- From Workflows To Agents: Rethinking BPO In Finance
- The Pragmatic Role Of Agentic AI In BPO
- What Strategic Transition to Agentic AI Entails
- 1. Get people and leadership in place.
- 2. Strengthen governance and risk management.
- 3. Build flexibly.
- 4. Pilot and scale thoughtfully.
- 5. Manage vendor dependencies.
- Conclusion
Gregg Mojica is Co-Founder & CEO at Alloy Automation.
Despite the billions of dollars at stake, portions of the financial sector have been slow to embrace AI.
Reporting from last year locates banks’ hesitation (subscription required) in fears of job losses, regulatory uncertainty and the challenges of digitizing analogue institutions; just 6% of retail banks are prepared to deploy AI at scale.
Yet this is the same industry that already wrestles with outsourcing’s costly trade-offs. Although business process outsourcing (BPO) delivers on scale and savings, for financial firms it entails operational risk, fragmented oversight and lengthy compliance chains.
Even routine tasks like processing address changes can carry audit risks offshore, where lower labor costs are offset by slower resolutions and fragmented workflows.
Necessary evils so far, but agentic AI offers a potential solution to these challenges.
Unlike traditional automation or BPO, agentic AI orchestrates people, tools and workflows to reduce cost and risk. Where banks dither, fintechs are already proving what’s possible: Automated money-laundering checks save money, AI agents can resolve countless queries in seconds and some customer service roles may become almost entirely automated.
Agentic AI is upending the calculus of outsourcing routine operational work, even as the global BPO market is projected to grow toward $525.2 billion by 2030.
In this article, I’ll take you through the changing role of BPO, the potential of agentic AI and how firms can begin the transition.
From Workflows To Agents: Rethinking BPO In Finance
Until now, the finance industry has treated BPO like a factory or supply chain, with predictable inputs, repetitive tasks and manual triage when things break. Modern workflow automation could handle this, but advances in AI now offer a stronger alternative.
Agentic AI can reason across incomplete information, adapt when a process breaks and decide when to escalate to humans. These agents coordinate across communication channels (voice, chat, email), core systems (ledgers, CRMs) and human reviewers—planning and recovering when workflows go off track.
If you pair workflow automation with AI’s reasoning capabilities, you no longer need BPO. But far from making people disposable, agents essentialize them: centering human judgment as the anchor of compliance and trust.
The Pragmatic Role Of Agentic AI In BPO
By “agentic AI,” I mean LLMs paired with memory that can intelligently call APIs and model context protocols, operate within guardrails and follow rule sets to surface decisions—and, crucially, defer to humans when needed.
Agents execute repeatable checks, assemble evidence, and either act automatically or escalate with full context—all without breaks, keeping operations continuous. An agent’s responsibilities in finance include: authenticating identity evidence, fetching transaction records securely, applying policy checks, drafting disclosures and escalating exceptions like mismatched charges.
Every step is logged and auditable, with those exceptions routed to a human reviewer (via Slack, email or a dashboard) with the full context. This hybrid flow (agents for routine work, humans for judgment) keeps institutions within the bounds of compliance while improving throughput and consistency.
Resilience, compliance and customer trust become the first differentiators with agents, compounding into meaningful cost savings as errors, exceptions and outsourcing dependencies are reduced.
Measured outcomes include lower average handle times (by 15% to 18% for large-scale BPOs, according to BCG), higher full-time equivalents (resulting in over $3 billion in revenue), more consistent compliance handling and improved satisfaction scores for customers who interact in their preferred channel.
With governance and observability built in, automation offers lower operational risk, better customer retention and cost savings at scale.
What Strategic Transition to Agentic AI Entails
The financial service industry is increasingly exploring agentic, with early adopters already showing meaningful gains. Achieving success, however, requires both careful planning and concrete actions.
1. Get people and leadership in place.
A recent EY survey shows that about 84% of employees are eager to adopt agentic AI but feel overwhelmed by constant change, anxious about skill gaps and constrained by unclear direction.
Without leadership support, pilots stall or fail to scale. Executive buy-in ensures alignment across operations, compliance, IT and risk—turning pilots into scalable, well-governed systems.
Invest in upskilling staff as supervisors, exception handlers and auditors, creating hybrid human-agent roles. Define KPIs early, and communicate the strategic vision across the organization.
2. Strengthen governance and risk management.
Agents will reshape BPO in finance only if they are deployed as governed orchestration layers that: 1. make decisions auditable, reversible and aligned with compliance standards; 2. let humans remain audit anchors for edge cases; and 3. deliver measurable pilots that show reduced risk as well as meaningful cost savings.
Poor data quality or weak oversight can create compliance gaps and regulatory exposure. Agents must operate on verified, well-classified, standardized data with full lineage. Implement governance frameworks that define decision rights, escalation paths and human-in-the-loop checkpoints from day one.
3. Build flexibly.
Financial institutions are still running on legacy platforms that weren’t designed for agents and can block workflow coordination. By adopting modular architectures, open APIs and orchestration layers, firms can introduce agentic capabilities without expensive system overhauls.
4. Pilot and scale thoughtfully.
Early wins can come from embedding agents alongside existing teams to automate high-volume, low-risk processes like invoice validation or loan intake. That’s why you should run narrow, high-value pilots to validate agentic AI performance.
To do this, design deterministic workflows for five to ten processes where reliable data already flows cleanly between systems. These make ideal pilot environments for interoperable agentic overlays. This layered approach phases out the transition from outsourced BPO to agent-driven operations.
Scaling too quickly without measurable outcomes risks pilot purgatory. Track performance metrics and gradually expand agent responsibilities in-house.
5. Manage vendor dependencies.
Financial firms might need to integrate solutions from multiple providers. This introduces challenges: interoperability gaps, inconsistent standards and a larger attack surface that can propagate errors across workflows.
When doing so, maintain firm control over data, compliance and operational risk. Use orchestration layers and standardized integration frameworks to coordinate agents across vendors and ensure seamless operations.
Conclusion
Careful implementation of AI agents, alongside human review and governance, can help firms modernize routine operations without compromising oversight or trust.
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