Archive for October, 2008

Is Catch-22 Coming True?

Tuesday, October 21st, 2008

Like most Catch-22 readers, I found its characters hilarious and their ploys convincing but never imagined that I’d come across their likeness in real-life.

Well, I’m now not so sure.

Charles Tyrwhitt seems like Chief White Halfoat in reverse.

COBRA could be as much in demand as Colonel Cargill’s services.

Instead of following balance sheets with bloated CDOs as alarm bells for the next bank stock that’s going down, FORTUNE magazine advises us to watch the movements of British shirt seller Charles Tyrwhitt. It appears that, wherever Charles Tyrwhitt goes, banks close! It has shops in two New York City locations – one at 377 Madison Avenue, in what was once Bear Stearns’ headquarters, and the other at 745 Seventh Avenue, in the Lehman Brothers building.


In Catch-22, every place Chief White Halfoat pitched his tent, oil companies would sink a well. Every time they sank a well, they hit oil. And every time they hit oil, they made him pack up his tent and go someplace else.

Over a period of time, oil companies could guess where Chief White Halfoat was going to stop next so that they could begin drilling even before he got there. For its readers, FORTUNE takes the guesswork out by revealing where Charles Tyrwhitt plans to open its next store (Lime Street in London, near Lloyd’s).

Inspired by financial instruments named after animals like Lehman’s LION and TIGER from Merrill Lynch, Rajrishi Singhal suggests some more in a recent Economic Times column. Named COBRA – or Collateral Based on Reducing Assets – this instrument might become popular with profitable companies seeking losses for claiming tax breaks.

Remember Colonel Cargill in Catch-22? Before joining the army, he was a marketing executive. He was such a lousy marketing executive that he could run the most prosperous of businesses aground and deliver losses which could be used for claiming tax breaks. He enjoyed high prices and massive demand, for “failure often did not come easily”.

After Chief White Halfoat and Colonel Cargill, I wonder if we’re going to encounter the likeness of Milo Minderbinder anytime soon. Despite buying eggs at seven cents apiece in Malta and selling them to his mess hall at five cents apiece, he was able to convince his syndicate that he was making a profit of around three and a quarter cents apiece. He kept them happy by promising that “everyone had a share”.

Or, from the recent turmoil in the financial industry, can we infer that a number of Milo-like characters have already been at work in the last few years?

No Risk No Gain

Monday, October 20th, 2008

I think it was HP that came up with T-Shirts in the early ’80s with the slogan “No RISC No Gain” to advertise the superior power of its then newly-launched RISC processor (as compared to the then-widespread CISC technology) while admitting at the same time to the relative untested nature of this technology.

While the risk-return dilemma is much older than this slogan, it has reared its head once again in the aftermath of the recent turmoil in the global financial industry. Do we conclude that the events of the last few weeks indicate a total breakdown of risk management practices used – or not used – by banks and financial institutions? Or, instead, were they the outcome of flawed interpretation of risk-return analyses? Or something else… ?

Before the pendulum swings to the other extreme, we need to strike the middle-ground and explore ways to apply risk management principles optimally. In doing so, we might want to keep in mind Intel’s onetime CEO Andy Grove’s lament a few years ago that the raft of legislation that came in the wake of Enron and Worldcom scandals had the unfortunate effect of forcing Corporate America to completely abandon risk-taking, which is a vital ingredient to succeed in business.

To understand what constitutes a middle-ground, let’s take the example of credit card fraud control. Fraud leads to losses, so it’s natural for credit card companies to devise mechanisms to control it to the utmost extent possible. Many automated systems exist for this purpose including some advanced ones that use predictive analytics to predict fraud in real time. (Interested readers can click here to download my recent article on this subject). However, none of these systems is 100% accurate – a certain level of “false positives” cannot be ruled out where the fraud control system scores a genuine transaction as fraud and rejects it. Apart from loss of revenue from the rejected transaction, false-positives can lead to customer dissatisfaction, and in the worst case, drive the customer to stop using the credit card altogether. As a result, credit card companies seek ways to minimize false-positives by striking a middle-ground via manual overrides and by disabling some of the screening rules during peak shopping seasons.

To get a feel of how to apply risk management principles optimally, let us consider another example viz. the risk of bad debts. Every company that sells a product or service faces the risk that some of its accounts receivable may turn into bad debts. There are various ways to mitigate this risk including factoring, customer credit rating, collecting payments in advance, and so on. While 100% advance assures 0% bad debit, very few companies will be able to get away with it even if they offer heavy cash discounts. Other companies seeking to use advance as mitigation against the risk of bad debt have to be mindful of the counter-risk of losing business from some customers who used to enjoy credit in the past and are not willing to pay higher advance. This suggests that there’s an optimum level for advance beyond which lost revenues can outstrip reduced levels of bad debt loss.

This optimum level can vary from one company to another, and, even for a given company, from one line of business to another. To arrive at the optimum level, let’s plot two sample profiles of bad debt and revenue losses against varying levels of advance. The intersection point between the two lines (or curves in more complex cases) yields the optimum level of advance.

The company in CASE 1 is able to get away with higher advance …

… whereas the one in CASE 2 is unable to demand much advance.

Therefore, it’s necessary to apply risk management principles optimally in order to mitigate bad debt losses.

As these examples illustrate, management of business risk requires striking a middle-ground. Finding that middle-ground is the real challenge going forward. 

Bill Me Later’s Instant Credit Decision

Tuesday, October 14th, 2008

Earlier this year, I’d written about The Everlasting One Minute it took for CapitalOne to process my credit card application in the UK.

In sharp contrast, Bill Me Later promises – and delivers – credit approval or denial instantly.

Bill Me Later is a convenient and secure way to pay on the web or over the phone. Bill Me Later lets users pay without using a credit card. At checkout, users select Bill Me Later as the payment method, simply provide their date of birth and the last four digits of their social security number, and Bill Me Later gets back within a few seconds with an approval or denial decision.

When I tried Bill Me Later recently, it came back with a decision (denial!) truly within a few seconds – there was no “we’ll get back to you in the coming days” sort of response that I’d received from CapitalOne. And, when my sister in the US tried it today, the acceptance decision happened in less than a minute.

Last week, Bill Me Later was acquired by eBay for close to 1 billion USD. Apart from several advantages it offers compared to paying by credit and debit cards, Bill Me Later’s powerful set of credit decisioning algorithms that allow it to take a credit approval / denial decision within a few seconds, and reportedly at lower default rates compared to credit cards, is cited as the main reason for its rather steep valuation.

On the face of it, there seems to be no difference between Bill Me Later’s real-time credit decision and “instant approval” being advertised by a few credit card issuers in the US. Given my experience with CapitalOne in the UK, I am inclined to be a bit skeptical about claims for instant credit card approvals. However, my experience with CapitalOne was in the UK. Whereas, both Bill Me Later and most instant approval credit cards are only available in the US. Given that things can operate quite differently between the US and the UK, I decided to probe a little deeper.

Fact is, in the case of instant approval credit cards, the credit card company does convey an acceptance or denial decision instantly. This is a great improvement over my experience with CapitalOne in the UK.

However – and here lies the fundamental difference between Bill Me Later and instant-approval credit cards – the credit card itself arrives only after 7-10 days, and you can do your shopping only afterwards. On the other hand, with Bill Me Later, you can achieve “instant gratification”: your shopping is complete as soon as Bill Me Later approves your application within a few seconds.


Why Indian Marriages Last Longer

Thursday, October 9th, 2008

Compatibility is an important ingredient in any relationship, including marriage. At the same time, opposites are supposed to attract. How do we reconcile these seemingly contradictory notions, especially in marriage?

I recently read that, of all types of compatibility, compatibility in financial priorities is vital for marital success. That is, the views of a husband and wife on financial priorities must be quite similar, if a marriage is expected to last long. It appears that a marriage can survive incompatibilities on several grounds as long as the husband and wife are compatible on their financial priorities.

In the developed world where basic necessities have been taken for granted for over a century, marital relationships faced decisions of financial priorities at a higher level. This probably led to growing incompatibility of financial priorities between husbands and wives e.g. brand new furniture for the home versus college education for a child. 

Contrast this with developing countries like India where fundamental financial priority for virtually everyone –  especially those born in and around the Independence in 1947 and a couple of decades afterwards – has been the fulfillment of basic necessities. Since most people couldn’t afford to have any higher level of financial priorities, compatibility in financial priorities between a husband and wife has been automatically assured, which virtually guaranteed longevity of a typical Indian marriage.