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A Timeline of the Contribution of HADOOP on the Fraud Detection in the Banking Sectors

It is the banking sector that secures our money, but it is Apache Hadoop that secures the unstructured data of those banks. Every little and big banking sector looks for profit, profit and only profit. To acquire such profit, banks have to take care of the safety and security of the money they hold as deposits from their customers. Internal  as well as external risks always prevail in the banking sector of any society. So banking sector always looks forward to having such kind of strict security where nothing remains unnoticed. The banking and financial firms have got a huge amount of unstructured data that only Hadoop has the capacity to store it in streams. Many banks use Hadoop technology, which gives rooted analysis to improve the security team and protect our investments and savings.

For the last two decades, the banking sector has suffered drastic changes due to the security of the deposit they keep hold in their big and strong lockers. Thus, in  order to avoid those crises and move towards efficiencies and fraud detection, fast finding and a high-level guard is a must. Hence, the marketing and financial domains are running towards Apache Hadoop for high encryption and deep analysis with 100 percent correct statistics.

Distribution of all the different customers to different sections is another behavior of Hadoop, which is so much appreciable. The MApReduce finds out which account belongs to which particular customer and HDFS stores them in different packets with greater regulation.

Data management is all that any bank cares for and same its customers do. Hadoop is such a technology that gives its hundred percent in the managing of data with proper security.

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The applications for account openings, the sanction of loans, etc. suffers various mismanagement by the bank workers, resulting high loss of the banks disappointing their customers ironically. When Apache Hadoop is implemented, because of its high storage capacity, and analytical capability, it stores the entire big data in blocks and analyses the managing of them by matching them up with the particular strict bank policies. Hadoop is such a strong noticing tool that it even detects if the managing team of the bank itself makes any blunder.

The various records from old accounts, fraud that has to be caught in them, the lost files, all these need Hadoop for its antidote. Authenticating and encrypting the bank’s data in high security is the reliability Apache Hadoop offers.

The conventional data storage could store only to a minimum number of year’s data. But Hadoop stores such big size elephant data  that from now till ages, all the bank’s investment data, loan data and every data can be packed safe inside Hadoop only and its fast processing system gives faster feedback and assistance to the bankers and also to the queries of the customers.

Detecting the various patterns

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When all the historical records of all the accounts are present, obviously everything will be cleared and any sudden change or mismanagement can be caught by Hadoop resulting in gaining  the true trust of the customers.

Since the transactions in the banks occur at a faster rate so the financial services are heading towards the use of big data and Hadoop in order to avoid fraud in a much standard fashion.

Hadoop can figure out the customer’s  behaviour and filter it if there is in case any sign of fraud. Hadoop analyzes the data from devices and social media. It figures out how often a customer swipes his/her atm card. In case, if it is swiped more than normal, it will send the message to the customer’s mobile phone to ask whether the card has been swiped by the same person or not.

Big Data and Hadoop is also assisting the financial services to have an idea of the type and time of their future attacks to happen. This gives an alarming ring tone to the banking firm to have layered security and services.

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Detecting the various patterns of a transaction and finding out if any misbehaviour prevails in  those transactions either by the server or by the customer is helping out the banking and other financial firms to have a cross-checked status of their security.

MapReduce the processing unit of Hadoop technology, has enabled the banks to detect and understand the frauds occurring during payments of money and reduce malpractices in those currency firms. High level of analytics and forensics is possible with the help of the implementation of the Big data and Hadoop technology. Faster analytical practices, market planning strategies and data storage costs are lowered after the involvement of Hadoop and data analytics in banking sector.

Uncovering the various patterns of a transaction and finding out if any misbehaviour prevails in  those transactions either by the server or by the customer is helping out the banking and other financial firms to have a cross-checked status of their security.

Thus, implementation of Hadoop  is resulting a huge relaxation of security and concern to the financial services and a hope of  profound confidence  in the eyes of their account holders. To understand the fundamentals of Hadoop, you can visit our detailed Hadoop tutorial.

The opinions expressed in this article are the author’s own and do not reflect the view of the organization.

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