How Big Data Hadoop plays an important role in Banking Sectors

How Big Data Hadoop plays an important role in Banking Sectors

For many of you, acronyms like KYC/AML/CTF/FCPA sounds like some technical jargon, which has been used here to discuss something technology related.

But, for others who are employed in banks, the above said acronyms sound familiar. They can decode these acronyms as AML means Anti-Money Laundering, KYC as Know Your Customer, CTF as Counter Terrorism Financing and FCPA as Foreign Corrupt Practices Act.

 

The bankers also do not have to pay attention to another painfully obvious anecdote regarding any analyst who has just entered the company and has an opportunity to work with known bankers.

However, for a bank experiencing large fines, what happens when these things aren’t clear and the answer aren’t of multiple-choices? How can technology & data help people depend less on personal predictions than on facts to avoid money laundering and illegal financing?

 

Fines & budgets are escalating

At present fines & financial settlements for banks not adhering with AML norms are growing which have crossed 13.4 billion dollars in 2014. As such financial institutions are escalating their investment in counter-measures that involve the employment of earlier examiners as staff members in senior agreement roles and advanced solutions and data systems.

 

If we trust an Ovum’s annual ICT Enterprise insights survey, then compliance tends to be an integral driver of escalating IT budgets. The research revealed that 55\% of retail banking respondents longed for AML related IT budgets to grow this year.

 

Now, if the banks have the IT budget, the question here pops out is how can they most efficiently use it to avoid money laundering & adhere to regulatory consent? Well, the simple answer is big data and analytics.

 

Track the money with the data

AML displays a data analytics challenge along with a number of sources and types of data accessible for analysis. These include both public and private data that may be in structured, semi structured and unstructured format.

 

Publicly Available Sanctions Lists: These data sets encompass the OFAC (Office of Foreign Assets Control) sanctions lists of Politically Exposed Persons (PEPs), Specially Designated Nationals (SDNs), sanctions programs and countries.

 

Client & Legal Entity Data: Historically, banks have handled their client databases within their premises or relied on other commercially accessible data on persons and entities. Lately, they’ve started to combine efforts with the creation of clients & legal entity data utilities to be leveraged across many financial institutions.

 

This improves the customer identification of the bank and due diligence abilities and provides a common recognition method.

 

Personal Communications: Interactions with counterparties can take many forms as well as marked themselves in many systems.

 

Financial Transaction Data: Transactional structured/semi-structured data is usually kept within the trades or associations in which transactions have occurred. .

When banks exploit the value of this data, they can set big data analytics to help find out the activities of money laundering. As banks are getting into big data platforms on fast pace along with Hadoop and analytical tools, they can be applied to resolve this issue.  A platform that allows ingestion, analysis, enrichment, and visualization of these large, diverse and regularly changing data sets can be the best assets of a bank.

 

Apart from that, by enriching transaction data with client or lawful entity data along with name, address, contact numbers and other identifiers, and publically accessible OFAC lists, financial institutions can track every transaction to decide if they were done by known high-risk persons or non-cooperative jurisdictions.

 

In addition, by enriching transaction data with client/legal entity data (including names, addresses, and other identifiers), and publicly available OFAC lists, banks can track transactions to determine if they were completed by known high risk individuals or non-cooperative jurisdictions. Supplementing this data further, with written and verbal communications info can help show the net wider when looking at possible indicators.

 

In today’s world no bank or individual wants to allow money laundering or provide finances to any terrorist group or organization. With the right venture in the right solution and data platforms we can rest assured that no illegal activity is going around and that we are implementing big data analytics to help check this problem.

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