GCC HNW Directory — Find Wealthy Gulf Individuals by Name

Built for Youssef (SSM) by Saad Belcaid.

This scraper finds named high-net-worth individuals across the Gulf — Saudi Arabia, UAE, Qatar, Kuwait, and Bahrain. ~940 unique named GCC HNW individuals across all 5 countries.

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What's actually in the dataset

GCC HNW is structurally different from the West. There's no Companies House. Most wealth is private and family-held. So instead of a single feed, this scraper pulls every public record where named GCC HNW individuals show up — billionaires, business executives, ruling-family members — and merges them into one verified directory.

After cross-source dedup by normalized Latin name + country: ~940 unique HNW individuals with name, country, role (billionaire / business executive / royal family member), and a confirmation count (how many independent records confirmed this person).

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Volume by country

Saudi Arabia (SA)  ─  288 individuals
UAE (AE)           ─  210 individuals
Kuwait (KW)        ─  259 individuals
Qatar (QA)         ─  105 individuals
Bahrain (BH)       ─   78 individuals
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TOTAL              ─  ~940 unique HNW

~80 multi-confirmed — same person showing up in 2+ independent records (e.g. Mohammed bin Rashid Al Maktoum, Hussain Sajwani, Mohammed bin Salman, Hamad bin Jassim Al Thani). These are your highest-confidence leads.

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How to use it

Default run — get the full directory

countries: ["SA", "AE", "QA", "KW", "BH"]
minWealthBand: "unknown"
includeUnknownWealth: true

Hit run. Wait ~3-5 minutes. Expect ~940 rows.

Filter recipes

Saudi only:

countries: ["SA"]

Just the most-confirmed names: After scrape, filter dataset by confirmation_count >= 2 — gives you ~80 highest-confidence rows.

Royal family only: After scrape, filter by role == "royal family member" — ~700 ruling-family members across the 6 GCC dynasties.

Working executives (not royals): After scrape, filter by role IN ("business executive", "billionaire").

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Output — one row per HNW individual

Field	Example
person_name	Mohammed bin Rashid Al Maktoum
country	AE
country_name	United Arab Emirates
wealth_band	$10B+ (when confirmed) / unknown
source_of_wealth	royal household — Dubai
role	royal family member
primary_company	(often null — most rows are person-attributed not company-attributed)
confirmation_count	3
signal	"Mohammed bin Rashid Al Maktoum, royal family member (United Arab Emirates) — royal household — Dubai — 3 confirmations"
scraped_at	ISO timestamp

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How the dedup key works

GCC names appear in many forms:

• "H.H. Sheikh Mohammed bin Rashid Al Maktoum" (formal)
• "Mohammed bin Rashid Al Maktoum" (standard)
• "Mohammad Bin Rashid Al-Maktoum" (alternate transliteration)

The dedup function strips honorifics (H.H., Sheikh, Prince), normalizes Al-/Al particle spacing, lowercases, and removes diacritics. The three forms above all collapse to mohammad bin rashid al maktoum and merge into one row with confirmation_count = 3.

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Honest limitations

1. Some rows are historical figures. Past kings (King Abdullah, King Salman) appear in the directory. Filter post-scrape if your CRM only wants living targets.
2. No live freshness signal for most countries. GCC exchanges don't expose SEC-style insider feeds. The directory is best read as a stable HNW list, not a fresh-event feed.
3. Wealth bands are mostly unknown. Public records publish names; USD net worth is usually only available for the top tier. Post-process if you need wealth tiering for everyone.
4. Royal family members dominate. ~700 of 940 are ruling-family entries. Some are minor princes/princesses; some are heads of state. They're all HNW but the wealth distribution within ruling families is uneven.

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Architecture

• Apify Actor, TypeScript, Playwright
• Multiple inputs run in parallel
• Apify Residential Proxy recommended for resilience
• Cross-record aggregator dedups by normalized Latin name + country, merges fields, picks the highest wealth band when records disagree

Costs

• Apify compute: ~3-5 min per run × 1024 MB ≈ trivial CU
• Apify Residential Proxy (optional): ~$0.10 per run

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Connector OS Station integration

Pipe the dataset into Station as the demand side. The signal field plugs straight into the I Layer for evaluation against your supply network (wealth managers, family-office consultants, real-estate brokers, alt-investment platforms).

Flow: scrape → dataset → paste dataset ID into Station → match against your supply network → scored introductions.

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Built by Saad Belcaid for Youssef's SSM workflow.