Agentic AI refers to AI systems that go beyond assistive tools to autonomously execute multi-step workflows, adapt to context, and make decisions with minimal human intervention. In financial services, this includes autonomous agents for fraud detection, compliance monitoring, KYC automation, and portfolio management — systems that can set goals, use tools, and complete tasks end-to-end rather than simply responding to prompts.
Agentic AI in Financial Services: Turning Vision into Value
Agentic AI in financial services is moving from concept to competitive priority. BFSI institutions have significantly increased their spending on innovation — yet ROI still lags. The next wave of competitive advantage will not come from experimentation. It will come from execution at scale. And at the heart of this shift is agentic AI: systems that go beyond assistive support to autonomously execute workflows, adapt to context, and deliver outcomes with minimal human intervention.
This white paper, produced by Tenity’s team in Singapore, sets out the state of play, the barriers institutions face, and a practical framework for moving from possibility to production.
Download the full PDF below, or read the key findings here.
By the Numbers
BFSI innovation spend has grown from $200K to $780K per $1B of assets between 2022 and 2024. Banks’ tech spend now exceeds 10% of revenue and is rising 9% yearly, outpacing inflation. Financial services AI spend reached $35B in 2023 and is projected to reach $97B by 2027.
Despite this, the execution gap remains wide. Innovation may be accelerating in theory, but most institutions struggle to convert capital into real outcomes.
From Generative AI to Agentic AI: The Next Frontier
Around 70% of institutions are now using GenAI to improve internal productivity, automate document handling, and support compliance and customer service. But while GenAI is fundamentally assistive, agentic AI is built for delegation.
Agentic AI introduces autonomous systems capable of setting and adapting goals, executing multi-step workflows in real time, interfacing with APIs, tools, and human stakeholders, and making decisions dynamically based on evolving inputs.
The digital transformation timeline:
- 2010–2018: Digital transformation and RPA rule-based automation
- 2019–2023: GenAI as productivity assistant, human-in-the-loop
- 2024 onwards: Agentic AI — autonomous multi-step workflow execution
Economic impact:
The agentic AI market in BFSI is projected at $196.6B by 2034, growing at a 43.8% CAGR. Projected ROI from agentic AI deployment averages 171%, with US firms reporting 192%. In 2024, 37% of VC targeted AI, with agentic systems seeing the largest deal growth in financial services.
The supporting technology stack is now mature across five layers: domain-specific applications, orchestration frameworks (Amazon Bedrock, Azure AI Foundry, Vertex AI), foundation models (Mistral, Gemini, Claude, OpenAI), cloud infrastructure (Google Cloud, Azure, AWS), and semiconductors (AMD, Groq, NVIDIA, Intel).
Early Use Cases: Agentic AI in the BFSI Value Chain
Adoption typically begins where workflows are high-volume, rules-based, and low-risk — echoing early RPA deployments.
Banking:
KYC automation, relationship manager co-pilots, compliance monitoring, cybersecurity
Payments:
Real-time fraud scoring, autonomous dispute handling, reconciliation and treasury
Insurance:
Claims triage agents, underwriting assistants, fraud and abuse prevention
Wealth management:
Portfolio summarization, client insights generation, financial advisory support
Examples in active deployment include fraud checks, claims triage and routing, investment memo generation, regulatory report drafting, customer service email triage, and data-matching and extraction. These are fast to implement, easy to validate, and generate quick wins that build executive buy-in. As confidence grows, institutions can move into higher-stakes domains such as credit decisioning or compliance — though these carry additional regulatory requirements under frameworks such as the EU AI Act.
Case study — Unique AI:
Rather than relying on generic copilots, Unique co-develops tailored agentic solutions embedded directly into client workflows. For institutional wealth management, these systems use agentic loops to dynamically retrieve, verify, and refine responses based on real-time inputs. Every output is fact-checked — if verification fails, agents loop back to re-source data or escalate for human review. Region-specific knowledge scopes ensure answers comply with local regulations.
Breaking the Pilot Trap: Barriers to Scale
Despite growing interest, many BFSI institutions struggle to scale agentic pilots into production. The core issues are not just technical — they are organizational, structural, and governance-related.
Organizational misalignment.
Only 23% of institutions report board-level oversight of AI strategy.
Fragmented operating models.
Most AI efforts stall at the pilot phase.
Governance and risk gaps.
Governance is consistently cited as the top barrier to AI scaling.
Without clear answers to these challenges, agentic pilots can quickly shift from strategic opportunity to unmanageable liabilities.
Tenity’s BFSI AI Execution Framework
To scale agentic AI effectively, BFSI institutions need a structured, outcome-driven approach. Tenity’s framework operates across three stages:
Stage 1: Exploration — Discovery and Exposure
Secure buy-in from senior leadership and cross-functional teams. Define what success looks like — faster loan processing, smarter fraud detection, enhanced underwriting — and establish measurable KPIs. Conduct a market scan of existing AI capabilities, competitor moves, regulatory considerations, and potential partners. Build a hypothesis-driven roadmap focused on areas with the highest potential ROI.
Stage 2: Application — Strategy-Aligned Collaboration
Run tailored innovation sprints: short, focused projects that bring together cross-functional stakeholders to rapidly explore, prototype, and validate AI-driven concepts. Use strategic programs to connect BFSI institutions with execution-ready startups and conduct emerging tech readiness assessments across data infrastructure, integration pathways, security posture, and governance mechanisms.
Stage 3: Execution — Scale Through Readiness
Confirm teams have the skills, tools, and processes to support AI systems at scale — including monitoring, incident response, and ongoing model tuning. Integrate regulatory requirements and ethical guidelines into deployment processes. Design AI solutions to fit seamlessly into existing IT architectures without disrupting customer experience. Establish governance frameworks for ongoing performance review, risk assessment, and adaptation to evolving market and regulatory conditions.
How Tenity Helps BFSI Institutions
Tenity operates across all three stages of the framework, bringing a combination of ecosystem access, corporate innovation expertise, and startup connectivity.
Discovery and exposure:
Executive sessions on GenAI and agentic systems for senior leadership, global startup scouting and trend analysis, and hands-on experimentation through hackathons and tech showcases.
Strategy-aligned collaboration:
Tailored innovation sprints, structured programs connecting BFSI institutions with vetted AI startups, and readiness assessments.
Execution enablement:
Embedded innovation teams working alongside core business units, implementation and go-to-market support, and venture collaboration pathways including co-investment opportunities and integration frameworks.
Partners in delivery:
Tenity has supported corporate innovation programs with institutions including Allianz Türkiye (HackZone), Visa (Innovation Program Europe), UBS, and Julius Baer, across fintech, insurtech, and digital health.
The Path Forward
Agentic AI is not a future concept — it is already reshaping BFSI. The institutions that will lead are not just those experimenting, but those executing at scale with intent, trust, and control.
The winning institutions will identify regulation-ready, high-impact use cases; partner with execution-ready innovators; run risk-aligned, outcome-focused pilots; and scale with internal alignment and strong governance.
The question is no longer whether institutions will adopt agentic AI — but how successfully they will deploy it.
To explore how Tenity can support your institution’s agentic AI journey, contact our innovation team.
Agentic AI in Financial Services: Frequently Asked Questions
What is agentic AI in financial services?
How is agentic AI different from generative AI in banking?
Generative AI is fundamentally assistive — it responds to prompts and supports human decision-making. Agentic AI is built for delegation — it can initiate actions, interact with external systems and APIs, and complete complex workflows autonomously. In banking, this means moving from a co-pilot that drafts a compliance report to an agent that monitors transactions, flags anomalies, compiles the report, and routes it for approval without human initiation.
What are the biggest barriers to scaling agentic AI in BFSI?
The three most common barriers are organizational misalignment — only 23% of institutions report board-level AI oversight — fragmented operating models that stall pilots before they reach production, and governance gaps around risk, accountability, and regulatory compliance. Technical readiness is rarely the limiting factor; the bottleneck is structural and cultural.
Is agentic AI regulated in financial services?
Agentic AI in financial services sits within existing and emerging regulatory frameworks including the EU AI Act, which classifies certain financial AI applications as high-risk and requires human oversight, transparency, and auditability. FINMA in Switzerland and MAS in Singapore have also issued guidance on the responsible use of AI in regulated contexts. Institutions deploying agentic systems in credit decisioning, compliance, or customer-facing roles must ensure their governance frameworks meet these requirements.
How can financial institutions get started with agentic AI?
The most effective starting point is identifying high-volume, rules-based workflows with clear success metrics — such as KYC checks, claims triage, or regulatory reporting — where the risk of autonomous action is manageable and ROI is measurable. From there, institutions should run structured innovation sprints to prototype and validate use cases, assess their data and integration readiness, and build governance frameworks before scaling. Partnering with an ecosystem operator like Tenity can accelerate this process by connecting institutions directly with execution-ready startups building agentic solutions for financial services.