In financial services, operational speed is not a neutral improvement. A faster onboarding flow changes conversion rates, but it also changes exposure to compliance failure. A faster loan decision improves customer experience, but it also reshapes how credit risk accumulates across a portfolio. A faster fraud response reduces losses, but it also changes the institution’s liability profile in a world where regulators and customers expect near-real-time protection.
This is why business process management (BPM) is gaining traction well beyond the usual "efficiency tooling" narrative. In finance, BPM is increasingly used as a governance layer: a way to make work traceable, auditable, measurable, and reliably repeatable across teams and systems,without forcing the institution into brittle, one-off automation scripts.
The real constraint: complexity that turns into cost
Banking productivity has not been a simple story of "digitize and win". A large part of the sector’s cost pressure comes from complexity that keeps expanding: compliance requirements, risk management overhead, fragmented technology estates, and operating models with too many handoffs. McKinsey notes that banks’ operational costs have climbed over the past decade and a half, and frames process inefficiencies as a structural driver of rising "unit work" rather than a temporary problem that a single efficiency program can remove. [Read more]
One visible symptom is the economics of mortgage origination. McKinsey cites data showing the average cost to originate a mortgage rising from roughly $5,100 in 2012 to about $11,600 in 2023, with process complexity and compliance-related work being major contributors. This matters because unit cost is not an internal KPI; it is a competitive boundary that affects pricing, underwriting appetite, and ultimately the accessibility of credit.
In that environment, "automation" alone is not enough. Finance needs something closer to operational design: reducing unnecessary variability while keeping decisions explainable.
What BPM changes in practice
BPM platforms are typically described as tools to map, execute, monitor, and improve end-to-end processes. The key phrase is "end-to-end". Instead of optimizing isolated tasks, BPM treats the workflow as a lifecycle with states, transitions, responsibilities, rules, and measurable outcomes.
FinTech Weekly frames the core value proposition in a way that resonates with regulated industries: automation paired with accountability. The point is not merely "doing repetitive work faster", but doing it inside a controlled context, where approvals, validations, and compliance checkpoints are explicit rather than implied.
To make this distinction concrete, it helps to compare BPM to adjacent approaches that are often conflated.
| Approach | Strength | Typical pitfall in finance | Where it fits best |
|---|---|---|---|
| BPM (process orchestration) | Governance + traceability across the full lifecycle | Over-engineering if the underlying process is still ambiguous | Multi-step workflows with approvals, exceptions, and audit needs |
| RPA (task automation) | Fast automation of repetitive UI actions | Creates fragile "bots" when screens, rules, or handoffs change | Narrow, stable tasks that do not require complex decisioning |
| AI-assisted automation | Interprets messy inputs; proposes options | Non-reproducible outputs if used as the decision authority | Perimeter tasks: extraction, classification, triage support |
The economics of this table are simple: BPM reduces the cost of coordination and control, not only the cost of labor.
The central risk: automating chaos
There is a recurring failure pattern in transformation programs: a process is painful, leadership feels urgency, and automation is deployed as relief. If the workflow is unclear - ownership is informal, exceptions are handled through heroics, and data is scattered - automation does not create clarity. It magnifies the confusion, locks it into software, and makes future change more expensive.
Andrea Stanese describes the principle in direct terms: automation is a multiplier, not a starting point; if the structure is unclear, automation accelerates error propagation and produces rigid mistakes. [Read more]
In finance, this failure mode is especially costly because it tends to surface as governance breakdowns: unclear accountability, weak auditability, inconsistent decisions, and escalating exception handling. When regulators ask "how does this decision happen", a system that cannot explain its own logic becomes an operational liability.
Why determinism matters more in finance than in most industries
A useful line to draw is the one between inference and execution.
Modern AI is good at inference: extracting meaning from unstructured content, summarizing, proposing options, detecting patterns. But automation in business-critical workflows must be deterministic: given the same validated inputs and the same policy rules, the system should behave predictably and produce auditable outcomes.
Stanese makes this boundary explicit: AI can help interpret and propose, but automation must execute and enforce; when “agents” are plugged into unclear workflows as substitutes for process definition, outcomes become hard to reproduce and hard to audit. [Read more] This is not an anti-AI argument; it is a governance argument. Finance can use AI, but it must be placed where ambiguity is real and bounded, while keeping the core workflow rule-driven and accountable.
FinTech Weekly makes a similar point from the BPM angle: a mortgage workflow can automate document collection, but also track approvals, flag discrepancies, and ensure each step remains aligned with regulatory constraints. In other words, BPM is valuable because it encodes "how the business stays safe", not just "how the business goes faster".
Where BPM delivers the most leverage
Finance is full of processes, but not all processes are equal candidates. BPM tends to pay off where three conditions intersect: high volume, meaningful risk, and multi-step coordination.
Rather than listing dozens of examples, the table below shows a practical mapping: what the institution is trying to achieve, what must be controlled, and what should be measured.
| Workflow area | Primary economic goal | "Non-negotiable" controls | Metrics that matter |
|---|---|---|---|
| Customer onboarding (KYC/AML) | Reduce time-to-revenue without increasing compliance risk | Role-based access, evidence trails, escalation paths | Time to onboard, exception rate, rework loops |
| Loan origination | Shorten cycle time while preserving underwriting discipline | Validation gates, approvals, discrepancy handling | Turnaround time, “stuck” cases, approval latency |
| Fraud / disputes case handling | Reduce loss and protect customer trust | Traceable decisions, threshold-based escalations | Time to resolution, false-positive pressure, queue aging |
| Internal audit preparation | Lower cost of proving compliance | Logged steps, consistent artifacts, versioned rules | Audit readiness lead time, control test failure rate |
These are also areas where BPM creates data "as a byproduct". FinTech Weekly emphasizes that once processes are digitally executed and centrally governed, institutions can monitor turnaround times and bottlenecks in real time and reuse that data for performance management and compliance.
A finance-safe implementation sequence
The fastest way to waste money with BPM is to treat it as a software rollout. The finance-safe approach is to treat BPM as operating model instrumentation: you are making work explicit, then making it enforceable.
Stanese proposes a simple sequence - clarity, then structure, then automation - because it prevents the common trap of hardcoding ambiguity. BPM becomes powerful when it is introduced after the institution can answer basic questions consistently: what triggers the workflow, who owns each step, what “complete” means, and what happens when something goes wrong. [Read more]
To keep this practical and less “methodology-heavy,” the table below turns the sequence into an execution model that teams can use.
| Phase | What you produce | Why it matters in finance |
|---|---|---|
| Clarify reality | A shared, testable description of how work actually flows | Prevents automating different interpretations of the same process |
| Design structure | Explicit ownership, rules, and exception routes | Creates accountability that auditors and regulators can follow |
| Stabilize behavior | The process works reliably without heroics | Reduces hidden operational risk before scaling |
| Automate deliberately | Automation focused on repetitive effort + controlled gates | Keeps speed gains while preserving explainability |
This aligns with a broader productivity message coming from banking strategy research: lasting improvement tends to require simplification and operating-model redesign, not only incremental cost-cutting. McKinsey argues that banks often run repeated efficiency programs that treat symptoms, and calls for a more comprehensive approach that reduces unit cost of delivery while improving client and employee experience. [Read more]
The macro point: BPM lowers the cost of control
If you step back, BPM’s economic value in finance can be summarized in one sentence: it lowers the cost of control.
Control is expensive in finance because it is embedded in labor, meetings, handoffs, escalations, and audit preparation. When workflow states are explicit, when rules are versioned, when exceptions have defined routes, and when evidence is produced during normal operations, an institution spends less effort coordinating and proving what happened after the fact. FinTech Weekly highlights “compliance built in” as a practical advantage of BPM: governance and control structures embedded into day-to-day work, including role-based constraints and traceable histories. [Read more]
That matters in the real economy because lower control cost is not just an internal win. It affects the institution’s ability to serve customers with tighter margins, respond to shocks, and avoid pulling back from risk-taking simply because the operational overhead has become unbearable.
