Coining Concepts

..where we coin concepts of money, economics and finance

Is Endowment Bias “a” reason for Corporate and Bank financial woes in India?

Is endowment bias a reason for financial woes in at least a few economies?

Last year, in mid-2016, the biggest ever fire sale of Indian corporate assets begun, to tide over bad loans crisis. Among the top 10 business houses of India, assets worth 200,000 crores of rupees were slated to be on sale, forced on the hit of whip by banks (source: Report, The Hindu). In case you are wondering over the whopping figure of the fire asset sale, it is the debt of over 500,000 crore INR which stands unpaid by these business houses which is being recovered.

It is not that the peril of holding on to dogs or pets (as defined by the Boston Consulting Group) is unknown to promoters. It is also not that the central bank hasn’t been vigilant. SDR (Strategic Debt Restructuring) has all means to transfer the ownership and divestiture rights to the bank to which the company owes the debt. But here lies a chain of deadlocks.

Does the bank need the ownership of a power, infrastructure or steel company? No. Does a bank possess the expertise to run an infrastructure company or to even sell it off to a buyer, capable enough to overturn the afflicted company? No. Then why would a bank encourage SDR? But then, the bank has to recover the unpaid loan. So what does a bank do? It depends on the defaulted owner or promoter to sell the stake to a potential buyer, filtered after a due diligence process headed or facilitated by the bank. Now what if the promoter is indisposed to sell the stake?

An investment banker recounts how last year he got a debt-ridden steel plant promoter to meet prospective buyers. During the course of the meeting, the promoter asked the investor to provide him with $300 million and he would sell his plant to a proxy and buy it back, making some money for the investor in the process. He told the investor that he does not intend to sell his steel plant and is attending the meeting only to please the bankers.

Such instances are frequent and the RBI has sent out a warning to banks to be careful of such deals or round-tripping. Simply stated, the stalemate is this: Banks are obsessed to sell, promoters are coy for selling.

One straight question at this stage: What explains this unwillingness? Why are the valuations mostly not appealing for an acquisition to the promoter? As promoters, are most of us like the girl selling a lemonade business for $100 million? ūüôā (Please see the picture at the top.)

In behavioral economics, the endowment effect is the hypothesis that people ascribe more value to things merely because they own them. We prevent losing possession than we prefer gaining possession. The WTP (Willingness to Pay) for gaining is much less than the WTA (Willingness to Accept) the price paid to us for parting ownership. There is a plethora of experimental evidence and graphical elucidations to it.


Could this be the reason for which promoters choose sitting over ballooning liabilities and bloating losses?

If YES, we need to appreciate some implications and its aftermath:

1.      Endowment bias leads to Divestiture Aversion. BCG matrix suggests Dogs to be divested. Divestiture aversion is going to have more dogs in the field than cash cows: not something we wish!

2.      Divestiture aversion is likely to leave the economy wanting of investment. The present Indian economy is a great laboratory to witness this. In fact, divesting and investing could be looked upon as two sides of the same coin (just like buying and selling). You cannot have a liquid market with only one of these!

3.      Divestiture aversion eventually engenders FIRE SALE of corporate assets as the last resort. Fire sale neither fetches a good price for promoters, nor does it help the banks to recover debt.  The 2016 Fire dale has expected fetch of 2 lakh crores INR whereas the outstanding debt was more than 5 lakh crores INR.

4.      As Dr. Raghuram Rajan presents in his research (with evidence from the Great depression of 1929), fire sale of financial assets triggers a contagion where fire sale of real assets ensues, followed by plummeting prices of real assets, sinking values of collateral and floundering of financial institutions.

So, what is the way out? I guess nothing in the short run except to have RBI continue its strict procedures.

However, in long run, while we fix and place other nuts and bolts of the new economic regime, ENDOWMENT BIAS needs to be ATTENDED. Whether the key is to undo endowment bias through altering the industry organization to cut down company sizes or to accept endowment effect and mitigate its aftermath using brute force, is another, and possibly a complex debate.

As a positive signal, the divestiture aversion seems to be less in the inchoate start-up ecosystem. This should be one concrete reason to hail start-ups and see if we can take away some lessons to clean the malaise in other sectors. ūüôā

What does the BILL mean to Indian service companies? – The BILL Assessment Framework

We, the Indian service industry, have been hit with the news of a BILL proposing a crackdown on US immigration. It should not be an exaggeration to state that the Indian services sector has been despondent with the news and people are vexed enough. To start exploring new ‚Äėgeographies‚Äô is the lookout of IT bellwether companies.

Companies in the industry and wedged employees (aka individuals of the society) are busy salvaging reputation. Some may even be found rescuing their existence shortly. Geographical dependency is a genuine offshoot of globalization. Nevertheless, if just a BILL can send a nationwide sizeable 108 billion USD industry into shambles, as an industry, we have definitely missed something. This framework is an attempt to locate that missing aspect, and alas, the framework also happens to be called ‚ÄúThe BILL Assessment Model‚ÄĚ!

Simply put, a service business aims at serving a customer need. As a service business, your success is a proxy of how well and how much you are fulfilling the customer demand. To illustrate this, we build a continuum.


The X axis denotes the business offerings of the service providers and Y axis the customer expectations. For a given magnitude and quality of customer expectations, you are either a relatively red company offering less, or a relatively green company offering at par with expectations. Though there are buzzwords with essence of delivering beyond expectations viz. process improvements, thought leadership, lateral thinking and business orientation flashed in company’s press releases and grandiloquent addresses of executives to employees, the ambit of those is very limited AND THE THEME VERY DIFFERENT as we shall see ahead in the article.

The reds are running to match the greens, investing in learning, pressurizing their employees to belabor on every opportunity to bedazzle the client representatives in umpteen ways and by following stringent yardsticks for appraising their employees hoping that a conflation of these steps would bring them close to the greens. Such efforts are logical and laudable indeed. A multitude of companies are in this constant cut-throat healthy competition. The description should sound familiar to those working in Indian technology outsourcing companies.

However, while there is no stone left unturned for reaching greener pastures, there is still an ‚Äėother side‚Äô unattended where the grass is even greener. To understand that side, we re-draw the graph, but with a difference.


Limiting our view till the two white boxes, on the X axis, the Business Offering axis, at an instant, a company may have some offerings in its bouquet which are indicated by YES on X-axis. There could be some services, which may not be in its spectrum which are indicated by NO zone on the graph. As we move right, we see a company is expanding its bouquet of services to reach customer expectations. The continuum, just like the first graphical illustration, is progressive. A company positioned right has more services to offer (and is relatively green) than one positioned on the left (which is relatively red).

To understand rest of the framework, we would need to shift gears and veer our opinions away from one assumption: The only way to run a service business is to serve the customer.

Let me explain with an example. Facebook is a service consumed by millions at present.

Rewind back 14 years. We, as customers, had telephones, SMS facility and extremely cheap call rates to fulfill our needs, expectations and demands. We, as customers, did not ‚Äėask‚Äô any provider to develop a platform to connect with distant friends, share our thoughts, wish on occasions and publish our pictures to the public.

Was Facebook fulfilling any customer ‚Äúdemand‚ÄĚ then?

Facebook, Payments using smartphones, 3D secure transactions, WhatsApp calls, smartphones, Google search engine and many others could be quoted as instances of successful businesses where companies were NOT AT ALL trying to fulfil a customer need, which was acknowledged or touted as unfulfilled.

Recognizing this should make us appreciate a possibility, that the Y axis in the first graph is not complete. There could be two sections on Y-axis (which denotes customer expectations). As the graph demonstrates, YES section on Y-axis is the set of expectations, consciously demanded by the customer. NO section indicates needs or expectations, which, though eventually, would be embraced by customer when presented, there is less or no acknowledgement of these services at the moment. Continuing with our example of Facebook, the demand of Facebook in 2003-04 shall fall in NO section of customer expectation had this framework were used then.

Identifying these aspects of nature of demand and supply (offerings) categorizes the players in service industry into four sections.

The B category: The YES ‚Äď YES block

As discussed in early part of this article, a substantial chunk of Indian IT service companies has been busy in serving customers’ needs. Typical characteristics are:

  • We, as companies, take pride in calling ourselves Client ‚ÄúDRIVEN‚ÄĚ Organizations.
  • Customer centricity does not mean what it is as per the dictionary, in such companies. It is taken only as acceptance to working on weekends, tolerating arrogance from clients and other such non-professional practices on whims of the worshipped customer.
  • The company repository is awash with white papers, but no one refers to them as there is no need. This is because, companies are not knowledge or ideas driven. They are driven by customers‚Äô need. If the needs are mediocre, it is legitimate to settle with a mediocre service. In case bowing down literally can suffice for continuation of projects, why even care about knowledge.
  • Fresh ideas brought to table by employees are not entertained or evaluated. The culture carries a strong stench of repulsion towards freshness.
  • Continuation or discontinuation of ongoing services is totally on the behest of customer. It can choose to pick another service provider over us with no cost of shift. Reason for no cost: Customer needs someone to bow down and take orders. Whether company X or company Y, DOES IT MATTER?
  • Employees are not allowed to raise voice against racism (in case) done by client representatives.

For all the above reasons, based on the intent of service delivery, by virtue of nature of offering, it is absolutely logical to term this category of service providers as BEGGARS. B for BEGGARS. Yes, for all the wealth created and amassed by such companies running in billions, for all the employment provided to lakhs of people, and for all their labor, just by virtue of their ways of work, BEGGARS. The criterion does not take into account the size of the company. There could be big Bs and small ones, but B.. for BEGGARS.

BEGGARS are mostly found contemplating on how to outshine the other beggars and may constantly find themselves entangled into a red vs green comparison and competition. They may hire the best strategists but the red vs green war strategy is inferior for the sole reason that it’s a war between two BEGGARS. A much better strategy is to replace this strategy with a paradigm shift.



The I category: The NO ‚Äď NO block

‚ÄėI‚Äô here stands for INNOVATORS. And the criterion of defining innovators is not based on whether they allow Tees and Jeans in office for their employees, weather their founders are graduates of an ace school or are illiterate, whether employees enter the office on swings, slides, flying brooms or SLVs, whether they are hailed as cool companies by the public and whether they simply ‚Äúcall‚ÄĚ themselves as innovators.

Innovators are those who work in the NO-NO block: attempting to serve customer expectations which customers are not cognizant of. It is usually unlikely that such attempts are made by more than one company: even if they do, they cannot be replicas of each other.

As customers, we never asked for a search engine. Archie (Alan Emtage) sensed it. Google introduced the offering of Google Search at a large scale. As customers, we never asked for a social networking website. Facebook thought about it and delivered an offering. The world did not know of photocopying before Xerox introduced it. E-mail was an unthinkable way of free and immediate communication before 14-year-old Indian boy (Shiva Ayyadurai) made the world wake up to this new service. ATMs were never asked for, by the public. While the world was satisfied (or even delighted) with the branch banking service, someone worked in the NO-NO category to ‚Äúinnovate‚ÄĚ the concept of ATMs. While NASA was busy spending billions on Mars mission, the Indian Space Research Organization (ISRO) cleverly utilized the sling-shot mechanism to launch Mangalyaan.

In short, an innovator is working ONLY TO PROVIDE DISRUPTIVE SERVICES.

The categories of L: The YES ‚Äď NO and NO – YES blocks ‚Äď THE INTERPLAY OF INNOVATORS AND BEGGARS

I have illustrated all the above examples to underscore that the common trait of the hailed innovators is that they choose to start working in the NO-NO land. This distinction of YES-YES and NO-NO is crucial. These are the only two categories of companies based on the intent of service delivery.

The other two are transitory stages and companies do not remain permanently in any of the YES-NO and NO-YES categories.

A typical sequence in a service industry is this.

  1. While beggars are busy with serving the present expectations, the innovator comes in with a new offering after working hard for months/years.
  1. The customer may have inertia in acceptance of the new offering. The innovator is engaged in making his selling cogent to increase its customer base. At this stage, the innovator enters the YES-NO category to be a LEADER. Please note that in the graph, the service provider now has an offering (YES section), but the customer is still not sure accepting it wholeheartedly.
  1. A typical trait of this stage is that overtly or covertly, the INNOVATOR (Now a LEADER) is TELLING the customer how his offering would make the customer be at par with the market. The INNOVATOR is still not taking orders and IS NOT begging for an engagement. Do you notice the contrast between INNOVATOR and BEGGAR? I hope the naming now makes more sense.
  1. After some time, the customer base becomes significantly sizable. This time may be different for different products/ services. The beggar wakes to the surprise. While the innovator’s new offering, which is disruptive in nature, starts sweeping the market, it results into a market loss for the beggar. This can be illustrated with examples.
  • Google search swept the market of books.
  • ISRO, launching 104 satellites in a single rocket provided a cost effective solution to launch satellites for nations across the world which was a potential loss to other space organizations providing similar but relatively expensive services.
  • WhatsApp call has reduced margins of telecom operators.
  • Introduction of ATMs took away business of banks (and associates) providing only branch services.
  • Introduction of cashless payment apps is taking away business of cash centric businesses (banks and associated technology consultants).

As this happens, the beggar enters the NO ‚Äď YES zone to be termed as a LAGGARD. This is because now it lags the market. The company is not offering this service (NO) but the market is expecting (YES).

  1. The LAGGARD pushes hard: learning programs are arranged, research teams are established, industry consultants are invited for seminars and trainings. Courses on latest practices are disseminated to employees. The ones who break apart to salvage company’s losing face are (said to be) rewarded. A sense of urgency is created to restore the relinquished position of the BEGGAR category or else the business stands at a high possibility of being pushed away from the market.
  1. Finally, things enter the YES-YES block since the once innovative offering becomes the customer expectation now. Temporarily, the one who started as INNOVATOR ends up working in the YES-YES zone. The BEGGAR, however, is happy that it could bring itself back to its perceived ‚ÄúNORMAL‚ÄĚ.
  1. The story doesn’t end here! The INNOVATOR doesn’t settle in the YES-YES zone. It is consistently thinking to outclass the customer expectations. He will find a way back to find a need which is not an expectation at present, will develop an offering for that potential expectation and will then sell it in a way that you and I be compelled to start expecting it.
  1. Meanwhile the BEGGAR will also grow, but due to changes in exchange rates, and due to some newly found masters who would glorify the beggar as a champion so that the beggar never wakes up to the truth of being a BEGGAR.

There are some takeaways from this framework:

  1. There is something over the customer’s perception. Customer is not the King. (This, however does not mean that customers should not be respected. J)
  1. As a BEGGAR, you are always competing with the Reds and Greens. There are No Red vs. Greens competitions in the INNOVATOR block. As an innovator, your competition is the customer itself. All you are busy with is to make logical guesses on what is the customer’s need which is also a potential demand? How do we shape this need to an expectation?

Who was Facebook’s competitor when the platform was being developed?

Shouldn’t be a brainer to guess no one. Hindrances are always there in establishing businesses as there are no free lunches in the world. But you do not see reds and greens and the resulting comparison / competition in case of Facebook and other examples quoted.

This one makes it even more tenable. Compared to successful launch by the Russian Space Agency launching 37 satellites in one go, Indian Space Research Organization (ISRO) became the first country to script history by launching 104 satellites in a single rocket. Had ISRO aimed at merely competing with peers, wouldn’t it have limited its goal to launch 38 or 40 satellites?

Now, please read the statement again: As an innovator, your competition is the customer itself. And think how would the head of a BEGGAR company respond to this.

I expect instant resentment. J How can you compete with the one whom you worship?

  1. As a team or as an individual, or a company under one leadership, you can either be a BEGGAR or be an INNOVATOR. YOU CANNOT CO-EXIST.

As a reader, you may wish to go back and skim through the attributes of a beggar mind and an innovator mind and see for yourself that coexistence of both is near to impossible. You can either dance to the customer’s tunes or compose a tune unheard by the customer and make the customer dance on it subsequently. Please note that as an innovator, you definitely need to be aware of what lies in YES zone on the Y axis and what lies in the NO zone. However, this awareness is disparate from positioning oneself as a beggar are completely disparate things.

Though you may have ‚Äúdisconnected‚ÄĚ teams or units under a brand with the beggar units providing for bread and butter for the innovators initially, it is unintelligible to expect an employee or a team to demonstrate a BEGGAR and INNOVATOR thinking simultaneously.

  1. INNOVATORS neither gripe for, nor are baffled or stupefied by bills demanding supply bans, sanctions or embargos. This is because bans, sanctions or embargos are not applicable if the customer is dependent on you, rather than you surviving on customer’s mercy.
  1. Last but not the least, an innovator can end up being a beggar if he makes a choice of being complacent of being an innovator in recent past. Self-disruption is the key to survival. The best example of this is Nokia: once an innovator, but smashed out of market (before being a LAGGARD for a short time) by another innovator since Nokia did not consciously take itself to the NO-NO zone from the YES-YES beggar zone.

Given this BILL assessment model, it is for you as a team, as a team leader or as a company to evaluate where are you placed. In case it seems difficult to locate yourself in the graph, there is hint. You can either be an INNOVATOR or a BEGGAR. In case you are sure you are not an innovator, it should be simple to make out the rest. ūüôā


Predictive Analytics: The Cat and the Washing Machine

As reported by most of the blogs and newsletters on technology and analytics, marquee banks achieve competitive advantage by leveraging analytics to improve decisions, enhance marketing, and influence consumer behavior. The availability of data and low-cost storage technology is creating an imperative for financial services companies to fully leverage and centralize analytics across their enterprise.

That is the popular belief. We live in that particular era of technology, where technology is considered capable enough to predict, with sufficient precision and accuracy, that which customer would need which banking product and when would he or she ask for it. Not only this, predictive analytics is also regarded so supple and nimble, that it could prompt offers for customers in real time which are potential enough to influence consumer behavior with immediate effect. In my view, we place such a tremendous amount of trust in data analytics that we miss to attend the classic paradox of PAST vs. FUTURE. Let us take a look at what the paradox is.

The core of predictive analytics

The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes. Putting it simply, one’s past data works as proxy for the expected outcomes of similar events in future. At this conjuncture, I ask a question: We toss an unbiased coin ten times, of which nine times the coin shows up its Head. The frequentist approach says that it is 90% plausible that the coin would fall on its tail when tossed next time. Does it mean we are 90% sure that the next toss would show up Heads? Does it mean that if a hundred coins are tossed, 90 of them are going to turn heads up? The answer is NO. Even though in the last ten tosses, the result was heads in 9 tosses, the eleventh toss is an independent event! Probability of getting a Head in a toss is still 50%. The reflection of probability in frequency of outcomes in equivalent events will be conspicuous only when the number of occurrences of the event approaches infinity.

Predictive analytics works analogous to the case discussed above. The input, more or less, to a predictive analytics system is the past data of all customers who enjoy banking services of debit cards, credit cards, deposits, loans etc. With application of predictive analytics on this past data, banks devise their customer relationship management strategies, including advertising and marketing campaigns; monitor customer buying habits, up sell and cross-sell initiatives; and attempt to build long-term customer loyalty, retention, customer screening and rewards programs.


Let us park the discussion and break from here. Things will converge as we connect the components of analysis one by one later.

The Cat and the Washing Machine

I am buying a concept of ‚ÄėThe Cat and the Washing Machine‚Äô from a book ‚Äď Antifragile, authored by renowned economist Nassim Nicholas Taleb. ¬†The concept posits boldly that based on behavior, systems could be classified into two categories. One is the Cat, comprising of animate, organic objects; other is the washing machine ‚Äď the inanimate ones. Washing machines – Inanimate‚ÄĒthat is, nonliving‚ÄĒmaterial, typically, when subjected to stress, either undergoes material fatigue or breaks. They are fragile. The house, the food processor, and the computer desk eventually wear down and don‚Äôt self-repair. However, typically, Cat – the natural‚ÄĒthe biological‚ÄĒis antifragile, depending on the source (and the range) of variation. Such systems elicit stressors to grow: rather they need stressors for their development. As an example, a human body benefits from stressors to get physically and mentally stronger.

The noncomplex and complex

To broaden the scope of analysis, let us redefine the distinction. More effective is the distinction between noncomplex and complex systems.

The washing machine, the torch, the lighter, the fan and the pen have a fixed way of working. You press the lighter, containing inflammable fuel, in a particular fashion and it lights a flame. Whether in summer or winter, whether in India or Greenland, whether by a man or a woman, by a toddler or an octogenarian, its behavior is same, and consequently, predictable. These are non-complex systems.

Social groups, societies, governments, institutions, organizations, businesses, markets and eventually the economy are complex systems. Though they are created by man, they are closer to the cat than to the washing machine. They may not be strictly biological, but they resemble the biological in a way, they multiply and replicate. And they are antifragile and robust as well! If we think of rumors, ideas, technologies, and businesses, we shall find them to be of the ‚ÄėCat‚Äô category. The noteworthy point is that such systems have a typical kind of complexity associated with it. The complexity of such systems could be understood with the help of an example: We take a specific animal away from its place and we disrupt a food chain: its predators will starve and its prey will grow unchecked, causing complications and series of cascading side effects.

In essence, ripples are manifold and unimaginable in complex systems, in case of a minor rearrangement of its components. Moreover the ripples are drastically different in every single case. In non-complex systems, ripples are minimal. The issue with the business case of predictive analytics is that though it works on all complex systems, i.e. social groups, businesses and economies, the analytical treatment given to those by predictive analytics systems is the one deserved by the non-complex systems. The Cat is mistakenly dealt with as if it were a washing machine! J

The Paradox of Cat and Washing Machine in action

To make the concept visible, let us take an example. The likelihood of a prediction being correct, of how a lighter (non-complex) would work, is much more than that of how a savings account holder would want to use his savings. The reasons are simple.

  • We know how does a lighter function, and
  • There is hardly a chance of any deviation in its function.

However, if we make similar assumptions for consumer (complex) behavior: that we know how does (a certain variety of) credit card consumer(s) function and that there is hardly a chance of any deviation in his/her function, we get into a serious trouble. The likelihood of the prediction being correct drops. This brings us to an important question as business managers/ consultants: What do we choose to predict and what we do not? The non-complex can be predicted accurately, but no one needs their prediction. The complex is not predictable by a layman and so this needs expertise (and probably therefore outsourcing). But the fact remains that accuracy of prediction is substantially diminished in case of complex systems.

I have been discussing such problems with data modelers and would want to discuss with many more in different organizations in future. What comes out is that the predictive analytics systems themselves are complex systems. This compounds the problem! To have a look at specific issues, please refer the following link.

I would request the reader to be in the shoes of a customer while going through the article above. As a customer, reading through the list of challenges faced in predictive analytics, I have started having doubts over predictability of the systems targeted at the consumer: at least partially.

Let us park the second idea. As discussed before, everything converges in the end. J

Traditional versus Desired Predictive Analytics

We have defined the paradox in the previous section. We now know that the market demands us to deliver prediction of a complex entity, as accurate as possible. Let us see how the traditional method does that.

It is widely known that traditional predictive analytics tools are based on the assumption of repeatability: that by storing and modelling data of the past, we can forecast or predict the same data of the future. Of course there are tolerance limits and there are explanatory variables which impact the values of predicted variable.  In short, it is past data of x to predict future of x.

The other way is to detect that ‚Äėx‚Äô is determined by ‚Äėp‚Äô and that ‚Äėp‚Äô is not as risky as ‚Äėx‚Äô to predict! So, better use analytics to predict ‚Äėp‚Äô. Predicting ‚Äėx‚Äô is then a cakewalk. The focus of business shifts from the skill of prediction to the skill of detection of what all ‚Äėp‚Äô like factors could be, and then to the skill of calculation of x based on predicted values of p. It is clear and so sellable too.

As an example of this approach, let us see how the Baltic Dry Index is calculated. The Baltic Dry Index (BDI) is a measure of what it costs to ship raw materials‚ÄĒlike iron ore, steel, cement, coal and so on‚ÄĒaround the world. The Baltic Dry Index is compiled daily by The Baltic Exchange. To compile the index, members of the Baltic Exchange call dry bulk shippers around the world to see what their prices are for 22 different shipping routes around the globe. Once they have obtained these numbers, they compile them and find an average.

The Baltic Dry Index is a leading indicator that provides a clear view into the global demand for commodities and raw materials. The fact that the Baltic Dry Index focuses on raw materials is important because demand for raw materials provides a glimpse into the future. Producers buy raw materials when they want to start building more finished goods and infrastructure‚ÄĒlike automobiles, heavy machinery, roads, buildings and so on. Producers stop buying raw materials when they have excess inventory and when they stop infrastructure projects.

Typically, demand for commodities and raw goods increases when global economies are growing. For investors, knowing when the global economy is growing is helpful because that means stock prices, commodity prices and the value of commodity-based currencies should be increasing. Conversely, demand for commodities and raw goods decreases when global economies are stalling or contracting. For investors, knowing when the global economy is contracting is helpful because that means stock prices, commodity prices and the value of commodity-based currencies should be decreasing.

‚ÄėThat‚Äô difference!

Though both the approaches of prediction eventually predict about complex systems, the difference between the two is that the traditional method is using a complex systemic variable as the means while the second approach depends on a robust and non-complex systemic variable. The business trick here is to use non-complex variable(s) to predict a complex one. Such relationships are there and if not, could be created.

For instance, unlike inflation in economy and guidance estimates declared by public companies, Baltic Dry index is robust in not only short, but medium run as well. It could be altered only by manufacturing more ships which takes at least two to three years or by trading quantities of raw material which are more or less than the actual needed. Either of the two cases would hardly occur. Also, this index is based on what has happened. The trading is already done. The predictor is placing itself smartly as he knows that the Baltic Dry Index could be a significant determinant of the stock prices. The traditional predictor would have used a model where growth projections of the economy and the sector/ industry would be inputs to predict the stock prices.

If a bank is looking at customer’s demographics, stage of life and family size as a means to present pension funds, children education plans and retirement plans, it is using robust parameters to predict a complex system. However, if past year’s transactions of credit cards spent on purchase of furniture or gadgets make a bank infer that the customer would prefer discounts on similar purchases ahead, chances are that this would classify under the header of iatrogenic.

Is it another iatrogenic?


If this article is appearing to be anti-predictive analytics, it is not. The intention is not at all to discourage prediction. We need predictions¬†and we need predictive systems. All we do not need is an iatrogenic. The name for a net loss, the (usually hidden or delayed) damage from treatment in excess of the benefits, is iatrogenics, literally, ‚Äúcaused by the healer,‚ÄĚ iatros being a healer in Greek.

I would illustrate this with a classic incident. In the 1930s, 389 children were presented to New York City doctors; 174 of them were recommended tonsillectomies surgery. The remaining 215 children were again presented to doctors, and 99 were said to need the surgery. When the remaining 116 children were shown to yet a third set of doctors, 52 were recommended the surgery. This story allows us to witness probabilistic homicide at work. Every child who undergoes an unnecessary operation has a shortening of his/her life expectancy.

The proportions of children recommended with surgery in the three events were 45%, 46% and 45% respectively. Do we see how strikingly similar are these proportions even when the event is completely random and outcomes completely based on medical tests and their evidences! This is a classic example of how the Cat was slanderously assumed to be a Washing Machine.


Similarly, it is the ‚ÄėNeed to do something‚Äô which appears at work in the case of traditional predictive analysis. It is an established fact that Humans (and hypothetically other complex systems too) view data in form of a normal frequency distribution. What does not get appreciated is that the normal distribution is of the nominal data and not real values. For example, it is usual for banks to call up customers earning slightly above average incomes in the age group 23-35 years for high end credit cards. The nominal data is the income and age group. The real values could be the ones considered and discounted with purchase aspirations, affinity towards credit and past experiences about possession of money. How often we find banks interested in affinity towards (the credit card kind of) credit of individual customers and the reasons underlying the varying predilection? This paves the way for an iatrogenic mechanism.

Usually, such iatrogenic patterns are not visible and so are not pronounced as a danger until it is late. J

The¬†BANK-Wagon¬†Needs¬†Fuel..!! Keep Fuel in the BANK !!

Nilav is an NRI in US. Before migrating, he used to stay in Mumbai. He belongs to a small Gujarati village.

His relatives and antecedents have resided outside India as well for a few decades in the past. They now have repatriated to the village. Every month he transfers some money to India. The figure runs into a few lacs of INR. He has an account in a bank branch in the metro city where his company’s head office is. He also has an account in a bank in his native village. The question is does it really matter what choice he makes while picking up the transferee account?

On the face of it, it’s a trivial question; isn’t it? Does it even deserve to be pondered over? Let’s see..


We had asked two questions above. In quintessence, the existence or continuance of a bank is ensured if the answer is YES to two questions. One, if it has sources of funds which come in form of demand and time deposits. Two, if it has sufficient (and credible) demand for loans. Demand for loans is the pair of front wheels of the bank-wagon, which propel the wagon ahead. However, equally significant are the rear-wheels of source of funds without which the wagon just cannot move.

In the real world, it is impractical to have the web of banks so evenly woven that each bank has a supply of deposit funds equaling the demand for loans. As a consequence, there are inter-bank markets where banks surplus in deposits (when compared with their own loans) can lend to banks with overwhelming demand for advances. If you observe, following this arrangement, a bank can ensure its sustenance by fulfilling any one of the two quintessential conditions discussed above!

Given this information, say I establish a bank in a village lacking even a soupçon presence of banks. To perpetuate the operations of this bank, all I have to do is to have people there park their money in the bank or/and make them dream of something which could prompt them to borrow from the bank. If I fail to realize both of these, the bank in the village will have to shut shop very soon.

It is here that Nilav’s choice will make a difference. Let’s take an observational route to this situation.

As a snowballing effect of the exponential expansion of IT industry, a steep depreciation of rupee, and an erratic, fitful and mutable stock market behavior, there are thousands of people like Nilav who are awash with money and wish to park into risk-less trustworthy places. They have fulfilled their desires of building bungalows and owning expensive cars and have enough for holidaying abroad every year. As per their own individual choices, they are financially sound. The only abode where they would prefer their money to take refuge is a bank in their home-town. There is less apparent rationale to it, but that is what the reality is!)

Let’s think about it. When we migrate from our native to other town, don’t we still prefer stashing our savings at a place where our permanent address is? Of course we do and Nilav is no exception to it. Emigrants of previously unbanked villages are likely to stockpile money in the same villages if they are banked now. To translate this likelihood into certainty, higher interest rates in such banks may work as a subtle finishing touch.


These occurrences are conspicuous because the figures are staggering. The reason is the percentage of people migrating to high earning jobs is more here. Banks here are offering higher interest rates as well. In villages in Punjab, Orissa and Andhra Pradesh too, similar practices are observed. No bonus points to guess that had these NRIs chosen to deposit their money elsewhere; the banks in villages had no option except to collapse as demand for loans is already not encouraging there.



As Fiscal Authorities, our focus needs to be on funds also along with offering cards and phone payments to tier 4 spaces. Let’s not miss to acknowledge that funds to a bank are like fuel to a vehicle. No doubt those marquee initiatives are needed; but actions on these simple sinews would add celerity and ease to connect things.

The bank-wagon needs fuel first to keep operating. So, let’s arrange the fuel before planning for fancy headlights. J

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