Q1 2026 fintech dataset
416 companies across 9 niches, with public, private, and M&A comps in one working Excel file.
Each tab answers a different valuation question. Use the summary tabs to frame valuation range, then use the database to build a peer group you can actually defend.
Average and median EV/Revenue by niche, with percentile ranges and dispersion across public, private, and M&A segments.
416 companies with EV, revenue, EBITDA, funding stage, and EV/Revenue multiples, each mapped with source links.
Public vs. private vs. M&A pricing by segment, helping you see where premiums hold and where they compress.
Private market multiples from Seed to Pre-IPO, structured for stage-matched benchmarking.
Transaction multiples by niche versus public and private benchmarks, with premium and discount analysis.
Source notes, methodology, and usage terms, structured for internal work and client-facing support.
This is the level of detail inside the file, with structured outputs across major fintech segments.
| Niche | Companies | Avg EV/Rev | Median EV/Rev | Avg Funding |
|---|---|---|---|---|
| Payments & Transfers | 82 | 7.7x | 3.6x | $444M |
| Lending & Credit | 59 | 11.8x | 7.7x | $181M |
| Banking & Neobanks | 55 | 12.4x | 6.9x | $495M |
| WealthTech & Robo-Advisors | 34 | 25.0x | 16.2x | $279M |
| Blockchain & Crypto | 40 | 26.6x | 14.2x | $314M |
| SMB & Enterprise Fintech | 46 | 17.1x | 10.1x | $428M |
| Company | Type | EV ($M) | Revenue ($M) | EV/Rev |
|---|---|---|---|---|
| Stripe | Private | $159,000 | $38,000 | 4.2x |
| PayPal | Public | $40,080 | $33,170 | 1.2x |
| Adyen | Public | $17,030 | $2,380 | 7.2x |
| Wise | Public | $8,650 | $1,720 | 5.0x |
| Remitly | Public | $2,890 | $1,640 | 1.8x |
| TransferGo | Private | $250 | $79.5 | 3.1x |
| Stage | Companies | Median EV/Rev | Avg EV/Rev | 25th–75th |
|---|---|---|---|---|
| Seed | 39 | 8.0x | 12.7x | 2.7–13.8x |
| Series A | 36 | 7.6x | 15.4x | 4.4–16.4x |
| Series B | 47 | 9.1x | 14.8x | 4.6–16.5x |
| Series C | 55 | 7.2x | 12.5x | 3.8–14.6x |
| Series D | 28 | 9.3x | 15.9x | 4.9–18.1x |
| Growth / Pre-IPO | 22 | 5.3x | 9.8x | 2.8–11.2x |
| Niche | Deals | Median EV/Rev | vs Public | vs Private |
|---|---|---|---|---|
| Payments & Transfers | 17 | 5.3x | +2.3% | −18.6% |
| Lending & Credit | 5 | 8.1x | +6.0% | +73.6% |
| InsurTech | 5 | 4.8x | −12.4% | −44.2% |
| Blockchain & Crypto | 7 | 11.6x | +41.2% | +18.5% |
| RegTech & Compliance | 5 | 7.4x | +5.1% | −28.3% |
| Capital Markets | 6 | 5.9x | −3.6% | −22.1% |
* Illustrative preview. The full dataset contains 416 companies with source links and working analysis tabs.
This is not a lightweight market summary. It is a working file for professionals who need current numbers, clean segmentation, and a peer set they can defend.
Frame internal valuation ahead of a fundraise and understand how stage, business model, and niche affect trading range.
Build comp sets faster, filter by niche and stage, and arrive at internal discussions with the benchmark work already done.
Understand transaction premiums and discounts versus public benchmarks, and separate strategic value from market noise.
Support share issuances, ESOP work, impairment testing, or investor reporting with a current and cited comparable set.
Collected through post-purchase feedback and shared without attribution.
We used the dataset to come up with an internal valuation and it helped us confirm assumptions we had been relying on anecdotally. Having structured comp sets by AI niche made the difference.
Exactly what I needed to build a defensible comparable set for an AI infrastructure client. The niche segmentation meant I wasn't averaging across companies that aren't genuinely comparable.
We used the dataset to construct our valuation and support our argument for a revenue multiple in the sale. It gave us a structured, defensible comp set we could put in front of the other side.
We used the dataset to benchmark a valuation on a company we had invested in, with third parties also reviewing the position. It gave us a credible, structured reference point we could share externally.
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