EASE 9.0 · Excellence · Cost Optimization & Income GrowthSavings a Market Competes Away
Quantifying financial impact: cost reduction, headcount optimization, and ROI.
Without treating cost-cutting and growth as a trade-off.
The financial case for agent-based decisioning is not a story about incremental efficiency gains. When a bank moves a routine decision from a multi-desk manual process to an automated agent pass, the fixed cost of human review disappears from the routine case entirely.
Where the Savings Actually Come From
A manual credit approval carries the cost of every desk it passes through: documentation review, credit assessment, compliance check, committee sign-off, each with its own staff time allocated whether the application is straightforward or not.
The Path to Progress
The cost per decision does not drop by ten or twenty percent. It typically drops by an order of magnitude — but only when the process is redesigned, not just automated.
The PSU Number That Backs It, and What the Wider Research Shows
The clearest Indian PSU data point available is from collections, not credit approval. Central Bank of India's General Manager for Credit Monitoring & Policy reported, at the Bharat Collections and Lending Summit, that the bank's Special Mention Account (SMA) ratio, its measure of accounts showing early stress before they turn non-performing, fell from 8% in 2024 to 3% in February 2025 after adopting an AI-driven collections platform, alongside a 25–30% cut in bounce rates. That is an actual PSU number this category can stand behind, distinct from the illustrative table above.
The wider research points the same direction. McKinsey's banking research puts industry-wide cost reduction from AI at up to 20% at full adoption, and cautions explicitly that most of that saving does not stay in a bank's own P&L, because competition erodes it back into pricing over time, the same pattern digital banking's early cost gains eventually followed. BCG's more granular, process-level data is where the order-of-magnitude claim actually holds up: 25–35% cost reduction per origination in onboarding, 30–40% in back-office payments and reconciliation, and up to 50% in KYC and compliance workflows specifically. But BCG is explicit that the higher end — 60% or more — is reached only when a bank redesigns the process end-to-end around the agent, not when it bolts automation onto the existing workflow. That distinction, not a single percentage, is the accurate answer to "how much will this save": a bank that automates its current process gets the low end of these ranges; a bank that redesigns the process the way Business Process Re-engineering describes gets the high end, and Central Bank of India's result sits at that high end.
Sources: Central Bank of India GM remarks at the Bharat Collections and Lending Summit, as reported by PTI/The Wire/Business Standard (2025); McKinsey Global Banking Annual Review (2025); BCG banking-automation research (2025–26). Figures as publicly reported, not independently audited.
The Business Case
The mechanism behind the turnaround-time reduction described on the Credit Risk & Resilience page is described in full there; the number that matters specifically for cost is different from the speed number. A licensed decisioning platform is close to a fixed cost: the same policy engine that processes 500 applications a month processes 5,000 at a marginal cost per additional application that approaches zero, because the cost driver in a manual process — a human reviewer's time — simply is not present in the automated path. That is why cost and growth stop being a trade-off here: the infrastructure investment is sunk once, and every unit of volume above the break-even point is closer to pure margin than the equivalent unit was under manual review.
Underlying mechanism source: Appice client engagement data, EASE 9.0 & AI client briefing, 2026; see Credit Risk & Resilience for the reported turnaround-time figures. Indicative of results reported across these campaign-management engagements, not independently audited; bank name withheld at client request.
Where AI Agents Fit
The deployment figures above come from Appice's campaign-management engagements, not from a bank running the full agent stack described below; this section describes where the category goes next. The Credit Decisioning Agent, the same one described on the Credit Risk & Resilience page and part of the shared agent vocabulary used throughout this guide rather than a specific bank's current deployment, would shift the lens here from speed to unit economics: every application it processes without escalating to a human is an application that did not consume a credit officer's fixed daily capacity. An Optimize Agent would then do the specific cost-optimization work: continuously identifying which decision types have low escalation rates and are safe to push further into full automation, versus which ones still need a human in the loop, so the automation boundary itself keeps shifting toward more coverage rather than being fixed at go-live.
The Skeptic's Answer
McKinsey's own numbers, cited above, hand a skeptic the sharpest available argument. If most AI-driven cost savings get competed away into pricing instead of staying on a bank's own P&L, why build the investment case on cost savings at all? Isn't that a self-defeating rationale?
It is self-defeating only if a bank automates the current process and stops there, which is exactly the 20–25% low end BCG describes. The 60%-plus outcome comes from redesigning the process end-to-end, and that redesign produces something competition cannot price away as easily as a cost line: the Central Bank of India's SMA ratio falling from 8% to 3% is a risk-quality improvement, not a cost saving a competitor's pricing move erodes. A bank that only chases the cost number is racing pricing it cannot win long-term. A bank that redesigns the process gets faster, safer decisioning as a durable capability: the cost reduction is real, but it is not the part that survives competition; the redesigned infrastructure is.
The Investment Is Sunk Once
McKinsey's own caution, that most AI-driven savings get competed away into pricing, describes what happens outside a bank's control. What happens inside the P&L is simpler and stays put: the policy engine built to fix decisioning speed keeps running, still owned, generating a cost curve that flattens toward zero with every added application. A second budget line labelled “cost optimization” will not buy new capability. It buys a duplicate invoice for infrastructure the bank already paid for once.