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MemberShield — Fraud Decisioning for Community Banks & Credit Unions — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Financial Industry →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
rebelsffm.com ↗	68 match 1 shared topics	Rebel Structured Finance \| Sale-Leaseback for Credit Unions	en	financial-industry	robotsllmsaihumans	emailphone	none
hacaudit.com ↗	68 match 1 shared topics	Supervisory Committee Audits & Risk Advisory Services for Credit Unions	United States~ en	financial-industry WordPress WooCommerce	robotsllmsaihumans	emailphone	none
askalfi.ai ↗	68 match 1 shared topics	Alfi — Decision Intelligence for Liquidity, Credit & Capital Allocation	en	financial-industry	robotsllmsaihumans	emailphone	partial · 8
phoenixintellex.com ↗	67 match 1 shared topics	Phoenix · Intellex — Credit Decision Intelligence	en	financial-industry	robotsllmsaihumans	emailphone	partial · 8
ask-holmes.com ↗	66 match 1 shared topics	Holmes \| Securing the future for Credit UnionsHolmes	en	financial-industry WordPress	robotsllmsaihumans	emailphone	none
gulfscore.com ↗	66 match 1 shared topics	GulfScore - The First Decision Engine for the Middle East Region	en	financial-industry WordPress	robotsllmsaihumans	emailphone	none
unionfintech.com ↗	66 match 1 shared topics	Union Fintech \| Cloud-Native Core Banking Platform	en	financial-industry WordPress	robotsllmsaihumans	emailphone	none
mddccua.org ↗	66 match 1 shared topics	MD\|DC Credit Union Association	en	financial-industry WordPress	robotsllmsaihumans	email phone	none
ofscreditcompany.com ↗	66 match 1 shared topics	OFS Credit Company \| OCCI	en	financial-industry WordPress	robotsllmsaihumans	emailphone	none
cdfintelligence.com ↗	66 match 1 shared topics	Blueprint of Community Capital: Decoding CDFI Loan Funds for Lenders — CDFIntelligence	en	financial-industry Gumroad	robotsllmsaihumans	emailphone	none
efraudprevention.com ↗	66 match 1 shared topics	eFraud Prevention – Fraud Awareness & Prevention Since 2004	en	financial-industry	robotsllmsaihumans	emailphone	none
cellery.ai ↗	65 match 1 shared topics	Cellery® Behavioral Intelligence \| Fraud Detection for Financial Institutions	United States~ en	financial-industry	robotsllmsaihumans	emailphone	none
fintechnorth.ca ↗	65 match 1 shared topics	Fintech North \| Canada's Fintech Practitioner Community	Canada en	financial-industry Lightspeed	robots llmsaihumans	emailphone	partial · 8
onlylandjourney.com ↗	65 match 1 shared topics	Landjourney — The all-in-one platform for agricultural lenders	en	financial-industry	robotsllmsaihumans	emailphone	none
cdbanks.org ↗	65 match 1 shared topics	Community Development Bankers Association (CDBA) \| Partners for the Common Good \| Community Development Bankers Association	en	financial-industry Drupal	robotsllmsaihumans	emailphone	partial · 8
qolo.io ↗	65 match 1 shared topics	Payments Infrastructure for Banks and Platforms \| Qolo	en	financial-industry WordPress	robotsllmsaihumans	emailphone	none
codat.io ↗	65 match 1 shared topics	Codat \| Advisory Intelligence for Commercial Banking	en	financial-industry WordPress WooCommerce	robots llmsaihumans	emailphone	none
capitallink.com ↗	65 match 1 shared topics	Capital Link – Your Link To the global Investment Community	en	financial-industry WordPress	robotsllmsaihumans	email phone	none

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.