Rule of Thumbs

In a recent ACFE conference, I was lucky to hear Mr. Muralidharan, Co-Founder, Hethi, talk of "disruptive change" in a new and novel way. He was mentioning that if we could clone our knowledge / experiences into bots making ourselves redundant, and look beyond where the bots would aid us in managing the current day function and we look at a larger, bigger, piece of unchartered territory which we have never gone in to earlier owing to time and management limitations. While he was talking about the same in the fraud prevention perspective, his perspective was a breath of fresh air.

In the days of algorithms, logics, bots, artificial intelligence, robotics, entering every field, it is imperative that any method / process is reduced to a template / algorithm. Here the algorithm refers to a series of data points which presents itself in multiple combinations based on a pre-set logic that links the data points.

Let us make this clearer. I take an example in Agriculture. Let us grow closer home. Let's say we're into Farm Equipment Business. Let us say we have the following databases :

(a) Rainfall Database - data of rainfall by state, district, taluk, gram/ village

(b)         Cropping Pattern - Let us take rice for example. So for the crop of Rice, we arrive at a logic of

• ·        Rice-Rice-Rice
• ·        Rice-Rice-Cereals
• ·        Rice-Rice-Pulses
• ·        Rice-Groundnut
• ·        Rice-Wheat
• ·        Rice-Wheat-Pulses
• ·        Rice-Mustard-Wheat
• ·        Rice-Fish Farming

               We can build such a crop logic for every crop.

(c)          Implements Database - List of all implements mapped to its most popular use / dominant use.  This is

probably one of the most important links in the databases

(d)         Models Database - List of all Tractor and Harvester Brands, Models, Horsepower, PTO, Torque, MRP and Subsidy.

(e)         Irrigation Pattern - Data of Irrigation modes adopted by Village / Block level. This should include               metrics of water levels and / or cusecs of water received on an average.

(f)          Mandi / Market Database - List of all Mandis and Storage Warehouses by District, Taluk, Block,  Village.

It is possible for just these metrics to convey to us, if information received during viability assessment immediately after FI, is accurate or the level of accuracy can be predicted.

Unlike popular myth, Agriculture is a studied science, with predictable processes and predictable outcome. While a farmer does suffer tremendously under natural calamities, the extent is perfectly measurable.  

Every farmer adopts local practices, and practices native, natural methods of evaluating threats, and eradicating pests / unwanted interferences. This is what makes him / her most resilient.

Evaluating such a person for a financier therefore requires some understanding of the farmer's atmosphere.

Now just imagine, if my person on the field, evaluating the farmer for a loan, is prompted to ask 3 - 4 pointed questions, which when fed into my logic / algorithm, would throw up a risk quotient, which would translate itself into a "funding exposure permissible" - Right there, on the field, within minutes. In the days of the yore, we called it Business Rule Engines. Today, it developed into predictive and calculated patterning.

Instances plotted by geo, and clustered together becomes PATTERNS

Patterns mapped to pre-defined methods become RULE OF THUMBS

Rule of Thumbs linked to payment cycles, repayment culture, borrowing patterns, lending availability becomes CULTURE

A very powerful tool, with a powerful capability. I tend to agree with Mr. Muralidharan. If we need to progress closer to the farmer and his eco system, there is greater need to harness technology and equip it to aid us towards making benefits available to the farmer. This way, even the smallest farmer will be serviced suitably and the proverbial "Double Income" for the farmer will churn itself... Food for thought !