Black Money curbing enabled by Data Sciences

Time to Read: 4 minutes

In a surprise announcement late Tuesday, Prime Minister Narendra Modi banned 500-rupee  and 1,000-rupee notes effective midnight, sweeping away 86 percent of total currency in circulation. In a nation where 98 percent of consumer transactions are still made in cash and about a quarter of the $2 trillion economy is unaccounted for, Modi’s pushing for crackdown on Black Money, making the illegal stacks of hidden cash instantenously invalid. A commendable move!

Though this move is bound to bring significant results, no doubt, but could it be completely fool proof? The black marketers who stand to lose the most would set in motion the usual machinations to convert the black money to white. Channelling their stashes in small amounts through tricks like income in cash, bogus loan entries, Artful Sale of personal belongings (jewellery) etc. Even though big players won’t be able to move huge stashes  in 50 days, which is the assigned duration to exchange or submit all old bills, to some extent, there is a distinct possibility that they could get away with smaller stashes. Then there is always the possibility that once the new notes are released, the black money accumulations starts all over again, with new stashes created with new currency bills.

For overall monitoring and crackdown on Black Money, it would be imperative to implement machinations to curb money laundering which will work even after the new notes have been released, and this calls for leveraging latest technologies in location tracking. To put in place, a measure to track accurately the geographical circulation of cash and its density distribution, and employing intelligent data science algorithms to track their movement to predict the transaction channels and end points of black stashes, in real time, would lead to a major fallout of black money transactions and money laundering. This  could be the final missing piece of making crack down on black money completely fool proof.

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Even though the rumours around embedding NGC (Nano GPS chips)  in the new INR 2000 notes are unsubstantiated, the possibility of such scenario could have had far-reaching benefits. In case it was feasible to implement nano gps chips, or non-gps tracking hardware like TIMU (Timing and Inertial Measurement Unit) right into the currency bills, it could instantaneously provide a heat-map-like snapshot of the currency bills, their stash locations, the density distribution, and the circulation flows. But to figure out the Black stashes from the legitimate ones would require intelligent data science algorithms in place.

Even though the rumours around embedding NGC (Nano GPS chips)  in the new INR 2000 notes are unsubstantiated, the possibility of such scenario could have had far-reaching benefits.

One of the main goals is to find patterns and commonalities between money laundering incidents so the police agencies can be better prepared and place resources in hot spots. Employing Display Lines to see movement over time, like the Lines Function employed over data models in Data Visualization Softwares like QlikMaps would give a clear trace. Many people think you need animation to see how things change over time. But that’s not the case. The use of lines is an effective way to visualize the movement over time.

Next, evaluating data points’ relationships to landmarks will give a clear understanding on how the stashes are maintained. This could be leveraged to create a pattern tracking algorithm to find or predict other geographical locations that could be potentially used as large or small stashes to hide black money. Employing individual prediction algorithms on both the display lines (physical flow of cash), and on the start and end points of movement (stash locations) would better the chances of coming up with extremely accurate predictions of illegitimate currency stashes.

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This can further be supplemented by virtually visiting the potential locations. Map Apps, street views and 360 degree views can be employed to zero in on each data point, so that manual assessment employing verification by humans could be leveraged to quickly nullify outliers or false positives. Also, on an advanced level, customized pop-ups to show real-time street camera views  to monitor behaviour in hot spots could be employed.

The Overall Currency Tracking Apparatus

This location based cash-tracking apparatus must be implemented not as a singular measure, but teamed with tracking digital transactions using data science. The finance ministry is looking at putting in place a new data science system to log large spends and monitor splitting of transactions as it steps up deployment of technology to track black money in the country.

The ministry is likely to soon release a request for proposal or RFQ, the first stage in a tendering process, officials said. Efforts have been on for at least three years to install such an analytics platform.  They understand what they want and are looking at a way to frame it within the RFP process. They have been reconciling data on a piecemeal basis, but putting in the system will automate a lot of the work. The department already has a system that logs transactions of Rs 5-10 crore, but the new engine will track spends starting at Rs 50 lakh. And they are looking to see if they can make the flagging a real-time process because currently the data processing in not real-time.

The fact that the physical location of every currency bill can be instantaneously tracked makes the task of tracking black money extremely easy, as the only hurdle that remains is to build an intelligent Data Science Algorithm to separate the illegitimate stashes from the legitimate, thereby leaving no breath for employing any means of converting black money to white. Instead of finding individual solutions of different ways of whitewashing black money dealings, this measure could provide an over-arching solution to beat every rogue plot in existence, or to be devised in the future.

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Black Money curbing enabled by Data Sciences

by XTech News Time to Read: 3 min
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