As a technologist and data enthusiast, Sandeep Lodha – Director, Netweb puts forth an argument on how Big Data can curb the menace of black money in India.
The effect of demonetisation on black money is an intense matter of debate today. It has received reels of coverage through debates on television, various perspectives in print, and parodies on social media to lighten the tense mood.
While most debates have been about big data’s role in helping the government track black money, what is missing is how this will be accomplished. To put things in context, let me tell you a story that stems from a personal encounter. It’s a great example to cite since we are eventually trying to implement similar practices in India. Technology always has its ways of bridging the gap between the developed and developing countries, large and small countries or companies and between individuals, by democratising costs and capabilities.
The story goes like this – a friend of mine, an entrepreneur, running a million-dollar business hit a rough patch a few years back. Due to a downturn in his business, he was not able to file his returns for a few years and was eventually summoned to appear in court. By then, he had managed to turn his business around and was ready to make a settlement in court. As he prepared to face the authorities, he armed himself with proper books of accounts and derived his exact tax liabilities. When he appeared in court, to his amazement, the officials had also prepared books of accounts with all his monetary details and transactions. His initial fear that the government would slap a hefty claim on him turned out to be a misnomer. They were so well prepared that eventually, both books of accounts were differing by only two thousand dollars or less – even the sub-heads were matching. If the story sounds implausible, let me explain how and why it is actually possible.
When I look back at this incident and try to figure out how the government managed to do this, the answer actually lies in the processes and amount of information available to the government. Did the data come from various government and semi-government sources? The answer is a resounding ‘Yes’. I’ve been running a company in Singapore for a few years now and with my data science background, it is not difficult to understand the co-ordination of data being shared between various data sources, which can be analysed by the government and put to use – even to create a company’s balance sheet. Bank transaction and GST details are in itself two important examples of such data sources.
Big Data Analytics will become very relevant
I believe this has great potential to generate a plethora of new data. More data is always helpful in performing better analytics. As and when a new dimension is added to the data pool, analytic possibilities are endless. We might be short-sighted now, but there are some critical data elements we can successfully capture. Let’s examine a few.
- RBI has been issuing new currency since demonetisation and all the currency has been shipped to numerous RBI chests and further issued to various banks. I’m certain RBI has a record of all unique serial numbers on the currency notes being infused in the system. In all likelihood, bank staff emerging as suspects is a good indication that the government is quickly able to figure out from where these currency notes were pumped into the system and where the money is being siphoned off.
- Another golden source to capture data are banks themselves. Banks possess a wealth of information based on transactions – withdrawal, deposit, foreign exchange etc.
- Another game changer that has not surfaced yet is scanning unique serial numbers on currency notes to track and trace the movement of money in and out of banks. Maybe tracing the currency notes back to its owner and relating this data to GST records might produce interesting results on the journey of the note itself. Fortunately, all notes have a unique serial number, which could deal a blow to fake currency, if effectively used to identify fake notes that have seeped into the banking system. In fact, Big Data Analytics has the capability to track the movement of currency in and out of the system which could bring about some unique patterns. Even patterns of dormancy to sudden appearances of a single note would be known. And what’s even more fascinating is that you could use this to track the hotspots of currency hoarders. Let’s assume this practice of scanning the unique serial number gets incorporated in our banking system to track various fake currency rackets in the country; the magnitude of data hence obtained from this would be so enormous that implementation of Big Data analytics would be inevitable.
- It might be of great value for the government to keep track of all the notes that have been recalled. Noting down the serial numbers and the banks and regions where these have come in from, may throw some interesting insight in the times to come. Some corrupt governments in other countries are said to have printed multiple notes with the same serial number to fund elections and other activities and while we do hear rumours of similar incidences in India, the truth will come to light if such an analysis is carried out. The more granular the data, the easier it is to pinpoint the sources.
- Needless to say, the increasing adoption of digital transactions will be key to get a strong hold on cashflow and movement of money. Every percentage increase in digitising the economy will subsequently strengthen the quality of data and its finding.
Days to come will show the effectiveness of all the data captured. The focus and planning must be towards capturing data now rather than analysing current data. This seems to be the harvest season and we should not worry much about the dishes it can serve at this juncture. We need to achieve a high degree of granularity of data and work towards preserving it.
GST will be the basis of it all
In India, the plan to soon introduce the Goods and services tax (GST) has set off alarm bells. The GST roll out will reduce a lot of business complexities and create ease in doing business, but the beauty of this system is how data will be captured at every transaction. Individuals or companies cannot evade the system because there is a part of the tax which will be offset and to their dismay, the goods they sell will be recorded in the first cycle; if not they will eventually get caught as the cycle progresses. It will also reduce the cascading effect of the tax on goods bought from other states for resale. GST will also provide a nationwide registration of businesses and facilitate easy business operation. GST can effectively reduce black money and will widen the tax net in itself.
We’ve heard about, how the introduction of GST will lead to an incremental GDP of about 2 per cent in India; my bet is, it’s largely aimed at individuals and companies who are currently not paying tax, and with these GST reforms, are expected to fall within the tax net. GST implementation in India will have its own share of hiccups, owing to the complex federal structure in India. Coming back to the topic of demonetisation – was it the right time to do this? It certainly seems plausible to think that any measure of cleansing (like demonetisation) should precede the GST implementation, for best impact.
This topic will not be complete if we do not discuss the huge endeavor of data collection that accompanied demonetization in India. What will the government do with such a huge volume of data? Why is data so important? How will the government be able to integrate huge islands of data? The answer to all these questions and more will be presented in the second and last part of this article, which will be published in HPC ASIA soon. Do comment or message your point of view in the meanwhile
Sandeep Lodha is the Director of Netweb Pte Ltd, Singapore. As a data scientist, he leads the big data solutions team at Netweb – a provider of servers, workstations, storage, high performance computing and big data solutions. Lodha’s journey with data analytics started in 2011 when he spearheaded various big data & HPC projects for Netweb Technologies. He is a frequent keynote speaker on big data, data science, and HPC at international conferences and meetings, and has written various articles on the application of HPC and big data across industry sectors.