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How Big Data and Analytics can help India fight against money-laundering


With the world economy and our lives rapidly moving to the digital platform, governments across the world are facing a new set of challenges in trying to protect the interests of their countries and citizens from many known and unknown threats. One such threat that, though mostly invisible, has a significant impact on economies worldwide is that of money laundering. It’s an issue that plagues the developed and developing nations alike, and national governments are working hard to create fool-proof regulations and enforce laws to help curb this menace, which not only fuels the underground economy but is also seen as a major factor contributing to increasing acts of terrorism across the world. In fact, the European Central Bank recently announced that it will no longer produce €500 bank notes, fearing that the bills are being used by criminals as a convenient way to launder money and finance terrorism.

Closer home in India, the Modi-led NDA government has taken various measures to rein in offenders, and has also entered into tax treaties with governments across 139 countries, obliging them to make available information on miscreants for tax purposes. According to a fresh estimate by a trio of senior economists from the Bank of Italy, from the $6-7 trillion worth of black wealth lying hidden in tax havens across the world, Indian’s share is estimated at $152-181 billion, which makes up a sizeable portion of our economy.

 To effectively deal with this menace, the Supreme Court-appointed Special Investigating Team on black money came out with a slew of recommendations in September last year to put physical curbs and penalties on money laundering activities. And in a rather progressive step forward, India’s honourable Finance Minister recently acknowledged how technology can be a good facilitator for the taxman to detect violations in tax filing by entities and individuals alike.

With technology at the core, we can go a long way in tracking and reining in the money laundering issue that India is currently dealing with. Financial institutions need global practices that make each customer’s information readily accessible across their global accounts, and consider their risk variations as well as product and country-specific requirements. But how can this consolidation be effectively implemented?

Technology companies offer compelling solutions to harness multiple data sources and to deliver timely insights that can keep banks vigilant and informed so as to help combat money laundering.

Underlining this solution is a flexible and scalable computing platform that integrates with existing applications and is capable of keeping up with today’s enterprise demands, while meeting multiple regulatory requirements. Also, this platform enables real-time advanced analytics such as machine learning, to quickly process and correlate massive amounts of disparate data and detect new risks earlier in the process, preventing potential fraud.

So where do financial institutions start?

First, they need an up-to-date, complete, and single version of data, which enables faster analysis and reporting. This ability to rapidly analyse large volumes of structured and unstructured data demands a technology platform that is specifically built for that purpose. It requires a high performance platform that can ingest billions of data records, perform advanced analytics, and deliver results in real time.

Second, financial institutions should aim to have an enterprise data hub built on an open-standard and open-source framework such as Apache Hadoop since it provides a cost-effective way for them to aggregate and store all their data, in any format, for all types of workloads, in a highly secure environment. Companies across the ICT industry have collaborated to provide more relevant and secure solutions to end customers. As an example, an open source effort – Project Rhino – was launched in 2013 to enhance the existing data protection capabilities of the Hadoop ecosystem to address these security challenges. Such solutions allows financial institutions to securely analyse big data for efficiently detecting known, unknown, and emerging risks, to quickly drill down into related information and determine if a given anomaly is truly a risk, and to generate accurate reports of new and emerging risks for regulators and executives, quickly and efficiently, even as requirements change.

The need of the hour is an up-to date platform for data management and analytics which will provide a comprehensive infrastructure, solutions, and services to tackle AML holistically. This, in combination with advanced analytics can enhance data collection, storage, and data preparation for advanced analytics operation. It also helps prevent duplication, and delivers fast analytics-based services enabling data driven decision making in real-time.

With the advent of the recent digital wave, banks and financial institutions will have to ensure that norms pertaining to money laundering are implemented, and effectively monitored. There are several end-to-end technology solutions in the market that can help combat money laundering, efficiently manage operations, and gain new insights that can translate to improved products and services.

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