Overview
Who I am, the value I bring to this mandate, and how I'd start. I'm a regional-bank finance and FP&A transformation leader — I help finance move from budget aggregation and reporting into a strategic decision engine.
How I position myself
I'm a regional-bank finance transformation leader who helps FP&A move from budget aggregation and reporting into a strategic decision engine — built for the run from $28B to $40B and beyond.
The mandate I'm built for
1 · Top-down decision engine
Convert bottom-up/reactive budgeting into top-down planning with firm budgets, decision rights, and effective challenge.
2 · Public-standard segment reporting
Consumer vs. commercial sub-segmentation (small biz, C&I, CRE) at a public-bank standard for board & leadership.
3 · Driver-based forecasting
Scenario modeling for capital allocation, strategic investment, M&A diligence, and the annual cycle.
4 · Tech/Ops finance & ROI
Project ROI tracking, investment prioritization, and "what can we afford" frameworks with the COO/CIO.
5 · M&A readiness
Diligence playbooks, target valuation, synergy & integration models as the bank scales toward $50–100B+.
Around those five: an efficiency-ratio / ROA-ROE cadence, board & owner reporting, capital-stress / ALCO support, a high-performance team build, and a "no jerks, no BS" culture.
What I bring
I build and lead high-performing teams
A player-coach who sets direction and still gets in the work. I've twice taken reporting teams "from peer laggard to peer leader," consolidated four scattered analytics groups into one enterprise function, and I develop analysts into true business partners.
A natural communicator and systems thinker
I translate complex financial analysis into clear, decision-ready guidance, and I zoom out to connect strategy, drivers, and outcomes into a vision people can act on. I partner across the C-suite and the lines of business — running reviews, building trust, and giving honest, effective challenge.
I turn strategy into a decision engine
Transformation is my track record — I lead Enterprise Analytics & Finance Transformation today. When it helps, I can build it myself: top-down, driver-based planning with governed AI and a project-ROI tool. The point is always better decisions, not the technology.
Real scale and M&A experience
~15 years across Regions, First Horizon ($40B+ segment), Synovus & Pinnacle — including capital and ALCO work (CCAR/DFAST), target valuation, and a live merger-of-equals integration now.
How I'd start — first 12 months
1 · Listen & map current process, metric integrity, decision rights
2 · Stand up cadence monthly segment reviews + efficiency/ROA-ROE pack
3 · Pilot top-down driver-based model on one segment + DFW/Frisco proving ground
4 · Make M&A repeatable diligence playbook before a deal is live
How I Fit the Role
How my experience maps to what this mandate demands. Left: the requirement. Middle: how I deliver it. Right: the evidence behind it.
| What the role demands | How I deliver it | Evidence |
|---|---|---|
| A · Top-down planning & decision rights | ||
| Bottom-up, reactive → top-down planning & decision-making engine | A top-down process starts with leadership's financial ambition, then forces clarity on the drivers — I've built the governance layer that makes that real. | Built a driver-based forecasting platform where every change is a reviewable proposal with attribution and second-order effects — governance bottom-up planning never had. Ran QBRs with the C-suite at Synovus. ↳ F1 · Top-down model |
| Set firm, top-down budgets; enforce decision-rights frameworks and effective challenge | Effective challenge only works when assumptions are transparent and business leaders feel heard — that's a process I build, not a posture. | Led expense-management identification and incentive-redesign efforts; trusted liaison to C-suite and LOB leads in a heavily matrixed org. ↳ F1 · Top-down model |
| B · Segment reporting at a public-bank standard | ||
| Design & implement segment reporting — consumer vs. commercial (small biz, C&I, CRE) | I've built the segment data spine — the hard part is consistent definitions and attribution, which is exactly what I govern. | Rebuilt Finance's enterprise data models spanning loans, deposits, fees, trust, securities, treasury mgmt & householding; supported Wholesale ($25B loans), CIB ($700MM), TPS ($84MM rev) as distinct segments. ↳ F4 · Segment reporting |
| Monthly segment reviews, quarterly strategic allocation checkpoints | I run a monthly segment review and a quarterly capital/allocation checkpoint anchored on efficiency and ROA/ROE. | Led quarterly business reviews with C-suite and LOB leadership; built banker/segment profitability reporting from company level down to the advisor. ↳ F4 · Segment reporting |
| C · Driver-based forecasting & scenarios | ||
| Establish driver-based forecasting & scenario modeling — capital allocation, M&A, annual cycle | My forecasts already encode how lines depend on each other, so a single lever surfaces what moves three lines downstream. | Forecasting platform links GL/ERP financials to driver data (loans, deposits, securities, profitability, employee tapes, external data); models second-order effects (deposits→interest expense & fee income). Earlier: consolidated NII, balance-sheet forecast, deposit-beta, CCAR/DFAST. ↳ F3 · Driver-based + AI |
| D · Finance support for Ops & Technology / Project ROI | ||
| Project ROI tracking, investment prioritization, "what can we afford" | "What can we afford" is a capacity question — I tie project demand to financial and people capacity, not just total spend. | Built a business-case→pro-forma tool: agentic extraction classifies recurring vs. non-recurring, marginal vs. sunk, capitalizable vs. expense, auto-spreads to templates with project ROI. Maps to a full capacity/prioritization & business-case governance model (sizing, RAC/EIC). ↳ F5 · Capacity & ROI |
| Partner with COO/CIO; financial linkage to enterprise project management | I'm finance's voice on the portfolio through clear governance — RAC (Resource Allocation Committee) for enterprise tradeoffs, EIC (Executive Investment Committee) for delegated and mandatory work — anchored in honest capacity transparency up front, then disciplined semi-annual post-mortems that track each project's realized ROI against its original business case. | Work from a RAC/EIC-style capacity & prioritization governance model (initiative sizing, investment pools, decision rights); built a corporate-projects tool that turns a business case into a pro forma with project ROI. ↳ F5 · Capacity & ROI |
| E · M&A readiness & due diligence | ||
| Diligence playbooks, target valuation, synergy analysis, post-merger integration | I led the finance diligence on a deal — revenue and workforce durability, normalized segment financials, and key-people retention — which is exactly FP&A's role in M&A. | Led the finance & strategic-finance diligence on a bank acquisition — customer/employee New-Lost-Existing durability, comp-vs-revenue, normalized segment financials with transaction-level income attribution, and key-people retention design. Plus M&A & capital modeling at Regions and a live merger-of-equals integration now. ↳ F6 · M&A diligence |
| F · EPM / BI / AI to drive FP&A | ||
| Enterprise performance management systems & BI tools; technology for speed and accuracy | I use technology to cut the mechanical work so the team spends its time on judgment — with explainability and audit kept intact. | Expert in Power BI / DAX / Power M; built the most-used Power BI dashboard at the bank ingesting 50M+ records; Databricks Genie agentic self-serve analytics; AI enablement across forecasting. ↳ F3 · Driver-based + AI |
| G · Leadership, player-coach, culture, talent | ||
| Build/retain a high-performance team; player-coach; upgrade talent; development pathways | I'm a player-coach — I set the operating model and still get in the model. I recently consolidated four scattered teams into one. | Built a team of 10; consolidated fragmented embedded LOB analytics into one enterprise function; long track record developing analysts from reporting into proactive profitability work. ↳ F7 · FP&A maturity |
| H · Board / owner / regulator reporting · capital · ALCO | ||
| Capital stress testing, ALCO support, balance-sheet analytics; board/owner/regulatory materials | I've lived the capital and ALCO side — NII forecasting, stress testing, IR materials — so I speak the CFO's and the board's language. | ALM Forecasting & Reporting (consolidated NII, balance-sheet forecast, risk simulation); CCAR/DFAST stress testing, capital planning; Investor Relations rotation (earnings, rating-agency & conference materials). |
| I · Talent & succession bench | ||
| Develop the team and build a credible succession bench | I build development pathways that turn reporting analysts into business partners, and grow the bench a scaling finance org needs. | Cross-functional finance breadth (FP&A, ALM, IR, strategic finance, analytics); a track record of developing analysts into business partners. ↳ F7 · FP&A maturity |
Each item reflects work I've led or built directly.
My Approach
How I think about the core parts of this mandate — in my own words.
1 My background ▸
My background is regional-bank finance, FP&A, and finance transformation. I started in core ALM, forecasting and capital work at Regions, then spent seven years at First Horizon building financial analytics for a $40B+ regional banking segment, taking reporting from peer laggard to peer leader. At Synovus I led strategic finance for Wholesale, CIB and Treasury Payment Solutions and ran QBRs with the C-suite. Today at Pinnacle I lead Enterprise Analytics & Finance Transformation post-merger — a team of 10. The thread is that finance has to move beyond explaining what happened to helping leadership see what's likely to happen and which levers matter.
2 Why Head of FP&A is my next step ▸
I view analytics and transformation as part of modern FP&A, not separate from it. The highest-value FP&A teams don't just consolidate budgets — they help executives see drivers, evaluate tradeoffs, and allocate resources. My career is that work: ALM and capital forecasting, segment profitability, strategic finance, and the analytics spine underneath it. This mandate takes that same skill set to a broader enterprise scope — bottom-up to top-down, tighter efficiency, stronger segment reporting. It's an extension of what I do, not a pivot.
3 What sets me apart ▸
A traditional FP&A background is valuable and I respect the discipline. But this mandate isn't about running the existing budget process — it's about transforming the function: top-down planning, public-standard segment reporting, project-ROI governance, more automation and AI. That's my differentiation — regional-bank finance plus transformation plus hands-on BI and AI. I operate in the details and design the operating model for a bank getting larger and more complex.
4 Why Arvest ▸
This is a true FP&A transformation mandate — improve profitability, tighten efficiency, move from bottom-up to top-down planning, and use automation to make finance more strategic. That's the work I do best and enjoy most, at a bank with real growth runway and an ownership structure that rewards long-term thinking. What draws me is the scope: broad enterprise reach and the authority to build the function.
5 Running the planning & forecasting process ▸
Beyond the model, the value is in running the process well. I start from the top-down targets, then make the operating logistics explicit: clear roles for who in FP&A owns each part of the forecast, the right granularity for each line and segment, and a published forecast calendar so everyone knows the cadence and deadlines. I build in structured review and buy-in with business-line leadership — the numbers land better when the people accountable for them have been heard and have signed off. And I close every cycle with a narrative: a clear story that explains the forecast results and the drivers behind them to executive management, so the conversation is about decisions, not reconciling versions.
1 · ROLES
Clear FP&A ownership
Who owns each part of the forecast.
2 · GRANULARITY
Right level of detail
By line, driver, and segment.
3 · CALENDAR
Published cadence
Deadlines the business plans around.
4 · BUY-IN
Review with the business
Leaders are heard and sign off.
5 · NARRATIVE
Explain results & drivers
A clear story for executive management.
6 Moving FP&A from bottom-up to top-down planning ▸
A bottom-up process becomes an aggregation of requests. Top-down starts with enterprise objectives — profitability, efficiency ratio, growth, capital capacity, investment appetite — then translates those into business-line targets, driver assumptions, and decision rights. Practically: define enterprise targets, build driver-based models, establish decision rights, create a challenge process, then run a monthly/quarterly cadence comparing performance to drivers, not just budget dollars. The hard part is credibility — leaders must feel heard while finance makes the tradeoffs visible. I've built the governance layer for exactly this.
Evidence My forecasting platform: the enterprise number is changed by many hands, with every change a reviewable proposal carrying attribution and second-order effects.
7 Improving profitability and the efficiency ratio ▸
I avoid treating the efficiency ratio as one blunt expense metric. I decompose it into the levers management can influence — revenue growth, NII trajectory, fee income, staffing, tech spend, branch/market productivity, ops capacity, support-function cost. Then a clean fact base: where are expenses outgrowing revenue, what's strategic vs. run-rate, where is cost carried without a growth or productivity link, where can automation reduce manual work. From there, a recurring profitability cadence with leadership — the goal isn't just cuts, it's resource allocation: fund growth, challenge low-return spend.
Evidence Led expense-management identification and product-profitability research; built banker/segment profitability down to the individual.
8 Using AI and automation without weakening control ▸
My rule is that AI improves speed, consistency, and analytical leverage without weakening control — in banking, explainability and auditability matter. So I separate the author from the calculator: the AI proposes a change in plain English, but a deterministic model does the math, and a human approves the merge. Start with the mechanical work — data pulls, variance-commentary drafts, reporting refreshes, first-pass anomaly detection — where automation cuts cycle time. Outputs still get review, tie-out, documentation, and ownership. I've built this.
Evidence Forecasting platform ("AI proposes, never does the math") plus a business-case→pro-forma agentic extraction with human-approved spreading.
9 Segment reporting at a public-bank standard ▸
It's consumer vs. commercial, with commercial broken into small business, C&I and CRE — each with its own revenue, direct expense, allocated overhead, credit cost, and capital usage, rolling to contribution and ROA/ROE. The discipline is public-company grade: consistent definitions, clean attribution, and the same numbers every month. I've built the segment data spine — loans, deposits, fees, trust, securities, treasury, householding — and supported Wholesale, CIB and TPS as distinct segments. The reporting is the easy part; governed definitions and attribution are the hard part, and that's what I do.
Explore F4 · Segment reporting
10 Driver-based forecasting ▸
Two layers. Early in my career I did the balance-sheet side — consolidated NII, balance-sheet forecasting, deposit-beta studies, MBS prepayment, CCAR/DFAST in ALM. More recently I built a forecasting platform that connects GL/ERP financials to the actual drivers — loans, deposits, securities, profitability system, employee tapes, external data — so the model encodes dependencies. Raise commercial deposits and it automatically surfaces interest-expense and fee-income effects. That's driver-based forecasting with the second-order math built in, plus scenario capability for growth, margin, credit, and capital.
11 Finance partnership for Operations & Technology ▸
"What can we afford" is really a capacity question — I weigh project demand against financial and people/execution capacity, not just total dollars. I size and categorize initiatives (strategic, mandatory, regulatory, carryover) and route them through a two-tier committee model with clear decision rights. RAC — the Resource Allocation Committee — sets the enterprise investment pools and owns the top strategic, large, and unfunded calls; it delegates mandatory, infrastructure, and corporate-function decisions to EIC — the Executive Investment Committee — which prioritizes that spend and oversees execution, continuing, limiting, or stopping projects. A Business Case Review vets each case before it reaches either body. On the ROI side I built a tool that takes a business case, extracts and classifies the economics — recurring vs. non-recurring, marginal vs. sunk, capitalizable vs. expense — and auto-spreads it into a pro forma with project ROI. That's the linkage between finance and enterprise project management the role calls for.
Evidence A two-tier capacity/prioritization & business-case governance model (sizing tiers, BCR, RAC & EIC) plus my business-case→pro-forma ROI tool.
12 My M&A experience ▸
There are two parts — the process and my hands-on work. I understand the full diligence process: working groups across finance, risk and credit, HR, tech-ops and legal; the VDR and request cadence; a standardized diligence memo; and the five decision lenses — strategic fit, financial return, revenue durability, workforce durability, and integration executability. On the acquisition where I led the finance and strategic-finance diligence, I owned the analytical core. I hardened the two questions that actually protect a deal — how durable is the revenue, and how durable is the workforce that holds it. On revenue: customer New/Lost/Existing analysis, concentration, and recurring-versus-one-time. On workforce: employee New/Lost/Existing, comp-versus-revenue, and incentive analysis by role. I then built the normalized segment financials — attributing transaction-level income to the right teams and stripping one-times to get to true run-rate earnings power for the valuation, and I fed key-people and incentive-structure recommendations — who the thesis depended on and the retention economics — to protect revenue post-close. That's exactly FP&A's role in M&A, and it's repeatable.
Evidence The full workstream I led is laid out in Framework F6 (Part B).
Explore F6 · M&A diligence
13 How I build and develop a team ▸
I build finance teams around clear roles and genuine development. Strong specialists own depth, process discipline, forecast mechanics, and day-to-day execution; I own the operating model, executive partnership, planning governance, and decision support — and I make the people around me more successful. I start by understanding each person's strengths, goals, and where they see the gaps, then set a structure with no ambiguity about ownership. The result is a complementary team with stronger executive alignment and a credible succession bench underneath it.
14 Partnering with and challenging executives ▸
Effective challenge works when the fact base is trusted and the tradeoffs are transparent — then the conversation is about choices, not credibility. I've spent my career as a liaison to the C-suite and LOB leaders in heavily matrixed orgs, running QBRs and profitability deep-dives. I challenge with data and options, not opinion, and I make sure leaders feel heard first. Intellectual honesty delivered respectfully is exactly the "no jerks, no BS" posture this culture values.
15 My current scope and team ▸
I lead Pinnacle's Enterprise Analytics & Finance Transformation team — 10 analysts and developers I built by consolidating fragmented embedded LOB analytics into one enterprise function. We own enterprise financial data models, banker KPI and profitability reporting from the company level to the advisor, AI enablement in forecasting, and the semantic layer the bank is standardizing on. Before this I led strategic finance for our largest commercial segments and ran QBRs with the C-suite. My span and executive exposure already mirror this role.
16 Where I'd take the FP&A function ▸
I think of it as a maturity curve with five stages — from a passive bookkeeper, to directional support, to an operational business partner, to a strategic driver, and finally to active value management, which is world-class. Most finance functions plateau in the middle — consistent reporting and some challenge. World-class is the top: finance integrates business, financial and investor strategy and actively manages the value drivers. The opportunity here is to climb from a bottom-up, reactive base toward strategic driver and active value management. The engine for that climb is concrete — fewer, higher-integrity metrics, automation that kills manual reconciliation, much faster time-to-insight, and real business partnership. I've run exactly that 'before-to-now' shift: I took a reporting team from peer laggard to peer leader and built the most-used dashboard at the bank, and at Pinnacle I consolidated fragmented teams into one governed source of truth. The real unlock is talent — job-applied learning and coaching embedded in the forecast and review rhythm, so capability compounds every cycle.
Evidence "Peer laggard to peer leader" plus the most-used Power BI dashboard (50M+ records); metric governance / single source of truth at Pinnacle; a Strategic-Finance value-commitment model — accurate reporting, end-to-end business-case support, driver-based forecasts, and long-range planning, rising to challenging the business as a trusted advisor.
Explore F7 · FP&A maturity
17 Improving profitability and positioning for scale ▸
I anchor it in a method I've run end to end — internally we called it 'Good to Great.' It started from a problem: total shareholder return was lagging the index and our peers. I helped run a regression on the drivers of valuation and confirmed investors were still pricing us on safety-and-soundness — liquidity, capital, perceived credit risk — and we traded at a discount to the return regression line. From there we benchmarked the operating model against peers on the metrics that move returns: efficiency ratio, ROAA, deposit cost and beta, and fee income to revenue. The winners clustered into two profiles — highly efficient commercial-oriented banks, and higher-efficiency-ratio but low-funding-cost banks — which pointed to one path: capital recycling, relationship deepening, and expense optimization. Then we went internal — NIR by component, and operating efficiency by business unit against the returns each unit generated, to separate the accretive units from the dilutive ones and see where we diverged from peers. The nuance that matters: efficiency ratio has to be read within business type — fee and wealth businesses structurally run far higher than commercial lending, so a blended number hides the story. Finally we translated the diagnosis into three opportunity buckets — balance-sheet optimization, business reimagination, and corporate-service optimization, each with its own targets, and a defined path to move ROAA and the efficiency ratio toward top quartile. That's exactly how I'd approach profitability and scale: diagnose where value leaks, benchmark by operating model, separate structural mix from controllable cost, and convert it into governed, quantified targets the businesses own.
The arc I led
1 · TRIGGER
TSR lagging
Returns trailing the index & proxy peers.
2 · DIAGNOSE
Regression on drivers
Valuation still set by safety & soundness; discount to the return regression line.
3 · BENCHMARK
Peer operating models
Efficiency, ROAA, deposit cost/beta, fee/revenue → bucket peers by model.
4 · CONCLUDE
Two winning profiles
Path = capital recycling + relationship deepening + expense optimization.
5 · GO INTERNAL
BU efficiency vs returns
NIR by component; accretive vs dilutive units; gaps vs peers.
6 · TRANSLATE
3 buckets → targets
Balance-sheet · business reimagination · corporate-service → top-quartile targets.
Explore F8 · Good to Great
Framework Diagrams
The frameworks I work from. When a question is open-ended, I answer with a structure — here are the ones I use, each with a short cue and a few points.
F1 · Bottom-up → Top-down operating model
How I approach it: moving a planning process from bottom-up aggregation to a top-down operating model.
Today · bottom-up
LOBs submit requests
→
Finance consolidates
→
Leadership reacts
↓ transform ↓
Target · top-down
Enterprise targetsprofitability · efficiency · growth · capital
→
Translate to LOBdriver assumptions
→
Decision rights+ effective challenge
→
Monthly / quarterly cadencevs. drivers, not just budget $
↻ variance feeds back into driver assumptions
- Start from ambition, not aggregation — enterprise objectives set the envelope before any LOB number.
- Translate targets into drivers — loan growth, deposit mix/cost, fee income, credit, staffing, tech, branch economics.
- Decision rights + challenge — make tradeoffs visible; leaders feel heard, finance holds the line.
- Compare to drivers — the cadence tracks driver performance, which is where the real story lives.
F2 · Efficiency-ratio decomposition
How I approach it: improving profitability by decomposing the efficiency ratio into the levers management can actually move — then benchmarking each against the peer pack.
Efficiency Ratio= Noninterest Expense / (NII + Fee Income)
↓
Revenue side
NII trajectorybalance growth · NIM · deposit cost
Fee incometreasury · mortgage · cards
Expense side
Peoplestaffing · comp · productivity
Technologystrategic vs. run-rate
Branch / marketproductivity · occupancy
Support functions+ automatable manual work
↓ benchmark every lever vs. the peer pack ↓
Peer benchmarkingpinpoint where each business diverges from the peer set — read within business type
Recurring profitability cadence: fund growth · challenge low-return spend
- Decompose, don't generalize — separate structural levers from temporary ones.
- Strategic vs. run-rate — protect investment that drives growth; challenge cost without a productivity link.
- Allocation, not cuts — the output is "fund / pause / automate / consolidate," not blunt expense reduction.
- Benchmark against the pack — I compare each lever to the peer set to find where a specific business diverges, reading the efficiency ratio within business type so mix doesn't mask the story.
F3 · Driver-based forecasting with governed AI ("forecast as a monorepo")
How I approach it: driver-based forecasting with governed AI — a system I built.
Plain-English ask"grow commercial deposits $120M H2"
→
AI authors a changeproposes — never computes
→
Deterministic modeldoes the math
→
Changeset / diff+ second-order: int exp +$1.4M · fee +$310k
→
Human reviewaccept · modify · reject
→
Versioned forecastattribution + commentary captured
↻ captured commentary becomes institutional memory the AI reads next cycle
- Author vs. calculator — the LLM proposes; the deterministic model computes; a human approves the merge. That's the control story banks need.
- Second-order effects surfaced — one lever shows what moves three lines downstream.
- Commentary as first-class — the "why" is captured, tagged, versioned — institutional memory that compounds instead of walking out the door.
F4 · Segment reporting architecture (public-bank standard)
How I approach it: segment reporting at a public-bank standard.
Enterprise P&L
↓
Consumer
Retail / deposits
+ Mortgage~20% of revenue
Commercial
Small Business
C&I
CRE
Each segment: Revenue − Direct exp − Allocated OH − Credit = Contribution · ROA / ROE · efficiency
Governed definitions, attribution & householding — same numbers every month
Governed definitions, attribution & householding — same numbers every month
- Sub-segment the commercial book — small business, C&I, CRE each behave differently on growth, margin, and credit.
- Full segment economics — direct + allocated cost, credit, and capital, not just revenue.
- Attribution is the hard part — every client has to land in the right segment, which means the householding work to roll accounts and relationships up to the correct entity before anything is bucketed. I've already built the segment data spine; governance makes it board-grade.
F5 · Capacity, prioritization & project-ROI governance
How I approach it: "what can we afford" — project-ROI and investment governance through a two-tier committee model (RAC and EIC).
Initiative demandLOB · enterprise · mandatory · carryover
→
Categorize & sizestrategic / mandatory / regulatory · $ · complexity
→
Balance vs. capacityfinancial + people + execution
↓
Business case → pro forma + project ROI
→
Business Case Review (BCR)vets, aligns & routes
↓ final approval — two lanes ↓
RAC — Resource Allocation Committee
Top strategic & enterprise callssets investment pools & funding across portfolios
Highly strategic, large, or unfunded initiatives
EIC — Executive Investment Committee
Delegated authority from RACmandatory, infrastructure & corporate-function work
Prioritizes spend & oversees executioncontinue · limit · stop
↓
Approved → pipeline / kickoff
- Capacity constrains strategy — the approved portfolio reflects what the organization can actually execute, across financial and people capacity. Project size sets the lane: small work stays in the line of business; larger or strategic spend escalates to committee.
- Two lanes, one discipline — RAC (Resource Allocation Committee) sets the enterprise investment pools and decides the top strategic, large, and unfunded initiatives; it delegates mandatory, infrastructure, and corporate-function decisions to the EIC (Executive Investment Committee), which prioritizes that spend and oversees execution — continue, limit, or stop.
- My ROI tool plugs in here — business case → classified economics (recurring/sunk/capitalizable) → pro forma with project ROI, ready for BCR and the right committee.
F6 · M&A diligence & readiness framework
How I approach it: M&A from the FP&A seat. Two layers: the standard diligence process, then the finance-diligence workstream I personally led.
A · The standard end-to-end process how a disciplined diligence runs
Working groupsFinance · Risk/Credit · HR · Tech/Ops · Legal · LOB
→
VDR & request trackersweekly cadence · issue log
→
Durability analyses
↓
Segment-level financialsnormalize one-times · attribute income
→
Synergies + integration costcost & revenue · dis-synergies
→
Decision-ready memo
→
5 lensesfit · return · revenue · workforce · integration
- Working groups + cadence — clear ownership, a VDR request tracker, weekly updates, and a standardized diligence memo so output is decision-ready.
- Five decision lenses — strategic fit, financial return (EPS, TBV earnback, ROIC), revenue durability, workforce durability, integration executability.
- Readiness > heroics — the playbooks and models should exist before a deal is live, not be built under deal pressure.
B · The workstream I owned finance & strategic-finance diligence on the deal I led
Two questions that actually protect the deal:how durable is the revenue? how durable is the workforce that holds it?
↓
Revenue durability
Customer Revenue NLENew / Lost / Existing waterfall
Concentration · recurring vs. one-time
Revenue by RM / team / segment
Workforce durability
Employee NLE + distributionwhere revenue & risk concentrate
Comp vs. revenueper producer · fixed/variable
Incentives by role & area
↓
Normalized segment financials
Transaction-level income attributionby team / segment
Strip one-times → core operating profiletrue run-rate earnings power
Key people & incentive structure
Identify producers the thesis depends on
Retention / stay / change-in-controlprotect value · cut attrition
What my work drove: normalized earnings power & valuation inputs · retention & incentive design · revenue dis-synergy assumptions · final-round diligence questions
- I owned the two durability questions — revenue (customer New/Lost/Existing, concentration, recurring vs. one-time) and workforce (employee NLE, comp-vs-revenue, incentive analysis by role).
- I built the normalized segment financials — attributed transaction-level income to the right team/segment and stripped one-times to land on true run-rate earnings power and the defensible multiple.
- I fed key-people & incentive structure — flagged the producers the deal thesis depended on and the retention / change-in-control economics to protect revenue post-close.
F7 · FP&A maturity curve — where I'd take the function
How I approach it: my vision for the long-term FP&A operating model.
A · The maturity curve five levels of contribution to strategic decision-making
Most finance functions
World Class
Conventional finance function
Passive "Bookkeeper"
- Manages traditional functions
Directional support
"Platform Manager"
- Shares best practices
- Maximizes synergies between BUs
Portfolio management
"Operational Business Partner"
- Challenges operational dimensions of BUs
- Engages in target-setting / planning
Strategic driver
"Strategic Business Partner"
- Challenges operational performance & strategy
- Engages in planning, resource & capital allocation
Active value management
Proactive "Value Driver"
- Integrates business, financial & investor strategy
- Challenges BUs
- Actively manages value-driver performance
Contribution to the company's strategic decision-making →
Arvest's mandate: climb from a bottom-up, reactive base toward Strategic driver and Active value management.
B · Strategic-Finance value commitment what "success" looks like climbing the curve — my operating creed
Success progresses upward ↑
Trusted strategic advisor — proactively shares responsibility for overall efficiency & effectiveness
Unique leadership — properly challenges the business; effective enterprise partnering; influences operational decisions
Analytical focus — a support level that enables results-based decision-making
Providing accurate: actionable management reporting · E2E business-case support · driver-based comprehensive forecasts · long-term strategic planning linking objectives to financial goals
How we deliver: intellectually curious · out-of-the-box · proactive · coach / coachable · team approach · communicate at all levels · confident / courageous · effective influence · prioritization
- The journey is the job — from report production to active value management via metric integrity, automation, faster insight, and partnership.
- Talent is the unlock — job-applied learning, coaching, assessment, and upskilling (SQL, Power BI, DAX, AI) embedded in the forecast/review rhythm.
- I've walked this curve twice — "peer laggard to peer leader" at First Horizon; consolidation + governance at Pinnacle.
F8 · Profitability & scale transformation — the "Good to Great" method
How I approach it: improving profitability and positioning for scale — the method end to end. Figures are illustrative.
Trigger: returns / TSR lagging peers
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Diagnose — regression on valuation driverswhat is the market actually pricing? safety & soundness vs. returns
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Benchmark peers & bucket operating modelsefficiency ratio · ROAA · deposit cost & beta · fee-income mix
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Conclude — winning operating profiles → strategycapital recycling + relationship deepening + expense optimization
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Internal diagnostics
BU operating efficiency vs. returnsaccretive vs. dilutive units
NIR by component · gaps vs. peers
Read efficiency within business type
Fee / wealth run structurally higher than commercial lendingbenchmark inside the model, not blended
↓ translate into the opportunity set ↓
Balance-sheet optimization
Funding · spreads · fee-income generation
Business reimagination
Transform a business's operating model
Corporate-service optimization
Streamline delivery & reduce cost
Translate to governed, quantified targets the businesses own → path to top-quartile ROAA & efficiency
- Start where value leaks — start from what the market is actually pricing before picking levers.
- Benchmark by operating model, not the average — peers cluster into winning profiles, and efficiency ratio only means something within a business type.
- Diagnosis → owned targets — three buckets (balance sheet · business reimagination · corporate service) become quantified goals the businesses are accountable for.
Closing
Why this role and I fit, in one place.
In short
This role sits right at the intersection of the work I do best — regional-bank finance, FP&A transformation, analytics and automation, and executive decision support. I've already built pieces of what it calls for: top-down, driver-based planning with governed AI; segment-level profitability; project-ROI governance; and the finance diligence behind an acquisition. I'd bring that directly to Arvest's move from $28B to $40B and beyond.
Alex Cardell · Head of FP&A candidate · Birmingham, AL