilization of blockchain technology in cloud
storage systems for enhancing data integrity and preventing
unauthorized modifications through decentralized validation.
International Journal on Recent and Innovation Trends in
Computing and Communication, 11(5), 629–637. https://www.
ijritcc.org/index.php/ijritcc/article/view/11942
13. Aggarwal, A. (2021). Utilization of continuous vulnerability
scanning in DevSecOps pipelines for preemptive threat detection
and secure code deployment through automated security checks.
NeuroQuantology, 19(1), 427–442. https://doi.org/10.48047/
nq.2021.19.1.NQ21055
14. Mahajan, R. A., Dey, R., Khan, M., Mohd Su’ud, M., Alam, M. M., &
Jadhav, P. (2025). Enhancing personalization in IoT-based health
monitoring via generative AI and transfer learning. Egyptian
Informatics Journal, 32, Article 100788. https://doi.org/10.1016/j.
eij.2025.100788
15. Aggarwal, A. (2021). Implementation of machine learning in
cloud SIEM solutions for enhancing threat detection accuracy
and reducing alert fatigue through correlation-based anomaly
detection. NeuroQuantology, 19(10). https://doi.org/10.48047/
nq.2021.19.10.NQ21226
16. Bhaskara Raju Rallabandi. (2023). AGENTIC-AI xAPPS: O-RAN
MULTI-AGENT CONTROL FOR ENTERPRISE POLICY, SCHEDULING
& SPECTRUM. International Journal of Data Science and IoT
Management System, 2(4), 54–58. https://doi.org/10.64751/
ijdim.2023.v2.n4.pp54-58
17. Devulapalli, N. (2020). The evolution of database security from
centralized architectures to distributed cloud-native systems: A
comparative and historical perspective. Turkish Online Journal of
Qualitative Inquiry, 11(4), 2954–2963. https://doi.org/10.69980/
j1064z97
18. Bhaskara Raju Rallabandi. (2019). Enhancing 5G NSA Performance
with Lightweight Network Data Analytics Loops. International
Journal of Engineering Research and Science & Technology, 15(4),
57–61. https://doi.org/10.62643/ijerst.2019.v15.n4.pp57-61
19. Bhanushali, B. (2022). Blockchain-driven carbon credit trading
systems for improving environmental transparency, reducing
double counting, and ensuring accountability in climate finance.
International Journal on Recent and Innovation Trends in
Computing and Communication, 10(11), 292–300. https://www.
ijritcc.org/index.php/ijritcc/article/view/11927
20. Bhaskara Raju Rallabandi. (2024). 6G Cross-Layer Intent Engine:
Automated Translation from Business Requirements to Network
Policies and Real-Time Scheduling Controls. International Journal
Journal of Integrated Science, Technology and Management, 2026, 2(4), 146-151 149
Mirzayev Bobur Soyibjonovich
of Data Science and IoT Management System, 3(1), 44–48. https://
doi.org/10.64751/ijdim.2024.v3.n1.pp44-48
21. Ahuja, R. (2023). The role of artificial intelligence and machine
learning in DevSecOps: Leveraging predictive analytics, automated
threat detection, and anomaly identification for secure software
delivery pipelines. International Journal on Recent and Innovation
Trends in Computing and Communication, 11(2), 381–389. https://
www.ijritcc.org/index.php/ijritcc/article/view/11946
22. Rangappa, A. S. (2020). Deployment of cross-platform code
scanning tools in polyglot DevSecOps projects for uniform security
testing and language-agnostic vulnerability coverage. Journal of
Critical Reviews, 7(11), 5000–5006. https://doi.org/10.69980/jcr.
v7:i11.13402
23. Bhanushali, B. (2024). Tokenization of real-world assets through
block chain for improving liquidity, enabling fractional ownership,
and reducing transaction costs in global financial markets.
International Journal of Advanced Research in Education and
Technolog y, 11(4), 1809–1816. ht tps://doi.org/10.15680/
IJARETY.2024.1104071
24. Gupta, A. (2021). Deployment of advanced endpoint detection in
remote work environments for preventing malware intrusions
and data exfiltration through behavioral analytics and AI-driven
responses. NeuroQuantology, 19(9), 1206–1224. https://doi.
org/10.48047/nq.2021.19.9.NQ21199
25. Bhaskara Raju Rallabandi. (2019). Comprehensive Design and
Field Performance Assessment of Early 5G NSA Private Networks
for Industrial IoT Campuses. International Journal of Engineering
Research and Science & Technology, 15(1), 22–26. https://doi.
org/10.62643/ijerst.2019.v15.n1.pp22-26
26. Chatrath, A. (2021). Implementation of AI in recruitment platforms
for improving candidate screening and diversity inclusion through
bias-free resume parsing and skill matching. International Journal of
Innovative Research in Computer and Communication Engineering,
9(12). https://doi.org/10.15680/IJIRCCE.2021.0912046
27. Anchala, S. (2023). Integration of confidential computing in multi-
cloud environments for enhancing data privacy and regulatory
compliance through encrypted processing and isolated execution.
International Journal on Recent and Innovation Trends in
Computing and Communication, 11(9), 5698–5709. https://www.
ijritcc.org/index.php/ijritcc/article/view/11933
28. Chatrath, A. (2021). Integration of AI-powered forensic analysis
in cloud breach investigations for accelerating root cause
identification and evidence collection through log correlation
techniques. International Journal of Advanced Research in
Electrical, Electronics and Instrumentation Engineering, 10(8).
https://doi.org/10.15662/IJAREEIE.2021.1008051
29. Rangappa, A. S. (2020). Integration of behavioral analytics in
developer workflows for detecting insider threats and malicious
activities through activity monitoring and contextual profiling.
International Journal of Innovative Research in Science, Engineering
and Technology, 9(1), 13375–13383. https://doi.org/10.15680/
IJIRSET.2020.0901064
30. Khan, S. (2019). Sales force security compliance: An in-depth study
of GDPR, HIPAA, and PCI-DSS enforcement in cloud-based CRM
systems and their implications for global enterprises. International
Journal of Advanced Research in Electrical, Electronics and
Instrumentation Engineering, 8(9), 2211–2219. https://doi.
org/10.15662/IJAREEIE.2019.0809010
31. Jain, Arpit, Adesh Kumar, and Sanjeev Sharma. “Comparative design
and analysis of mesh, torus and ring NoC.” Procedia Computer
Science 48 (2015): 330–337.
32. Rangappa, A. S. (2021). Database security in cloud environments:
Exploring multi-tenant risks, data isolation challenges, and
compliance mechanisms in hybrid and public clouds. International
Journal on Recent and Innovation Trends in Computing and
Communication, 9(2), 50–57. https://www.ijritcc.org/index.php/
ijritcc/article/view/11921
33. Jain, A. (2024, December). Energy-Efficient Signal Processing
Architectures for IoT Networks Design. In 2024 International
Conference on Emerging Technologies and Innovation for
Sustainability (EmergIN) (pp. 362–367). IEEE.
34. Talasila, D. (2023). Enhancing cloud security with intrusion
detection and prevention systems (IDPS): Comparative evaluation
of signature-based, anomaly-based, and AI-powered detection
models. International Journal on Recent and Innovation Trends
in Computing and Communication, 11(3), 828–836. https://www.
ijritcc.org/index.php/ijritcc/article/view/11945
35. Kumar, Ashok, and Arpit Jain. “Image smog restoration using
oblique gradient profile prior and energy minimization.” Frontiers
of Computer Science 15, no. 6 (2021): 156706.
36. Chatrath, A. (2024). The role of encryption and key management
in cloud securit y: Balancing performance, scalabilit y, and
confidentiality in multi-tenant and distributed data storage
systems. International Journal of Innovative Research in Science,
Engineering and Technology, 13(7). https://doi.org/10.15680/
IJIRSET.2024.1307187
37. Jain, J., & Jain, A. (2022). Securing e‐Healthcare Images Using an
Efficient Image Encryption Model. Scientific Programming, 2022(1),
6438331.
38. Dwivedi, D. (2022). Application of AI-powered vulnerability
scanners in legacy applications for identifying unpatched security
flaws and weak dependencies through semantic code analysis.
International Journal on Recent and Innovation Trends in
Computing and Communication, 10(12), 617–623. https://www.
ijritcc.org/index.php/ijritcc/article/view/11918
39. Mahadasu, S. S. (2024). Open RAN-based on-demand 6G agricultural
networks: Adaptive edge infrastructure for autonomous farming
systems. International Journal of Data Science and IoT Management
System, 3(4), 96–102. https://doi.org/10.64751/ijdim.2024.
v3.n4.1289
40. Dwivedi, D. (2020). Adoption of secure software updates in
application lifecycle for preventing supply chain tampering and
maintaining trust through cryptographically signed patches.
Journal of Critical Reviews, 7(10), 6974–6980. https://doi.
org/10.69980/jcr.v7:i10.13406
41. Jain, Arpit, Nageswara Rao Moparthi, A. Swathi, Yogesh Kumar
Sharma, Nitin Mittal, Ahmed Alhussen, Zamil S. Alzamil, and
MohdAnul Haq. “Deep Learning-Based Mask Identification System
Using ResNet Transfer Learning Architecture.” Computer Systems
Science & Engineering 48, no. 2 (2024): 341.
42. Anchala, S. (2020). Cloud security challenges in supply chain
systems: Addressing vendor risks, data integrity, and third-party
service provider vulnerabilities. Journal of Critical Reviews, 7(2).
https://doi.org/10.69980/jcr.v7:i2.13401
43. Khan, S. (2022). Sales force security in mobile and remote
work environments: Addressing authentication challenges,
secure synchronization, and device-level threats. International
Journal of Innovative Research in Computer and Communication
Engineering, 10(12), 8735–8743. https://doi.org/10.15680/
IJIRCCE.2022.1012040
44. Mahadasu, S . S . (2024). Real-t ime net work dig it al t w in
synchronization for predictive capacity planning in multi-
vendor private 5G systems. International Journal of Engineering
Research and Science & Technology, 20(4), 483–488. https://doi.
org/10.62643/ijerst.2024.v20.n4.4405
45. Talasila, D. (2020). Deployment of dynamic data masking in
cloud-based applications for protecting sensitive information
and enhancing privacy controls through contextual access
filtering. Journal of Critical Reviews, 7(9), 4056–4062. https://doi.
org/10.69980/jcr.v7:i9.13403
46. Kumar, S., Sharma, K., Kumar, P. A., Jain, A., Bhagat, S. K., & Singh,
P. (2024, September). An improved particle swarm approach for
energy-aware location-aided routing in mobile ad-hoc network. In
2024 7th International Conference on Contemporary Computing
and Informatics (IC3I) (Vol. 7, pp. 1119–1124). IEEE.
47. Gupta, A. (2021). Implementation of cyber threat hunting in
hybrid cloud environments for early breach detection and
threat containment through behavior-based analysis tools.
150
Journal of Integrated Science, Technology and Management, 2026, 2(4), 146-151
FinTech Innovation and Competitive Advantage: Examining the Role of Data Analytics Capabilities
NeuroQuantology, 19(12), 1312–1329. https://doi.org/10.48047/
nq.2021.19.12.NQ21342
48. Aggarwal, A. (2022). Cloud security in artificial intelligence
workloads: Protecting model training, data confidentiality, and
AI-generated insights in cloud-based machine learning pipelines.
NeuroQuantology, 20(3), 1367–1382. https://doi.org/10.48047/
nq.2022.20.3.NQ23004
49. Gupta, A. (2021). Application of cyber deception strategies
in milit ar y net work s for enhancing operational securit y
and misleading attackers through adaptive honeypots and
decoy systems. NeuroQuantology, 19(4), 317–333. https://doi.
org/10.48047/nq.2021.19.4.NQ21068
50. Ahuja, R. (2023). Implementation of AI-driven input validation in
user interfaces for preventing injection attacks and ensuring data
sanitation through adaptive filtering and pattern recognition.
International Journal on Recent and Innovation Trends in
Computing and Communication, 11(8), 884–891. https://www.
ijritcc.org/index.php/ijritcc/article/view/11936
51. Dwivedi, D. (2019). The use of homomorphic encryption in
cloud security: Enabling secure data processing, confidential
query execution, and privacy-preserving cloud applications.
International Journal of Innovative Research in Science, Engineering
and Technolog y, 8(4), 4690–4698. https://doi.org/10.15680/
IJIRSET.2019.0804136
52. Pandit, T., Mahendru, S., Raval, M., & Upadhyay, D. (2026). The
evolution of reranking models in information retrieval: From
heuristic methods to large language models. In X. Ding & Y.
Dong (Eds.), Computational linguistics and natural language
processing: CLNLP 2025 (Communications in Computer and
Information Science, Vol. 2775, pp. 56–67). Springer. https://doi.
org/10.1007/978-981-95-4788-3_6
53. Harshitha, G., Kumar, S., Rani, S., & Jain, A. (2021, November). Cotton
disease detection based on deep learning techniques. In 4th S
