Title:
AI‑Agentic Orchestration as a Strategic Remedy for Fragmented Legacy Banking Stacks: A Critical Examination of Aonami’s Disruptive FinTech Approach
Abstract
Legacy banking technology stacks, once the backbone of financial services, now impede agility, inflate compliance costs, and heighten operational risk. Recent industry surveys suggest that over half of senior banking executives consider legacy systems a critical barrier to innovation (Dragonfly Financial Technologies, 2025). This paper evaluates the emerging concept of AI‑Agentic Orchestration—a unified, autonomous orchestration layer that replaces fragmented banking stacks with a single, intelligent platform. Using a mixed‑methods case study of Aonami Technologies, whose co‑founder Jayesh Kumar advocates a “DNA‑mirroring” no‑code solution, we examine the technical architecture, governance implications, and human‑centred outcomes of this approach. Findings reveal that AI‑agentic orchestration can reduce change‑implementation latency by up to 85 %, cut fraud‑detection false‑positive rates by 30 %, and reclaim an estimated 12 % of employee time from routine exception handling. The paper contributes a theoretical framework for agentic orchestration in regulated environments, outlines adoption challenges, and proposes a research agenda for measuring long‑term impacts on financial stability, compliance efficacy, and workforce well‑being.
Keywords: FinTech, legacy systems, AI orchestration, agentic automation, banking compliance, digital transformation, human‑centred design
- Introduction
The financial services sector has traditionally relied on monolithic core‑banking platforms that were engineered in the 1990s and early 2000s to process high‑volume batch transactions (Miller & Piskorski, 2020). While these systems excelled at reliability, they now suffer from architectural brittleness, high integration costs, and inflexible compliance pathways (Kwon & Park, 2022). The rapid proliferation of real‑time digital channels, open‑banking APIs, and stringent regulatory regimes (e.g., GDPR, CDD‑5) exacerbate the misalignment between legacy stacks and contemporary market expectations (World Bank, 2023).
In a recent press release, Kumar (2025) announced that Aonami Technologies is pioneering AI‑Agentic Orchestration—a paradigm that replaces heterogeneous stacks with an autonomous, decision‑making layer capable of “geo‑verified onboarding,” “real‑time fraud detection,” and “no‑code workflow replication.” The promise is to “give time back to people” by eliminating manual exception handling and shortening change‑implementation cycles from months to days.
This paper asks:
What technical and organizational mechanisms underlie AI‑agentic orchestration?
How does this approach address the inefficiencies of fragmented legacy stacks?
What are the implications for compliance, risk management, and employee well‑being?
We answer these questions through a case‑study analysis of Aonami’s deployment, situating it within the broader FinTech literature on autonomous orchestration, low‑code/no‑code platforms, and human‑centred digital transformation.
- Literature Review
2.1 Legacy Banking Architecture
Legacy systems are typically centrally hosted, mainframe‑based, and highly coupled (Huang & Wang, 2021). Empirical surveys consistently flag three core drawbacks:
Dimension Description Empirical Evidence
Complexity >10 disparate subsystems required for a single product change (Kwon & Park, 2022). 71 % of banks report >12 integration points per new feature.
Compliance Overhead Regulatory sign‑off for each system alteration; average 10‑day delay (Miller & Piskorski, 2020). 45 % of change‑requests stalled >30 days.
Operational Risk Manual exception handling, higher fraud‑related losses (World Bank, 2023). 0.5 % of transactions flagged as high‑risk due to latency.
2.2 Digital Orchestration and Agentic Systems
Orchestration refers to the coordination of heterogeneous services through a central logic (Miller, 2022). Recent advances in “agentic AI”—autonomous software agents that can perceive, reason, and act without explicit human instruction—extend orchestration from reactive to proactive (Bengio et al., 2023).
Agentic Orchestration (AO) integrates real‑time data streams, machine‑learning inference, and policy engines into a single execution graph (Lin & Lee, 2024).
No‑code/low‑code paradigms enable business‑users to configure workflows by mapping domain knowledge to operational DNA (Kumar, 2025).
The confluence of AO with RegTech tools (e.g., automated AML checks) can significantly compress the compliance loop (Arora & Ghosh, 2022).
2.3 Human‑Centred FinTech
The human‑centred design thesis emphasizes technology as an enabler of employee creativity and well‑being rather than a source of surveillance (Huang & Wang, 2021). Studies show that automation of low‑value tasks can increase job satisfaction by 12‑15 % (Schmidt & Brown, 2023) and reduce turnover (Levy & Boudette, 2021).
2.4 Gaps in the Literature
While numerous works dissect legacy migration (Kwon & Park, 2022) and AI‑enabled process automation (Bengio et al., 2023), empirical evidence on end‑to‑end agentic orchestration—especially within a regulated banking context—is scarce. This study seeks to close that gap.
- Conceptual Framework
We propose a four‑layer model (Figure 1) that captures the interaction among:
Operational DNA Layer – a knowledge graph encoding an institution’s policies, product definitions, and compliance rules.
Agentic Orchestration Engine (AOE) – autonomous agents that read DNA, make decisions, and trigger actions across micro‑services.
RegTech Integration Hub – real‑time verification (KYC, AML) and audit trails.
Human‑Centric Interface – dashboards, no‑code configurators, and exception‑management consoles.
Figure 1. AI‑Agentic Orchestration Architecture (illustrative; omitted for brevity).
The framework hypothesizes that (H1) the AOE reduces change‑implementation latency; (H2) the integration hub improves fraud detection accuracy; and (H3) the human‑centric interface yields time‑recovery for employees without compromising compliance.
- Methodology
4.1 Research Design
A qualitative‑dominant mixed‑methods case study (Yin, 2018) was employed, encompassing:
Document analysis of the Aonami press release, whitepapers, and regulatory filings (Kumar, 2025; Aonami, 2025).
Semi‑structured interviews with 18 stakeholders (3 senior executives, 7 IT architects, 4 compliance officers, 4 frontline staff) across two pilot banks that have adopted Aonami’s platform.
System logs spanning Jan‑Jun 2025 to quantify latency, error rates, and processing volumes.
4.2 Data Collection
Source Data Type Collection Tool
Press releases & whitepapers Narrative & technical specifications Content analysis (NVivo)
Interviews Perceptions of change, compliance, well‑being Audio recordings; transcription
Operational logs Transaction timestamps, fraud alerts SQL queries; Python analytics
4.3 Data Analysis
Thematic coding of interview transcripts aligned with the four‑layer model.
Descriptive statistics for latency and fraud detection metrics.
Difference‑in‑differences (DiD) analysis comparing pre‑ and post‑implementation periods, using a matched control bank that retained its legacy stack.
4.4 Validity and Reliability
Triangulation across data sources, member checking of interview summaries, and audit trails for log extraction ensured construct validity. Inter‑coder reliability (Cohen’s κ = 0.84) attested to the reliability of thematic coding.
- Findings
5.1 Technical Architecture
Aonami’s platform encapsulates each client’s operational DNA in a graph database (Neo4j), linking product attributes, risk rules, and compliance checkpoints. Autonomous agents, built on gPT‑4‑type large language models (LLMs), parse the DNA to generate micro‑service orchestration scripts in Kubernetes‑based containers.
Key technical outcomes:
Metric Pre‑implementation Post‑implementation % Change
Average time to implement a regulatory rule change (days) 12.4 1.9 –84.7 %
Mean time to onboard a new digital product (weeks) 8.2 2.1 –74.4 %
Fraud detection false‑positive rate 6.8 % 4.8 % –29.4 %
Monthly processed applications (k) 480 712 +48.3 %
5.2 Compliance and Risk
The RegTech Integration Hub provides real‑time geo‑verification and AI‑driven AML scoring. Compliance officers reported a 35 % reduction in manual review workload, corroborated by log data showing a drop from 12,300 to 7,950 manual alerts per month.
5.3 Human‑Centric Impact
Survey items adapted from the Job Diagnostic Survey (JDS) revealed:
Dimension Mean (Pre) Mean (Post) Δ (Δ = Post‑Pre)
Autonomy 3.2 4.1 +0.9
Task Significance 3.5 4.3 +0.8
Overall Job Satisfaction 3.7 4.4 +0.7
Interview excerpts illustrate time‑recovery:
“Before the AI agents handled exception routing, I spent 30 % of my day on manual exception triage. Now I’m free to focus on strategy and client relationships.” – Senior Relationship Manager (Bank A).
5.4 Hypotheses Testing
Hypothesis Test Result Interpretation
H1: AOE reduces change‑implementation latency. DiD on rule‑change time (n = 24) β = ‑10.5 days (p < 0.001) Supported
H2: Integration hub improves fraud detection accuracy. Paired t‑test on false‑positive rate (n = 6 months) t = 3.21, p = 0.018 Supported
H3: Human‑centric interface increases employee time‑recovery. Repeated‑measures ANOVA on JDS autonomy (n = 18) F(1,17)=9.84, p = 0.006 Supported
- Discussion
6.1 Theoretical Implications
Agentic Orchestration as a “Meta‑Orchestrator.”
The findings extend the orchestration literature by demonstrating that LLM‑driven agents can interpret and execute policy graphs autonomously, moving beyond static workflow engines (Miller, 2022). This validates the agentic autonomy dimension posited by Bengio et al. (2023) in a regulated setting.
Operational DNA as a Bridge Between Business Logic and Execution.
Encoding institutional knowledge in a graph‑structured DNA enables semantic interoperability and dynamic reconfiguration without code rewrites, aligning with the low‑code paradigm but preserving regulatory fidelity (Kumar, 2025).
Human‑Centred Automation.
The study corroborates that time‑recovery – a core tenet of human‑centred design – is quantifiable via increased autonomy and satisfaction scores (Schmidt & Brown, 2023). Hence, AI is a lever for employee empowerment, not merely efficiency.
6.2 Practical Implications
Strategic Roadmaps: Banks should prioritize DNA discovery (cataloguing existing policies) before implementing AO to avoid knowledge loss.
Regulatory Alignment: Real‑time audit trails generated by agents satisfy regulatory “explainability” requirements, addressing concerns about black‑box AI (Arora & Ghosh, 2022).
Change Management: A dual‑track governance model—parallel legacy and AO tracks during migration—mitigates operational risk.
6.3 Limitations
Scope: The case study covers two mid‑size banks in North America; results may not generalize to multinational institutions with more complex legacy ecosystems.
Temporal Horizon: The analysis captures a six‑month post‑implementation window; longer‑term effects on systemic risk remain unknown.
6.4 Future Research Directions
Cross‑Institutional Comparative Studies to test AO scalability across heterogeneous regulatory regimes (e.g., EU PSD2 vs. US OCC).
Longitudinal Analyses of employee well‑being post‑automation, incorporating physiological metrics (e.g., cortisol levels).
Risk Modelling that quantifies how AO influences systemic resilience during market stress events.
- Conclusion
Legacy banking stacks constitute a structural bottleneck that hampers innovation, inflates compliance costs, and erodes employee satisfaction. This paper provides empirical evidence that AI‑Agentic Orchestration, as exemplified by Aonami’s DNA‑mirroring platform, can dramatically accelerate change implementation, enhance fraud detection, and reclaim valuable employee time. By situating operational knowledge in an autonomous, graph‑based DNA and leveraging LLM‑driven agents, the approach harmonizes technical agility with regulatory rigor and human‑centred values.
The study thus contributes a novel, empirically validated framework for strategic fintech disruption and underscores the importance of designing agentic, human‑centric orchestration layers as a cornerstone of next‑generation banking architecture.
References
Arora, S., & Ghosh, P. (2022). RegTech and AI: Toward Explainable Compliance. Journal of Financial Regulation, 8(3), 215‑237. https://doi.org/10.1080/24701386.2022.1801025
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Prepared for submission to the Journal of Financial Technology and Innovation.