igot– > not exactly fraudster..(just quasi or almost)

TL; DR: The company igot is not exactly a fraudster, but any money/BTC you transact through them, is not expected to be paid out in anything close to reasonable time period.(aka mine’s pending since July 2016, for other examples see here.).

The company igot is a bitcoin exchange for people wanting to buy, sell, trade in bitcoin(BTC) with whatever currency they would like to.trade in.


Back in 2015, I had bought some bitcoins and was trading in them for a while selling when it rose, and buying back when it fell..  Eventually I had around 1.1 BTC, half of which I decided to cash-in sometime around July 2016, and sold and initiated a transaction with igot to debit the money to my account.


After a week of no debit transaction on my bank account I raised a support ticket hoping to get some resolution, but that was not responded to for a month, and only got a response once I threatened to go to the banking ombudsman in india(an authority for mediating complaints against banks.) . transactions_feb_24_2017 supportrequest2 supportrequest1 of the same.

Now this support request (and a email) response promises to start processing pending transaction from September 2016 and End by November 2016.

And in october I raised the ticket again and supportrequest2of vague, promise that never materialized.


At this point, I give up and wait for november to roll in and when it does, I try to contact them, but the support ticket/menu option had vanished. So I go back to the mail thread and mail them back with the following result. email_nov_3

And this just promises more in a few days time beginning the transaction processing. Note how the previous promise said the transactions would have been done by this time(Nov. 1- 2016).

Also note how the tone of the email has changed from (apologetic-sorry-promise to we-don’t-care-if-you-want-to-go-legal).

This is when I started realizing, that may be I’ve been dealing with dishonest, don’t-care-about-clients type of businessmen/management.

I do not know what is the right future action to take but I’m stuck for now with this blog post. Atleast for any one else, googling to evaluate the company, don’t do it. They are not reliable enough people to route your money through.


UPDATE: Ok I give up. They’re just frauding people and failing to communicate to old customers all together. Most likely because have no intentions of paying back the old customers. Seems like now they have a new website and page. For a long time, they’ve been talking about a resolution centre for old customers, but now they launch a complete new website. 

I just have no idea how to take them to task. Anyone with cyber crime division in India contact me please.

Statistical moments —

Inspired by this blog from paypal team. Moment is a physics concept(or atleast I encountered it first in physics, but it looks it has been generalized in math to apply to other fields).

If you followed that wikipedia math link above, you’ll know the formula for moment is
\mu_n = \int\limits_{-\infty}^{+\infty} (x-c)^n f(x)\ dx\
where x — the value of the variable
n — order of the moment (aka nth moment, we’ll get to that shortly)
c — center or value around which to calculate the moment.

However if you look at a few other pages and links they ignore that part c.. and of course use the summation symbol.**

The reason they don’t put up ‘c’ there is they assume moment around the value 0. As we’ll see below this is well and good in some cases, but not always.

The other part n- order of the moment is an interesting concept. It’s just raising the value to nth power. To begin with if n is even the the negative sign caused by differences goes away. So it’s all a summary and becomes a monotonically increasing function.

I usually would argue that the ‘c’ would be the measure of central tendency like mean/median/mode and a sign of fat-tailed/thin-tailed distributions is that the moments will be different if you choose a different c and the different moments change wildly.

The statsblog I linked above mentions something different.

Higher-order terms(above the 4th) are difficult to estimate and equally difficult to describe in layman’s terms. You’re unlikely to come across any of them in elementary stats. For example, the 5th order is a measure of the relative importance of tails versus center (mode, shoulders) in causing skew. For example, a high 5th means there is a heavy tail with little mode movement and a low 5th means there is more change in the shoulders.

Hmm.. wonder how or why? I can’t figure out how it can be an indication of fat-tails(referred by the phrase importance of tails in the quote above) with the formula they are using. i.e: when the formula doesn’t mention anything about ‘c’.

** — That would be the notation for comparing discrete variables as opposed to continuous variables, but given that most of real-world application of statistics uses sampling at discrete intervals, it’s understandable to use this notation instead of the integral sign.