Tuesday, May 12, 2015

How the sub-prime crisis unfolded

This book review was first published in The Hindu on May 03, 2015

Easy Money — The Greatest Ponzi Scheme Ever and How it is Set to Destroy the Global Financial System: Vivek Kaul
Sage Publications India Pvt. Ltd.
B 1/I-1, Mohan Cooperative Industrial Area,
Mathura Road, New Delhi-110044
Rs 395

The first and second books in the trilogy traced the evolution of money through the era of commodities and even people being treated as money, the rise of gold as money during the World War II, the role of oil, the Gulf War and the dotcom bubble burst.

Kaul continues the journey in this concluding book in the trilogy. The beauty of this trilogy is that it makes boring interesting. The third book is the icing.

After the dotcom bubble burst, one would have thought that central bankers across the world would have learnt from the mistakes of Alan Greenspan, the then [2001] Chairman of the US Federal Reserve. Alas, that was not to be. Greenspan himself continued with the “easy money policy that had created the dot-com bubble...The low interest rate regime created conditions that were ideal for a bubble; the only difference this time around was that real estate replaced stocks as the medium of speculation”. With these lines, Kaul eases into the post dotcom bubble burst era of the building up of the sub-prime crisis.

Greenspan’s warning

The startling revelation is that, just like Greenspan had warned people of the U.S. of “Irrational Exuberance” much before the dotcom bubble burst, he did talk about the housing boom way back in 2002 and that it “cannot continue indefinitely”. But as Kaul points out, he chose to do nothing about it. In fact, the easy money policy continued.

In fact, after the sub-prime crisis, Ben Bernake (Chairman of the US Federal Reserve) continued his predecessor Greenspan’s policy of easy money.

The chapter, “Some are more equal than the others” very interestingly analyses the growing income inequality and ‘borrowing substituting rising incomes’. The book follows on to explain, very rationally, the entire cycle of Chinese exports to the U.S., the consumption of goods by Americans on borrowed money, fuelled by low interest rates, and hence more demand, more exports by China, investing of the earned dollars by China in U.S. financial securities! This cycle kept the interest rates in the U.S. low and at the same time financed the U.S. budget deficit.

While low interest rates were enticing enough to borrow, the financial institutions came up with innovative ideas like 2/28 option ARMs, liar loans, easy lending terms and general thrust to lend to the sub-prime borrowers, resulting in the household debt being 140% of the household income before the sub-prime crisis hit.

How did this happen? Surely someone would have realised the huge risk to the system with such unsustainable debt levels? Why didn’t the banks act earlier? What rocked the party? Why did people start defaulting? In the midst of the crisis, why did the CEO of Citibank get a $95 million severance package when he was quitting? Read the book to know about all these interesting aspects of the crisis. September 15, 2008, the date Lehman Brothers filed for bankruptcy is the date the global markets went haywire and sub-prime crisis became the talk of the global financial system. The foundations of the crisis were laid decades ago, depending on how far back into the history one would want to go. However, how much ever far back one would like to go, there is provision for doing so in this trilogy. Just decide the period and start reading from then on.

Earlier, in Japan

What happened in the U.S. in the 2000s had already happened in Japan in the 1990s. What is needed is that a country not only learns from the mistakes of its own past, but also learns from the mistakes of other nations. This would be very relevant for emerging countries as they would start going through the phases which some of the developed nations have already gone through.

Lastly, it is worth pointing out that the research in the book is up-to-date with references to Thomas Piketty’s, “Capital in the Twenty-First Century”, published in 2014. Events in the global financial system till the first half of 2014 have been comprehensively covered by the book.

In the introduction, Kaul writes that he feels “the best books on the current financial crisis are yet to be written. They will probably start to get published around 2033 (25 years after the current crisis started)”. But till then, for the next 18 years, this might be the best account of the what, why and how of the crisis!


The foundations of the crisis were laid decades ago, depending on how far back into history one would want to go

Friday, May 1, 2015

Health Insurance Hospital Registry

This article was first published in the IIB Bulletin, Vol 1, Issue 4: Co-Author: Varsha, GS1 India
https://iib.gov.in/IIB/Articles/IIB%20Bulletin%20Q4%202014-15.pdf

Poor data impacts many areas in the healthcare system. One of the areas that has an impact on Healthcare Analytics is the way hospitals are identified and stored in the various databases. In the case of the Insurance Industry, each Insurer has their own naming convention for Hospitals. For example, Table 1 shows that five different Insurers can name the same hospital in 5 different ways in their databases.

Table 1
Database A
Database B
Database C
Database D
Database E
ABC Hospital

The ABC Hospital & Emergency Services
ABC Hospitals Pvt. Ltd
ABC Hospital Group
ABC Group of Hospitals

In the above illustration one cannot be certain if all the names are referring to the same entity or if they are all different entities, without painstaking manual intervention. Using the list as it is would not give a clear picture of the number of claims, average claims, top diseases in a period in a particular Hospital, total insurance claims paid per Insurer to the hospital, and many more such statistics.

To overcome this issue it is recommended to identify each entity (hospital) with a standard and unique number. Think of it as a mailing address: an identifier for a single location in the world that is globally unique to that location. No other organization, agency, or affiliate can use it to identify their locations, but all parties can and should use it to identify that location.

The Standard adopted globally to identify a location using a unique and unambiguous number is a GS1 Global Location Numbers (GLNs) based on the GS1 System of Standards. Utilizing a GLN can help improve data integrity. In turn, it will help reduce cost and time spent on data cleaning and making it more reliable.

Such a system enables global and unique identification of products and locations, as well as the continuous, automatic update (i.e., synchronizing) of standardized information across all stakeholders. Unique identification provide the necessary foundation for achieving the best results when using complementary applications like automatic data capture, e-commerce, electronic record management, etc.

Insurance Information Bureau of India has undertaken a project to identify each Hospital in the Health Insurance Providers Network. GS1 India would allocate a GLN to each hospital, which is a unique, 13-digit number for a specific location. Implementing GLNs simplifies the exchange of information and provides the opportunity to manage accurate and authenticated data more effectively.

The GLN, or the globally unique ID would not only identify a specific location, but also provide the link to the information pertaining to it (i.e., a database holding the GLN attributes such as postal address and GPS co-ordinates of the location, services offered at that location, key contact person at that location etc.). This is a key advantage of using a globally unique identifier because all information can be held and maintained centrally in a database or registry reducing the effort required to maintain and communicate information between multiple parties on a national or global basis.

This enables various stakeholders to simply reference a GLN in communications, as opposed to manually entering all of the necessary party/location information. Using a GLN to reference party/location information promotes efficiency, precision and accuracy in communicating and sharing location information.

Figure 1

Several countries like UK, Australia, Austria, North America etc. use GLN’s in their procurement processes to enable efficiency and transparency to deliver better patient care.

The use of GLNs provides a method of identifying locations that are:

·         Unique: with a simple structure, facilitating processing and transmission of data;
·         Multi sectoral: the non-significant characteristic of the GLN allows any location to be identified - regardless of its activity
·         International: location numbers are unique worldwide.

By identifying hospitals with GLNs enables interoperability with other GS1 Healthcare Registries in the world, building global visibility of Indian healthcare facilities, services and capabilities for international patients

However, the most immediate impact of the Unique Identification would be on the quality of Analytics. Only when hospitals are properly identified, logged and data generated on health aspects from them are reliable, can any meaningful analysis be carried out. A list of unique hospitals will be beneficial to Hospitals, Insurers, Govt. Agencies and also the Public.
·         Claims payment can be accelerated
·         Fast, reliable and relevant Analytics
·         Geography based trends, patterns of disease occurrence, cost patterns, etc.
·         Footprint is visible
·         Will aid in the Fraud Analytics efforts of IRDAI

Ministry of Health and Family Welfare is working on standardizing treatment procedures and costing templates. Efforts are being made by IRDAI-FICCI to categorize hospitals. Unique Hospitals would complement all of these projects as well.

A simple illustration may be seen in Table 2 where the outlier analysis throws out more meaningful results when the hospital is correctly identified.

Table 2

Cost of treatment for Disease type Cholera

Database A
Database B
Database C
Database D
Database E

ABC Hospital
The ABC Hospital & Emergency Services
ABC Hospitals Pvt. Ltd
ABC Hospital Group
ABC Group of Hospitals
Claim Paid 1
16,016
2,093
33,115
24,299
39,113
Claim Paid 2
16,577
27,929
22,919
19,366
26,343
Claim Paid 3
12,122
23,767
30,916
29,279
26,000
Claim Paid 4
16,134
25,958
31,108
21,147
15,500
Claim Paid 5
10,280
15,981
1,99,400
26,828
25,000
Average claim paid per hospital
             14,226
                      19,146
              63,492
            24,184
              26,391
Overall Average claim paid
29,488




Highlight Outliers where Claim paid or amount claimed is above/below +/- 50% of the average for the hospital

Database A
Database B
Database C
Database D
Database E

ABC Hospital
The ABC Hospital & Emergency Services
ABC Hospitals Pvt. Ltd
ABC Hospital Group
ABC Group of Hospitals
Claim Paid 1
-
Outlier
Outlier
-
-
Claim Paid 2
-
 -
Outlier
-
-
Claim Paid 3
-
-
Outlier
-
-
Claim Paid 4
-
-
Outlier
-
-
Claim Paid 5
-
-
Outlier
-
-
If the Hospital is identified as the same hospital in all databases, the average claim paid will be Rs 29,488/- across all 25 claims.

Database A
Database B
Database C
Database D
Database E

ABC Hospital
ABC Hospital
ABC Hospital
ABC Hospital
ABC Hospital
Claim Paid 1
-
Outlier
-
-
-
Claim Paid 2
-
 -
-
-
-
Claim Paid 3
Outlier
-
-
-
-
Claim Paid 4
-
-
-
-
-
Claim Paid 5
Outlier

Outlier
-
-

Health Insurance vs Macro and Socio-Economic Indicators

This article was first published in the IIB Bulletin, Vol 1, Issue 4: Co-Author: Vishnu Vardhan, Syed Md. Ismail
https://iib.gov.in/IIB/Articles/IIB%20Bulletin%20Q4%202014-15.pdf




Inference
It may be inferred from the graph and the GLM analysis that higher the Literacy rate and the GDP per Capita, higher will be the Health Insurance Premiums in the State. In a research done at the Indian Institute of Management, Ahmedabad (Bhat & Jain, 2006) it was found that the purchase of health insurance is related to the awareness and knowledge about insurance and also the income of the household. Almost a decade later, our findings suggest the same.

Reference
Bhat, R. & Jain, N., “Factoring affecting the demand for insurance in a micro health insurance scheme”, Indian Institute of Management, Ahmedabad, Working Paper No. 2006-07-02, 29p