Demonetization: How Big Data could have unearthed Black Money in India

Dec 06, 2016

The Indian government has banned Rs. 500 and Rs. 2000 currency notes under the Demonetization drive. The exchange of such a huge number of notes is a Herculean task—millions of people hold that currency, with 85% of the monetary value of the economy held in those higher denominations. The drastic measures of Demonetization are supposedly aimed at curbing the so-called black money and ensuring transparency and rooting out corruption from the economy.

However, with the advent of new technologies, it is possible to trace black money with much less pain to the population and less drastic consequences in nature. Modern technology such as Big Data and Analytics can be leveraged to unearth the trail of black money in this age of Demonetization.

Big Data Analytics tools provide clues to monetary malpractices in real time. The tools can be utilized to detect currency corruption using present infrastructure. Notes have a denomination, country identifier, unique serial number, and a mechanism for counterfeit prevention. There are also cash counting and currency detection machines that banks and financial institutions use. Those machines can be equipped with enhanced tools to detect and store serial numbers of currency notes that are run through them.

“Using tools such as Big Data can be very effective in collecting information about financial misappropriation. The data once provided by banks can be analyzed to find out unusual activity in currency flow at any end point, whether it is at an individual or a regional level. The algorithms can indicate the rough location of hoarded money once the collected data is run through them,” said Shashank Dixit, CEO, Deskera, a global leader in cloud technology that has developed its own Big Data tool.

Big Data tools can run through complex and gargantuan data sets—something that wasn’t possible with traditional data-processing methods. Data analytics can then process the data sets to uncover hidden patterns and unknown correlations, leading to the discovery of meaningful trends and tips. Financial institutions can leverage it to describe transactions, predict financial fraud, and improve detection and surveillance. Mathematical and statistical tools and algorithms decipher trends (where are the currencies coming from?) and make predictions (possible locations where the cash hoarding may take place).

The tools may be calibrated to factor in several parameters such as the geographical location, serial numbers, individual account, etc. Using the data meaningful information can be retrieved and every transaction can be traced to the account holder or to the last person.

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