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Channel

Reinsurance Journal 📊

@reinsu

On this record: Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry

1,819subscribers

+52 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 1,000–3,162.

Register entry

Telegram ID-1001492986807
TypeChannel
Username@reinsu
CreatedBetween 1 April 2019 and 31 August 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live12 August 2026
Measurements held5
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/reinsu

Growth

1,7671,8191,7936 August 2026 — 1,767 subscribers6 August 2026 — 1,767 subscribers6 August 2026 — 1,782 subscribers9 August 2026 — 1,818 subscribers12 August 2026 — 1,819 subscribers6 August 202612 August 2026
5 measurements spanning 7 days, net +52. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 1,759–1,827 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 21:081,819+1
9 Aug 2026, 11:021,818+36
6 Aug 2026, 16:201,782+15
6 Aug 2026, 07:021,767no change
6 Aug 2026, 02:481,767first reading

Engagement

23 posts held, back to 24 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
23.8%
avg views ÷ 1,819 subscribers
Avg views / post
433
23 posts measured
Reaction rate
0.39%
reactions ÷ views · ER floor
Posts in window
23
of 23 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 14 of 23 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 7 August 2026
Posts held23 (24 July 20267 August 2026)
Views total9,965
Reactions total28
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Aug 2026, 17:31 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

Reaction mix

28 reactions across 13 posts, in 3 distinct kinds. The most used accounts for 50.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
1450.0%
👍1242.9%
🔥27.14%

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 14 of the 23 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 28reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 23 most recent posts we hold, published 24 July 2026 to 7 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Recent posts

7 Aug 2026, 05:04 UTC216 viewsread 7 August 2026
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اگر فاصله میان اعلام خسارت تا پرداخت، از چند روز به چند دقیقه کاهش یابد، تجربه بیمه‌گذار چگونه تغییر خواهد کرد؟ هوش مصنوعی در شرکت‌های بزرگ بیمه‌ای جهان، دیگر صرفاً ابزاری برای پاسخ‌گویی یا انجام امور اداری نیست؛ بلکه به بخشی مؤثر از فرایند ارزیابی و پرداخت خسارت تبدیل شده است. شرکت‌هایی مانند Ping An، Allianz، Zurich و Generali با استفاده از تحلیل تصاویر، پردازش هوشمند اسناد، شناسایی تقلب و خودکارسازی پرونده‌های س

Signed Feryal Farakesh

6 Aug 2026, 17:25 UTC274 views2 reactionsread 7 August 2026
Photo

معرفی شان جلیلوند نویسنده و متخصص حرفه‌ای صنعت بیمه

🔥2

6 Aug 2026, 06:19 UTC554 views4 reactionsread 7 August 2026
File

🚨 آینده صنعت بیمه اینجاست؛ آیا آماده‌اید یا ناپدید می‌شوید؟ کتاب «نردبان ناپدیدشونده بیمه» به صورت رایگان منتشر شد. آیا صنعت بیمه برای سونامی هوش مصنوعی آماده است؟ این کتاب با نگاهی ساختارشکنانه به دگردیسی مدل‌های کسب‌وکار در بیمه، بازتعریف نقش انسان و بازتعریف مدیریت ریسک در عصر هوش مصنوعی می‌پردازد؛ اثری کلیدی که اکنون با سخاوت جناب آقای شان (سعید) جلیلوند برای بهره‌برداری جامعه تخصصی در دسترس قرار گرفته است. ا

4

5 Aug 2026, 08:50 UTC330 viewsread 7 August 2026
File

نقش پژوهشکده‌ها و اندیشکده‌ها در توسعه و حکمرانی بهتر: از پژوهش تا سیاست‌گذاری

Signed Feryal Farakesh

5 Aug 2026, 08:49 UTC333 viewsread 7 August 2026

نقش پژوهشکده‌ها و اندیشکده‌ها در توسعه و حکمرانی بهتر: از پژوهش تا سیاست‌گذاری پژوهشکده‌ها و اندیشکده‌ها هر دو در توسعه صنعتی و بهبود کیفیت حکمرانی کشور نقش مهمی دارند، اما کارکرد آن‌ها یکسان نیست. پژوهشکده بیشتر به دنبال شناخت دقیق مسئله است یعنی داده جمع‌آوری می‌کند، روندها را بررسی می‌کند و با استفاده از روش‌های علمی به این پرسش پاسخ می‌دهد که یک مسئله چرا به وجود آمده و چه ابعادی دارد. از سوی دیگر، اندیشکده از

Signed Feryal Farakesh

5 Aug 2026, 03:38 UTC302 viewsread 7 August 2026
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در قیمت‌گذاری سنتی قراردادهای اتکایی، سابقه حق‌بیمه و خسارت نقطه شروع تحلیل است؛ اما تغییرات اقلیمی، تورم خسارت، افزایش ارزش دارایی‌ها، تمرکز جغرافیایی ریسک‌ها و ظهور مخاطراتی مانند سایبر باعث شده‌اند گذشته، دیگر به‌تنهایی تصویر دقیقی از آینده ارائه نکند. فناوری‌های نوین این محدودیت را کاهش می‌دهند. هوش مصنوعی می‌تواند الگوهای پنهان خسارت را شناسایی کند؛ پردازش هوشمند اسناد، داده‌های اسلیپ و بردرو را به اطلاعات ساختا

Signed Feryal Farakesh

4 Aug 2026, 03:41 UTC314 views2 reactionsread 7 August 2026
Photo

سال‌هاست بخش مهمی از فرایند واگذاری اتکایی میان فایل‌های اکسل، ایمیل‌های متعدد، نسخه‌های مختلف اسلیپ، مکاتبات پراکنده و پیگیری‌های دستی انجام می‌شود. در چنین ساختاری، مسئله فقط طولانی‌شدن فرآیند واگذاری نیست؛ احتمال مغایرت داده‌ها، گم‌شدن سوابق مذاکرات، تأخیر در دریافت پاسخ، دشواری مقایسه پیشنهادها و ضعف قابلیت حسابرسی نیز افزایش می‌یابد. پلتفرم‌های الکترونیکی اتکایی این زنجیره را بازطراحی می‌کنند. اطلاعات ریسک به‌صو

👍2

Signed Feryal Farakesh

3 Aug 2026, 04:59 UTC334 views1 reactionsread 7 August 2026
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*ارزان‌ترین قرارداد اتکایی، لزوماً بهترین قرارداد نیست.* فرض کنید دو برنامه اتکایی پیش روی یک شرکت قرار دارد: 🔹 برنامه اول، حق‌بیمه واگذاری کمتری دارد؛ اما در سناریوی خسارت شدید، سرمایه شرکت را در معرض نوسان جدی قرار می‌دهد. 🔹 برنامه دوم، هزینه بیشتری دارد؛ اما نگهداری، فرانشیز، ظرفیت لایه‌ها و پوشش تجمع خسارت در آن براساس ساختار واقعی پرتفوی طراحی شده است. انتخاب حرفه‌ای با مقایسه قیمت این دو برنامه پایان نمی‌یاب

👍1

Signed Feryal Farakesh

2 Aug 2026, 08:05 UTC350 viewsread 7 August 2026
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📊 صورت‌های مالی می‌گویند قرارداد سودآور است؛ اما خسارت‌ها هنوز همه‌چیز را نگفته‌اند. در بیمه اتکایی، فاصله زمانی میان وقوع خسارت، اعلام آن از سوی شرکت واگذارنده و ثبت ذخیره می‌تواند تصویری فریبنده از عملکرد قرارداد ایجاد کند. وقتی گزارش خسارت، Bordereaux یا اطلاعات تکمیلی دیر می‌رسد، ذخایر کمتر از واقع برآورد می‌شوند؛ در نتیجه سود، سرمایه آزاد و نتیجه فنی قرارداد موقتاً بهتر از واقعیت به نظر می‌رسد. این همان نقطه‌ای

Signed Feryal Farakesh

1 Aug 2026, 04:13 UTC434 views2 reactionsread 7 August 2026
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در واحد مالی بیمه اتکایی، یک مغایرت کوچک همیشه کوچک باقی نمی‌ماند. تأخیر در دریافت صورت‌حساب‌های فنی و بردرو، تفاوت در قالب و کیفیت داده‌ها، ثبت‌های تکراری یا ناقص، مغایرت حق‌بیمه و خسارت، خطا در محاسبه کمیسیون،مشارکت در منافع و حق بیمه برقراری مجدد لایه می‌تواند تسویه حساب‌ها، ذخایر و گزارش‌های مدیریتی را با انحراف جدی مواجه کند. هوش مصنوعی می‌تواند اسناد و حساب‌های فنی را استخراج و طبقه‌بندی کند، مغایرت‌ها و ثبت‌ها

2

Signed Feryal Farakesh

31 Jul 2026, 02:46 UTC391 views1 reactionsread 7 August 2026
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یک پرونده بزرگ صنعتی روی میز Underwriter قرار می‌گیرد؛ صدها صفحه سند، چندین موقعیت جغرافیایی، ارزش‌های انباشته و زنجیره‌ای از ریسک‌های به‌هم‌پیوسته. در روش سنتی، بررسی چنین پرونده‌ای ممکن است روزها زمان ببرد؛ اما در شرکت‌های پیشرو اتکایی، تصمیم‌گیری از همان لحظه ورود داده آغاز می‌شود. Munich Re با تحلیل مکانی و سناریوهای اقلیمی، Swiss Re با CatNet® و داده‌های جغرافیایی، Hannover Re با قواعد خودکار و قابل‌ردیابی، و S

👍1

Signed Feryal Farakesh

Showing the 12 most recent of 23 posts we hold for @reinsu. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Citation-graph rank

Citation-graph rank — 164,182 of 1,176,251entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

Forward network

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 4 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

حوادث روز
@havades_roz · 256,256
Telegram ranks this channel #79 of 83 here — alongside 82 others — read 10 August 2026

This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 12 August 2026 — this entry's latest reading, not the date you are reading this.

“Reinsurance Journal 📊” (@reinsu), 1,819 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/reinsu.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.