Oconee Federal Savings & Loan
A 102-year-old thrift with 81% of its loan book in residential mortgages is, structurally, a verification business. Every file Oconee Federal originates runs the same document chase: identity, income, employment, assets, property. This is what that chase costs today, and what a single verification link changes.
All figures are estimates built from public data (FDIC, HMDA, CRA filings). Read the full methodology
Where the time goes today
Roughly 200 files a year need borrower verification at Oconee Federal: identity, income, employment, assets, and property, collected today through document requests and follow-up calls.[3]
That is 0 staff hours a year in the expected case, recovered as origination capacity rather than headcount reduction.[1]
Value by lending line
Different files carry different verification loads. Commercial files (beneficial ownership, guarantors, business financials) take the longest; consumer files the least. Expected-case annual labor value:[1][2]
The full math
| Line | Conservative | Expected | Optimistic |
|---|---|---|---|
| Staff time savings[1][2] | $40K | $75K | $120K |
| Pull-through revenue (2-8 added closings)[4] | $2K | $4K | $6K |
| Total estimated annual value | $42K | $79K | $126K |
The growth side: new residents, captured digitally
Roughly 550 new households move into Oconee Federal's footprint every year, and about 30% of movers open an account with a new bank. They shop with their phones. A white-label, fintech-grade intake flow (the same 5-minute experience RAVEN runs for verification) turns that migration into a lead channel the bank owns instead of renting.[6]
| Annual | Conservative | Expected | Optimistic |
|---|---|---|---|
| Digital leads captured | 8 | 22 | 50 |
| Funded loans from those leads | 1 | 7 | 25 |
| Value (loan profit + avoided lead spend) | $1K | $12K | $57K |
This is new revenue, not savings, so it is shown separately and excluded from the headline number above. The younger and newly arrived households a digital flow captures are exactly the relationships a branch network alone does not reach.
See these numbers against your actual workflow
A 20-minute call with a live verification using test data. You'll see the borrower flow and the loan-officer dashboard end to end, and we'll pressure-test every assumption above with your real volumes.
Beyond the dollar math
The mortgage book is the bank
With $397M of $488M in net loans in 1-4 family residential, verification speed is not a back-office detail. It is the production line. Every hour saved per file compounds across essentially the whole book.
Lake Keowee borrowers arrive with fintech expectations
South Carolina leads the nation in 65+ net migration, and relocating retirees closed their last mortgage with a digital-first lender. Matching that experience locally is how a deposit-dominant franchise keeps the loan too.
Margin recovery is finite; capacity is not
The NIM climb from 2.19% to 2.94% came from repricing, and that lever runs out as the back book catches up. The next leg of growth has to come from volume, which makes per-file capacity the binding constraint.
We also published an independent analysis of Oconee Federal's performance and market:
Read: The Quiet Comeback at Oconee FederalMethodology & footnotes
Hours saved per file. Published verification-automation case studies (Blend Labs, 2025) report 15-16+ staff hours saved per mortgage file across loan officers, processors, underwriters, and compliance. We model mortgages at 6-14 hours, commercial files (which add beneficial ownership, guarantor identity, and business financials) at 8-16 hours, and simpler consumer or HELOC files at 2-6 hours. The expected case sits well below published benchmarks on purpose.
Loaded staff cost. The $38-48/hour range blends Bureau of Labor Statistics OEWS rates for South Carolina loan officers (~$30/hr), processors (~$28/hr), underwriters (~$55/hr), and compliance staff (~$50/hr), including benefits. Most verification labor falls on processors and loan officers, which is why the blend sits closer to the lower rates.
Verification volume. Mortgage counts come from HMDA Modified LAR filings via FFIEC, which report actual originations. Commercial, HELOC, and consumer volumes are estimates derived from FDIC call report loan mix and branch footprint; they are not reported figures and could vary materially. The 60-day pilot exists to replace these estimates with the bank’s own measured numbers.
Pull-through improvement. The MBA reports roughly 68% industry-wide mortgage application abandonment. We model a 1-5 percentage-point improvement applied to originations (not the larger application pool, which would produce a roughly 3x bigger figure), at the MBA-reported $785 average profit per closed loan. Published case studies report 10-15 point gains; our optimistic case is one-half to one-third of that.
What this is not. These figures are directional estimates built from public data and industry benchmarks. They are not a quote, a guarantee, or an analysis of the bank’s internal workflows, and recovered hours are modeled as redeployed origination capacity rather than headcount reduction. Banks already running highly automated verification will see less; banks running fully manual document collection will see more.
New-resident lead generation. TD Bank research reports roughly 30% of consumers open an account with a new bank after moving (and movers 55+ switch at a higher rate than millennials), while 91% of consumers say digital capability matters in choosing where to bank (MX, 2025) and more than half of online banking applications are abandoned mid-flow (The Financial Brand; Innovatrics). We model a bank with a white-label, fintech-grade intake flow capturing 1.5-9% of new-to-market households as started applications, converting 12-50% of those to funded loans (expected case: ~55% completion times the MBA-reported ~55% depository pull-through). Value per funded loan combines the $785 MBA average profit with $500-1,500 of avoided lead-acquisition spend, the going rate per funded loan from purchased shared and exclusive lead channels. New-household counts are derived from Census county population estimates and are not bank-reported figures. This line is shown separately and is not included in the headline savings number.
Data sources: FDIC BankFind (Cert #30111); OCC CRA Performance Evaluation (3/4/2024); HMDA Modified LAR via FFIEC; company quarterly releases; MBA Quarterly Mortgage Bankers Performance Report (2025); BLS OEWS (2025).