Saturday, February 17, 2018

PNB Analysis - whatsapp forward

What actually happened in PNB scam? Let’s start from the concept.

First, The Concept

Let’s understand how things work.

Some importer, let’s call him Nirav Modi or NM, wants to import pearls or diamonds and then sell them. The purchase requires money, so NM approaches a bank, say Punjab National Bank (PNB).

PNB says look, I’ll give you a loan but it will be like at 10%.

NM thinks hard and says, no, that’s too much. Wait, why don’t I take a foreign currency loan instead, after all I’m buying in dollars? Much lower interest rates no? I can get at LIBOR+2% and LIBOR is like 1.5% so I’ll have the money at 3.5%!

But who will give NM a foreign currency loan? A bank abroad? They don’t know NM. They don’t have any history of NM, so why will they give him money?

SO NM goes to PNB and says, boss, you’re my banker, so please help some foreign bank give me some money to buy diamonds. Say that you will guarantee my loan by giving me a “Letter of Undertaking” (LOU).

PNB now should be saying look, if you want me to give Rs. 100 cr. guarantee, you give me stuff worth 110 cr. at least. As collateral.

But PNB, for some strange reason, doesn’t ask for collateral. More on that later.

So now the foreign bank is ready to lend NM the money. Because PNB will guarantee it. And the foreign bank trusts PNB. Why does it trust PNB?

Because PNB sends a message on SWIFT – the banking message service – that PNB guarantees Rs. 100 cr. of money for 180 days for Mr. NM at an interest rate of, say, LIBOR + 2%.  It’s like a message – written in stone, effectively – that says PNB will pay if NM doesn’t pay.

In fact the foreign bank trusts only PNB. So it gives the money to PNBs account with it, called by PNB as a “Nostro” – the account that PNB maintains with banks abroad, where the other bank will send money meant for PNB customers.

PNB’s nostro account gets the money.

PNB then gives NM the money from the Nostro account, usually paid off to whoever NM is buying his diamonds from. This payment is to someone outside India usually, to fund a purchase of diamonds or whatever.

Note this carefully: The other bank gives money to PNB’s Nostro account. Not to NM. They don’t care about NM. They only know that PNB has given a guarantee on the SWIFT channel.

Note: the other bank is nowadays mostly the foreign branches of Indian banks. Because the phoren banks have realized something sinister – that PNB’s guarantee is a strange beast that isn’t backed with much, but we’ll come to that

The foreign bank couldn’t care less about whether NM was buying diamonds or bitcoin – to them, PNB would pay back even if NM’s bitcoin wallet got stolen.

Why does PNB give a guarantee? Fees. Each year, a bank may charge upto 2% to give the LoU.

So What Happens When It’s Time To Pay Back?

NM has to get the pearls in India, sell them, receive the money and pay PNB. On the due date written on the LoU.

Then PNB will pay back the foreign bank saying okay, we got the customer’s money so we’re giving it back to you. With interest etc.

That’s what is supposed to happen. But in reality, things went a little berserk, it seems.

The Reality: A Bit of a Ponzi

NM might not pay back at all. NM might use the money to speculate in the markets. Or do something else.

What if NM in the above example simply didn’t have the money to pay back? Instead, he asks a PNB official to open ANOTHER LoU. For the amount owed plus interest. So if we had the first LoU at $10 million the second one is $11 million to cover the interest on the first.

The money from the second LoU is used to repay the first.  It’s just rolling over of credit. Over and over. Standard definition of a ponzi scheme.

This can easily balloon into a larger amount, so large that it’s too much. In effect many such arrangements have turned into semi-ponzi schemes, with one LoU being opened to repay another and so on.

Which is what is likely to have happened.

We don’t know the details, but it looks like:

Nirav Modi took loans from foreign branches of Indian banks through an LoU issued by PNB

This was done through a SWIFT based LoU issued through a rogue employee (or many of them) at PNB

The orders never showed up in the core banking system for monitoring

LoUs were rolled over all the way since 2011, and possibly increased over time too.

The rogue official retired in 2017, and the replacement refused to roll over the LoU which came due in Jan 2018 because he couldn’t find the past transactions in the system

No rollover means a default, since there was no money to pay. So PNB quickly files an FIR saying oh goodness we have lost 280 cr. on the Jan LoUs

Then someone said, “Abeyaar, is there more of these not-in-system LoUs? Someone check no?”

Then someone checked.

Oh gawd. 11,400 crores.

That’s a lot of crores.

Everyone in the bank panicked.

Why couldn’t Nirav Modi just pay it back? He must have the original money no?

Because if it was ever intended to be paid back, the rollovers wouldn’t have been required. At some point, things got so out of hand that rollovers were required in order to stay current.

Typically this would not be a problem. If PNB had done things right, they would have had collateral worth the amount of guarantee, and they would have sold that collateral and paid the foreign bank.

But, and here’s the real issue:  PNB didn’t have any collateral.

Why did PNB give a guarantee without collateral?

If you and I go for a loan to a bank, they’ll ask us for income proof, and collateral. Only small tiny personal loans and credit card loans come backed without collateral. For something of the order of 11,000 cr. you would think they would ask for collateral.

Especially after the scene with Mallya where loans to Kingfisher were given on nearly no collateral (though even there they had a house and some promoter shares pledged)

Why did PNB give this guarantee then? It’s typical – banks give guarantees for more the amount you give as collateral. Because business relationships etc. And then:

Because nearly every bank is doing it.

The loan was not a “fund based limit”. In a fund based limit like a term loan, the bank pays out money. In non-fund-based limits, the bank will only pay if someone else defaults or an event happens – like a Bank Guarantee or an LC or an LoU.

Meaning, PNB assumed that the foreign bank was giving a loan directly to Nirav Modi and that PNB needed to pay only in case Nirav Modi defaulted. So in the eyes of PNB it was always an “non-fund-based” loan.

But this is how a significant part of import financing works. They all rollover credit, and they all use LoUs for much higher than they can offer as collateral.

From my sources, the scale is huge. For every Rs. 100 that a bank has collateral, they will easily provide LOUs for upto 6x the amount. This is a real problem – that most public sector banks do not keep much collateral against non-fund-based limits given to importing customers.

So even if a bank has collateral, it’s nowhere near enough. And then, such unfunded liabilities are not even reported to RBI!

Basel Reporting: No Disclosure

PNB has “unfunded” exposure of 11,000 cr. they say. But they don’t even reveal it in their latest Basel III disclosure:

The funded exposure to “Gems and Jewellery” is shown at 1860 cr.

Unfunded to the same sector: 842 cr.

This doesn’t even add up. So, in effect, PNB didn’t reveal that it was funding massive quantities of “unfunded, contingent exposure”. They will of course pretend that they didn’t know, because the transactions weren’t in the core banking system.

Did Employees Hide it? Was PNB Responsible or was it a fraud?

Can employees be responsible? Could they have hidden the credit and the rolling over of LoUs? But honestly, how does a 11,000 cr. credit pass muster without top management realizing it?

Think of it – your nostro account with these other banks keeps getting big credits that add up to 11,000 cr. Will you not reconcile it in the accounting? The “why is this money even here?” question should have been asked by someone who audits accounts, one thinks?

And the SWIFT messages. It’s a specific kind of message. Why wouldn’t PNB audit the SWIFT trail? Reconcile it with the core banking system? How many more such skeletons will tumble if they do?

Their excuses are

Data wasn’t entered into the core banking system. (Of course, otherwise you would have had to report it)

LOUs weren’t authorized. (Hard to believe, because the amounts are very large. Surely someone on the top would know?)

The SWIFT system was illegally used. (Again, hard to believe that a bank like PNB would not audit its SWIFT messages regularly. Or its auditors. Or RBI.)

On the face of it, it looks like the ex-employee is being used as a scapegoat. It’s likely that a lot of people were in on this thing. And that it generated massive, fat fees for PNB all these years.

Fees wise: Imagine 11,000 cr. worth LoUs being renewed each year – that’s upto Rs. 200 cr. in fees that was all hitting PNB’s top line. You could bribe an employee to maybe give you a small increase – say 10-20 cr. but when you hit numbers like 11,000 cr. this is surely something the top management would know.

What’s the Scale of this scam?

While PNB reported it as a 11,000 cr. scam, they filed an FIR with the CBI for only Rs. 280 cr. This has probably expanded since then but even if the total outstanding is as much as that, there’s a good chance that the actual loss amount will be lesser.

All of it will be borne by PNB right now. Whether someone abused their SWIFT usage is not relevant, if PNB’s SWIFT message said they will pay, they have to pay if there is a default.

But think about the fallout. The problem was that some liabilities were not in the system. There could be more such LoUs. From the same branch or others. Other banks could have such LoUs too. It’s trivial to start looking – and we know that Nirav Modi will not be an isolated case.

Also, the issue was that the limits had no collateral behind them. If all banks are told to verify their non-fund-based limits and demand collateral against them (say at least 25%) then the scale would be absolutely massive. It’s not like this is happening only with Nirav Modi or Choksi. A very large number of importers of commodities have been doing this, and rotating credit. A change in regulation here can change the game dramatically for every other bank (and import account) in the system.

The simple point: this particular transaction will result in a lower loss than 11,000 cr. for PNB. Because of recoveries and such. But if RBI asks all banks to pull up collateral on such lending and stop such practices, the scale is many times larger.

What about the PNB stock?

It’s fallen 17%. But note that it already has 60,000 cr. of gross NPAs. Another 11,000 cr. will hurt it but not kill it. It won’t die – the government will take it over. Shareholders might suffer, but come on as a shareholder of a public sector bank you’re used to suffering.

The problem really is: There is never just one cockroach. When you go deeper, you are likely to find more dirty, dark secrets, and none of them will be any good.

PNB is gonna hurt for a while, but so are others who will find their books similarly tarnished once they investigate.

Will This Bring The Market Down?

Have you been living under a rock? Nothing will ever bring the market down, nowadays.

But the one thing that does bring markets down is the outflow of liquidity. What if so much of the “ponzi” credit – essentially money that was rolled over very month – is being invested directly, or indirectly, into stocks? If RBI tightens up, liquidity will pull money out of stocks, and that will hurt.

Of course, this hurts the fiscal deficit since PNB has to be rescued. So bond yields are up to 7.6% and therefore we’d avoid any long term funds or bonds. Short term it will have to be.

But overall, we wouldn’t worry too much. Just react, don’t predict. What would you do if stocks fell? Better to answer that than to say they will, or they will not.

(And no, not buying PNB)

Our View: Fix it.

This is the Indian public sector banking system. Fix it.

How can you have transactions on SWIFT outside CBS? Fix it.

Why would you not reconcile the nostro accounts? Suspend the auditors. Fire top management. Fix it.

Closing the door behind Modi, who’s already left the country, is probably useless. If you find fraud,  invoke their personal guarantees, and file cases to attach their personal properties. After that, file in NCLT to make these companies insolvent. Take the hit, and try to recover.

Find out more such instances where collateral cover is too low. Find out if the LoUs or LCs are just getting rolled over or is the customer actually paying back through the Indian current account. And if not, demand more collateral to avoid further spread of the ponzi.

But this is quite unlikely to happen because the banking system is going to take massive hits now, and we’re going to have to deal with the fallout of really horrible systems. It’s amazing that our banks have been this lax, but they have been allowed to; with no bankers being investigated, the rot inside the banks has been ignored and instead, industrialists have been the target of outrage. It’s time to look at banks as malicious players too, and to fix that rot.

Sunday, March 26, 2017

Jyotilinga Shlok

मुझे आज भी यह सब याद है क्यों कि रोज सवेरे माँ को यह जपते सुनता रहा
जब तक वह रहीं या मैं  उनके साथ रहा

सौराष्ट्रे सोमनाथे श्रीशैले मल्लिकार्जुनम।
उज्जयिन्याम महाकालं - ओमकारं - अमलेश्वरं ॥

परल्याम वैद्यनाथं च  डाकिन्याम भीमशंकरं ।
सेतुबन्धे तु रामेशं नागेशं  दारुकावने ॥

वाराणस्यां तु विश्वेशं त्रयम्बकं  गोमतीतटे ।
हिमालये तु केदारं घुश्मेशं शिवालये ॥

एतानि  ज्योतिलिंगानि सायंप्राति पठेन्नर: । 
सप्तजन्मकृतं पापं स्मरणं विनश्यति ॥

एतेशां दर्शनादेव पातकं नैव तिष्ठति । 
कर्मक्षयो भवेत्तस्य यस्य  तुष्टो महेश्वराः ॥ 

Thursday, February 2, 2017

buy Stock in Indian Market

here is my reco list

1 YES BANK
2 Infosys
3 HCL Tech
4 LIC Housing Fin
5 Reliance
6 Tata Motors
7 IGL
8 NTPC
9 Ashok Leyland
10 Zee Entertain

Wednesday, February 1, 2017

my favourite quotes



1. “A goal is not always meant to be reached, it often serves simply as something to aim at.” – Bruce Lee


2. “You are never too old to set a new goal or to dream a new dream.” - C.S. Lewis

3. “Never give up. Today is hard, tomorrow will be worse, but the day after tomorrow will be sunshine.” - Jack Ma

4. “You don’t learn to walk by following the rules. You learn by doing, and falling over.” - Richard Branson

5. “The people who are crazy enough to think they can change the world are the ones who do.” - Steve Jobs

6. “If something is important enough, even if the odds are against you, you should still do it.” - Elon Musk

7. "If you're not stubborn, you'll give up on experiments too soon. And if you're not flexible, you'll pound your head against the wall and you won't see a different solution to a problem you're trying to solve." - Jeff Bezos

8. "The question I ask myself like almost every day is, 'Am I doing the most important thing I could be doing?'” - Mark Zuckerberg

9. “You can always find a solution if you try hard enough.” - Lori Greiner

10. “A goal without a timeline is just a dream.” - Robert Herjavec

11. “If you want to live a happy life, tie it to a goal, not to people or things.” - Albert Einstein

12. “I have not failed. I’ve just found 10,000 ways that won’t work.” - Thomas Edison

Tuesday, November 8, 2016

Bee on a flower


An 8 years old photo, wonder how they fly.  I fondly remember those days, when the photographing opportunities were in abundance..



Bee on a flower, originally uploaded by s_raghu20.
It was a photo hunting day for me, June in Switzerland, perfect time of year for spring photography.

And I found this bunch of bees hovering around this plant full of budding flowers. The colors from the flowers were great as it is, and the bees were giving the shots another dimension.

First time for me to shoot insects, though not in the macro sense. Still, I liked it very very much.

Saturday, July 23, 2016

Links to free big-data-sets



Many people who are starting their journey with big data and analytics find it hard to get their hands on the right kind of data to play or experiment with.

Most of the time, people have enthusiasm, they are learning the skill too, but they just don't have the right kind of dataset to apply their newly acquired skills.

Democratising data has been at the forefront of discussions for many data pioneers. Through their efforts and with some re-alignment of technology priorities, some government bodies have opened up their datasets to the public.

As a result, here is a set of links (reproduced) to some of the free sources.
  1. Data.gov http://data.gov The US Government pledged last year to make all government data available freely online. This site is the first stage and acts as a portal to all sorts of amazing information on everything from climate to crime. 
  2. US Census Bureau http://www.census.gov/data.html A wealth of information on the lives of US citizens covering population data, geographic data and education. 
  3. Socrata is another interesting place to explore government-related data, with some visualisation tools built-in. 
  4. European Union Open Data Portal http://open-data.europa.eu/en/data/ As the above, but based on data from European Union institutions. 
  5. Data.gov.uk http://data.gov.uk/ Data from the UK Government, including the British National Bibliography – metadata on all UK books and publications since 1950. 
  6. Canada Open Data is a pilot project with many government and geospatial datasets. 
  7. Datacatalogs.org offers open government data from US, EU, Canada, CKAN, and more. 
  8. The CIA World Factbook https://www.cia.gov/library/publications/the-world-factbook/Information on history, population, economy, government, infrastructure and military of 267 countries. 
  9. Healthdata.gov https://www.healthdata.gov/ 125 years of US healthcare data including claim-level Medicare data, epidemiology and population statistics. 
  10. NHS Health and Social Care Information Centre http://www.hscic.gov.uk/home Health data sets from the UK National Health Service. 
  11. UNICEF offers statistics on the situation of women and children worldwide. 
  12. World Health Organization offers world hunger, health, and disease statistics. 
  13. Amazon Web Services public datasets http://aws.amazon.com/datasets Huge resource of public data, including the 1000 Genome Project, an attempt to build the most comprehensive database of human genetic information and NASA ’s database of satellite imagery of Earth. 
  14. Facebook FB +0.32% Graph https://developers.facebook.com/docs/graph-api Although much of the information on users’ Facebook profile is private, a lot isn’t – Facebook provide the Graph API as a way of querying the huge amount of information that its users are happy to share with the world (or can’t hide because they haven’t worked out how the privacy settings work). 
  15. Face.com: A fascinating tool for facial recognition data. 
  16. UCLA makes some of the data from its courses public. 
  17. Data Market is a place to check out data related to economics, healthcare, food and agriculture, and the automotive industry. 
  18. Google Public data explorer includes data from world development indicators, OECD, and human development indicators, mostly related to economics data and the world. 
  19. Junar is a data scraping service that also includes data feeds. 
  20. Buzzdata is a social data sharing service that allows you to upload your own data and connect with others who are uploading their data. 
  21. Gapminder http://www.gapminder.org/data/ Compilation of data from sources including the World Health Organization and World Bank covering economic, medical and social statistics from around the world. 
  22. Google GOOGL +0.66% Trends http://www.google.com/trends/explore Statistics on search volume (as a proportion of total search) for any given term, since 2004. 
  23. Google Finance https://www.google.com/finance 40 years’ worth of stock market data, updated in real time. 
  24. Google Books Ngrams http://storage.googleapis.com/books/ngrams/books/datasetsv2.htmlSearch and analyze the full text of any of the millions of books digitised as part of the Google Books project. 
  25. National Climatic Data Center http://www.ncdc.noaa.gov/data-access/quick-links#loc-clim Huge collection of environmental, meteorological and climate data sets from the US National Climatic Data Center. The world’s largest archive of weather data. 
  26. DBPedia http://wiki.dbpedia.org Wikipedia is comprised of millions of pieces of data, structured and unstructured on every subject under the sun. DBPedia is an ambitious project to catalogue and create a public, freely distributable database allowing anyone to analyze this data. 
  27. New York Times http://developer.nytimes.com/docs  Searchable, indexed archive of news articles going back to 1851. 
  28. Freebase http://www.freebase.com/ A community-compiled database of structured data about people, places and things, with over 45 million entries. 
  29. Million Song Data Set http://aws.amazon.com/datasets/6468931156960467 Metadata on over a million songs and pieces of music. Part of Amazon Web Services. 
  30. UCI Machine Learning Repository is a dataset specifically pre-processed for machine learning. 
  31. Financial Data Finder at OSU offers a large catalog of financial data sets. 
  32. Pew Research Center offers its raw data from its fascinating research into American life. 
  33. The BROAD Institute offers a number of cancer-related datasets. 

Credit to Forbes article at

http://www.forbes.com/sites/bernardmarr/2016/02/12/big-data-35-brilliant-and-free-data-sources-for-2016/#5b2a54cf6796

Friday, June 19, 2015

Teradata Data type abbreviation - described

Teradata data types (as reported in DBC.Columns.ColumnType can be cryptic and not always easy to remember.  Here's a ready reckoner - 

Abbreviation
Equivalent English :)
A1
ARRAY  
AN
MULTI-DIMENSIONAL ARRAY 
AT
TIME  
BF
BYTE  
BO
BLOB  
BV
VARBYTE  
CF
CHARACTER  
CO
CLOB  
CV
VARCHAR  
D
DECIMAL  
DA
DATE  
DH
INTERVAL DAY TO HOUR
DM
INTERVAL DAY TO MINUTE
DS
INTERVAL DAY TO SECOND
DY
INTERVAL DAY 
F
FLOAT  
HM
INTERVAL HOUR TO MINUTE
HS
INTERVAL HOUR TO SECOND
HR
INTERVAL HOUR 
I
INTEGER  
I1
BYTEINT  
I2
SMALLINT  
I8
BIGINT  
JN
JSON  
MI
INTERVAL MINUTE 
MO
INTERVAL MONTH 
MS
INTERVAL MINUTE TO SECOND
N
NUMBER  
PD
PERIOD(DATE)  
PM
PERIOD(TIMESTAMP WITH TIME ZONE)
PS
PERIOD(TIMESTAMP)  
PT
PERIOD(TIME)  
PZ
PERIOD(TIME WITH TIME ZONE)
SC
INTERVAL SECOND 
SZ
TIMESTAMP WITH TIME ZONE
TS
TIMESTAMP   
TZ
TIME WITH TIME ZONE
UT
UDT Type 
XM
XML  
YM
INTERVAL YEAR TO MONTH
YR
INTERVAL YEAR 
=++
TD_ANYTYPE