Community Bank: C-Suite’s “Process First” Approach Reduces OpEx by Over $10M in 12 Months

Situation: A CEO’s multiple challenges—and opportunities

Like many of its peers today, this client—a respected community bank with 600+ employees, spanning multiple states—faced numerous headwinds:

• Outdated Processes: New core and LOS technologies were implemented over the last 5 years, but business processes and organizational structure remained untouched.
• Avoidable Rework: New accounts and loans were reworked up to ten times during onboarding, sapping productivity and customer experience.
• Over-Staffing: Despite declining lending volumes, staffing in sales, credit, and loan ops remained stubbornly consistent. The executive team needed better data to methodically reduce staffing without sacrificing talent.
• Strategic Management: The bank’s business intelligence (BI) team maintained over 100 management reports in Power BI, yet the C-Suite lacked insight into the vital few metrics that linked to their strategic goals.

The CEO heard from a peer bank about how The Lab had helped them transform their business. After an introduction and demo, he engaged The Lab for self-funding process-improvement implementation.

Phase I: Analysis and Design Approach

The two-phased, bank-wide effort started by mapping current-state business processes. This included all major work activities spanning the “end-to-end” lifecycle of both deposit and loan products, including:

• Retail Branch Operations
• New Account Opening
• Loan Origination
  • Consumer
  • Commercial
  • Mortgage
• Deposit Operations
• Loan Servicing
• Risk (BSA/AML)
• Finance & Accounting

The bank’s current-state operations were then benchmarked against The Lab’s proprietary industry-best-practice workflows. This highlighted the most valuable improvement opportunities. While the findings were not unexpected, the quantified fact-base documented during Phase I provided the executive team with a business case to launch Phase II implementation.

Major findings:

• Scalable capacity, bank-wide – 15% to 30%

• Incomplete loan and new account packages – 50% to 80%+

• Branch network over-staffing – 20%+

• Employee productivity variance for similar work – up to 5x

• Process improvement and automation opportunities – 120+

The Phase I findings and benefits were grouped into 10 modular work plans. These were further sequenced into two 6-month waves for implementation in Phase II.

Phase II: Implementation Work Plan Examples

1. Standardized “Super KPI” Performance Management

Management reports were woefully inconsistent—one-off designs cobbled together by different bank managers over several years. No bank-wide standards for KPIs and performance management existed. Consequently, the CEO & CFO led a charge to become more data driven.

Initial measurements uncovered that productivity varied by 2x to 5x between employees performing identical tasks. Among commercial relationship managers, variance for loan production and margin was as high as 10x.

To overcome this, the bank’s existing data sources and infrastructure were combined with The Lab’s standard data models to build a suite of “Super KPI” reports. These included:

• Standard KPI definitions (about 20 total, bank-wide)
• Bank-wide and individual-level performance targets for each KPI
• Automated, executive scorecard summarizing KPI performance
• Management “drilldowns” to use in day-to-day coaching
• Performance improvement plans for low-performing employees

2. Automation Program “Accelerator”

Despite purchasing the latest robotic process automation (RPA) technology, the bank had implemented only a single RPA “bot” in one year. The CIO needed The Lab’s help to overcome three common challenges:

• Use case identification: Informal surveys of business SMEs failed to yield feasible, high-value automation use cases.
• Pre-automation standardization: Many use cases required process and data standardization prior to automation.
• Development capacity: Two IT team members had completed RPA training but were constantly diverted by other tasks.

During Phase I discovery, the bank’s business teams identified more than 50 potential use cases from The Lab’s catalog of common bank automations. These were then prioritized for implementation based on annual hourly savings.

The Lab’s U.S.-based development team—working closely with bank process SMEs—had 14 bots up and running within 8 months, saving more than $1.5M every year.

3. Retail Branch Network Optimization

Half of the bank’s cost was tied up in the retail branches. But the excessive effort required to review the branch network—staffing, hours of operation, and other factors—meant that adjustments were made only every few years.

The Lab’s standard branch optimization models automated this review effort. These models also included demographic data—population growth, commercial activity, daily passing cars, and more—to inform network design.

The resulting interactive dashboards provided automated insights on each branch location, continuously serving up opportunities for optimization to the executive team. For example:

• Hours of operation: Certain branches had little-to-no traffic in the early morning and late evening.
• Excess staff: Nearly half of branches were over-staffed based on actual demand in the area.
• Consolidations, closures: The Net Present Value (NPV) of certain branches did not warrant continued operation.

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