Scaling AI in financial services requires strong governance, integration, and measurable value. In this edition, we explore practical steps for this transition.
We introduce RAISE (Reliability, Adoption, Integration, Security, and Efficiency), our framework for structured AI implementation, along with an accompanying AI Maturity Self-Assessment to help teams evaluate their readiness.
Additionally, we share delivery case studies on optimizing AI workflows and review key industry trends, including AI governance, fraud prevention, and stablecoin regulation.
Let’s dive in.
RAISE: Our Framework for AI in Production
Financial services leaders need AI workflows that are integrated, controlled, measurable, and secure before they scale.
Akvelon developed RAISE, which helps financial institutions evaluate how AI fits into existing processes, technology environments, and governance requirements before a broader rollout.
It structures AI implementation around five dimensions: reliability, adoption, integration, security, and efficiency.
Teams using RAISE gain clearer ownership, stronger controls, and a more consistent way to evaluate AI initiatives before scaling them. Reach out to give RAISE a try for your processes.
Take AI Maturity Self-Assessment
Is your organization ready to turn AI initiatives into repeatable business value?
Take the AI Maturity Assessment on Akvelon's homepage to identify gaps across strategy, governance, data, infrastructure, and engineering readiness.
This assessment is part of RAISE – Akvelon’s framework for governed AI adoption, which examines AI readiness from both business and technical perspectives.
You'll receive a readiness evaluation, practical insights, and an opportunity to discuss next steps with an Akvelon AI Architect.
Helping Financial Institutions Deploy AI With Control
Financial institutions have managed governance, risk, and oversight for decades. The core questions have always been practical ones:
Can decisions be explained? Who reviews the output? Where does accountability sit? How is compliance enforced? How is sensitive data handled? – None of these questions is new. AI simply adds a new layer that has to fit within those existing structures.
That's where Akvelon comes in. We help financial institutions and fintech companies integrate AI into existing workflows, controls, and operating models – from data preparation and integration to risk and compliance automation and ongoing monitoring.
The result is practical deployment of fraud detection, underwriting, KYC, and other high-value workflows, with less manual effort, faster cycle times, and stronger control.
Curious where AI can create measurable value in your case? Reach out to discuss your AI priorities: https://akvelon.com/contact-us/.
These project examples show how structured AI workflows can improve planning, analysis, and execution while keeping human review where it matters most.
Client Win: Faster Delivery in a Complex Legacy Codebase
Developers in large legacy codebases often lose a full day or two just reconstructing context before writing any code – a bottleneck that often remains outside the scope of AI coding tools.
In this case study, we share how Akvelon built a structured, human-controlled AI workflow within a large enterprise system, replacing ad hoc prompting with a repeatable process.
Results:
- Context reconstruction time dropped from 1–2 days to several hours.
- Implementation became more predictable, since planning now precedes coding rather than happening alongside it.
- AI output became more consistent across the team through shared workflows.
Client Win: Turning Fragmented RFP Inputs Into an Estimation-Ready Plan
Large RFPs typically require teams to spend weeks aligning on scope before development starts.
In this case study, we share how Akvelon built an AI-assisted workflow that turns fragmented inputs (RFPs, transcripts, and notes) into structured, estimation-ready delivery plans, surfacing ambiguity instead of hiding it.
In one engagement, the workflow turned roughly 30 fragmented feature requests into unified, review-ready estimation tasks within days.
For teams managing complex presales work, that translates into:
- Faster RFP response cycles (a 60% faster RFP response, in our client’s case),
- Requirement conflicts caught before they distort estimates,
- Delivery plans that are easier to audit post-facto.
Human review checkpoints remain at the specification, scope, and estimation stages.
Signals from Recent Industry Events
The Akvelon team attended Money20/20 Europe and Data & Digital Banking by American Banker this month. Both events reflected a common theme: financial institutions are focused on governance, fraud prevention, data foundations, and measurable AI outcomes.
From Digital Banking by American Banker
Topics worth attention:
- Banks are measuring AI through business outcomes, including productivity, operational efficiency, customer experience, revenue growth, and fraud prevention.
- Agentic AI is raising questions about execution and oversight. As autonomous systems start supporting financial workflows, banks need clearer validation, escalation, governance, and human-in-the-loop controls.
- Data quality, model visibility, and monitoring are becoming core parts of AI strategy. Fragmented data, vendor-embedded AI, model drift, and shadow AI make visibility and monitoring essential.
More details in the post on LinkedIn.
From Money20/20 Europe
Four signals stood out:
- Stablecoins and tokenized RWAs are past experimentation: institutions are evaluating them for cross-border treasury, settlement speed, and fraud reduction, with custody and compliance now part of the planning conversation.
- Agentic AI in financial operations is raising governance questions before efficiency ones – where human decision-making stays non-negotiable, and how that's enforced.
- Regulation is now a design constraint. MiCA, DORA, the AI Act, AML, and instant payment mandates are landing simultaneously, pulling compliance into architecture decisions from the start.
- Fraud defense is shifting to layered models – real-time data, multiple model types, and identity infrastructure combined, not one model carrying the load.
More details on LinkedIn.
Keep up with the latest news from fintech and finserv, carefully curated and analyzed by Akvelon's experts.
- Gartner: AI Value Depends on Execution – Gartner's latest research highlights data quality, ownership, and workflow integration as key factors for realizing AI value in financial services.
- McKinsey: Banks Are Defining Their Agentic AI Strategy – McKinsey outlines three paths for financial institutions: build customer-facing AI experiences, enable third-party ecosystems, or support AI-driven services behind the scenes. Whichever path is chosen, clean data, governed processes, and modern APIs remain foundational requirements.
We’d love to hear which operational AI, infrastructure, or engineering topics you’d like us to explore in future editions. Email us at info@akvelon.com!