Selected work
Case Studies
These case studies demonstrate how I approach financial, operational, and analytical problems. Certain company names, assumptions, values, and supporting data have been generalized or anonymized to protect confidential information.
Case Study 01
Broadband Expansion Investment Modeling
Expansion Screening Model
Representative cluster · $M
IRR
15.2%
Hurdle rate 12.0%
NPV
$9.4M
9% discount rate, 10 yrs
Cost / Passing
$3.9K
Rural aerial & underground mix
Payback
6.8 yrs
Discounted basis
Build cluster ranking
Ranked by IRR
| # | Cluster | Passings | Capex | IRR | Payback | Screen |
|---|---|---|---|---|---|---|
| 01 | Cluster 01 | 14,200 | $55M | 15.2% | 6.8 yrs | Fund |
| 02 | Cluster 02 | 9,800 | $43M | 12.6% | 7.9 yrs | Fund |
| 03 | Cluster 03 | 7,300 | $38M | 9.8% | 9.4 yrs | Review |
| 04 | Cluster 04 | 5,100 | $34M | 6.4% | 12.1 yrs | Defer |
Cumulative discounted cash flow — Cluster 01
$M · Y0–Y9
Prioritization output
Advance Clusters 01 and 02 — both clear the 12% hurdle with payback inside eight years at a 35% base take rate. Cluster 03 returns for funding and take-rate review; Cluster 04 is deferred pending cost-per-passing reduction.
Representative view based on actual work; figures generalized to protect confidential information.
Business Challenge
A large pipeline of infrastructure opportunities required consistent financial evaluation and prioritization across different markets, cost structures, and operating assumptions.
Analytical Approach
Developed financial models incorporating ROI, IRR, NPV, payback period, operating forecasts, sensitivity analysis, and multiple investment scenarios. Structured the analysis so decision-makers could compare opportunities using consistent financial criteria.
Tools and Methods
- Advanced Excel
- Financial modeling
- Power Query
- Scenario analysis
- Dashboard reporting
Outcome
Supported evaluation and prioritization across more than $1.2 billion in potential projects and analysis involving more than one million underserved locations.
Case Study 02
Audit Income Forecasting and Budgeting
Audit Income & Budget Model
Monthly · $M
Forecast income
$1.54M
Base case, monthly
Budget margin
34%
After staffing cost
Break-even recovery
$9.8M
Covers 9-FTE cost
Forecast accuracy
±3.1%
Rolling 3 months
Audit income vs. budget
$M per month
- Budget
- Actual at/above
- Actual below
Key assumptions
- Forecast horizon
- 12 months
- Base recovery rate
- 6.2% of reviewed value
- Accounts per FTE / mo
- 180
- Billing rate
- 3.0% of recovered value
- Base staffing
- 9 FTE
Driver sensitivity — monthly margin
Margin, pts
- Recovery rate-3.4 / +3.1 pts
- Account complexity-2.6 / +1.2 pts
- Staffing (FTE)-2.1 / +2.4 pts
- Billing rate-1.5 / +1.8 pts
- Turnaround time-1.3 / +1.1 pts
Downside impact shown left of centre, upside right; recovery rate and account complexity dominate outcomes.
Recovery scenarios
Income · margin
| Scenario | Income | Margin | Assessment |
|---|---|---|---|
| Upside | $1.82M | 41% | Above target |
| Base | $1.54M | 34% | At target |
| Downside | $1.21M | 22% | Below target |
Budgeting output
Hold 9 FTE in base case — at 6.2% recovery the program clears the break-even recovery volume with 34% margin. Upside recovery supports a 10th FTE; the downside case flags staffing reduction if complex accounts push throughput below 150 accounts per FTE.
Representative view based on actual work; figures generalized to protect confidential information.
Business Challenge
A large-scale unclaimed-property audit program ran across banking and brokerage accounts with highly variable recovery volumes. Leadership needed a reliable forecast of audit income and a budget that linked staffing levels, account complexity, and turnaround assumptions to expected revenue and margin — so resourcing decisions could be made ahead of the quarter rather than after it.
Analytical Approach
Built a forecast model that translated historical recovery rates, account mix, and team productivity into expected monthly income, then structured the budget around staffing tiers and per-account processing assumptions. The model compared base, upside, and downside recovery scenarios against the budgeted cost of audit work, surfacing the break-even recovery volume and the staffing level at which margin compressed.
Tools and Methods
- Excel
- Forecasting
- Budgeting
- Variance analysis
- Scenario modeling
- Dashboard reporting
Outcome
Supported resource-planning and audit-strategy decisions across more than $100 million in unclaimed and reportable assets, and improved the ability to compare recovery scenarios against operating cost and adjust staffing before the quarter.
Case Study 03
Monthly Executive Audit Status Report
Monthly Executive Audit Status
Report period · May
Accounts reviewed YTD
12,400
On plan through May
Assets identified YTD
$104.6M
Across all segments
Recovery rate
6.4%
Target 6.0%
Accounts at risk
38
Awaiting client response
Recovered vs. reportable value
$M per month
- Reportable
- Recovered
Segment status
Value identified YTD
| Segment | Phase | Value | Status |
|---|---|---|---|
| Banking — Northeast | Field review | $28.4M | On track |
| Brokerage — Metro | Reconciliation | $22.1M | Watch |
| Banking — Midwest | Reporting | $18.9M | On track |
| Brokerage — West | Field review | $15.2M | At risk |
Audit-to-decision workflow
- Audit intake
- Field review
- Reconciliation
- Reporting
- Decision
Month-end summary
Recovery rate holds above target at 6.4% with $104.6M identified YTD across four segments. The Brokerage — West segment is flagged at risk pending client response on 38 open accounts; all other segments are on track through field review and reporting.
Representative view based on actual work; figures generalized to protect confidential information.
Business Challenge
Audit work spanned multiple clients, account types, and processing teams, with status updates scattered across working files. Leadership needed a single monthly executive view showing audit progress, recovered and reportable asset totals, budget-to-actual spend, and accounts at risk — in a format consistent enough to act on every month.
Analytical Approach
Consolidated audit status data into a standardized monthly report with a fixed KPI set: accounts reviewed, assets identified, recovered versus reportable value, budget-to-actual spend, forecast accuracy, and accounts at risk. Standardized the logic so every month used the same definitions and layout, reducing manual prep and letting leadership compare months directly.
Tools and Methods
- Excel
- Power Query
- Dashboard reporting
- Variance analysis
- KPI development
Outcome
Reduced manual reporting work, improved month-over-month consistency, and gave leadership a repeatable view of audit progress and financial status across more than $100 million in identified assets.