Why Financial giants American Express and Citibank are using Big Data Hadoop ?

 

 

So, financial sectors all around the world are experiencing drastic changes, and you know why? Well, you must be aware of the fact that the global financial crisis of 2008 came out in failing of various banks that had negative impacts on jobs, incomes and wealth. As a result of which financial sectors needed to work hard to get rid of such occurrences in near future.

Financial institutions realized that they have to improve their efficiency, detect fraud immediately and accurately, model as well as manage their risks and cut back customer churn. To achieve this, many financial giants like American Express and Citibank turned towards Big Data Hadoop.

But did you know why Financial giants American Express and Citibank are using Big Data Hadoop? If you are unaware of it, let’s take a look at below-mentioned cases that exemplify how big data and Hadoop are being integrated.

Fraud Detection: Abating anomalous activities in real-time can help avoid potential security threats or frauds. The MapR Distribution in Hadoop architecture gives financial institutions the ability to construct usage models of “normal” characteristic from histories of consumer behavior and take suitable action if the activity goes out of the confidence level of normal behavior. As a heap of data is ingested, a heap of price models can be built so that it can more accurately separate the inconsistent but normal behavior from the apprehensive yet suspicious activities.

Analysis of Customer Sentiments: The growing number of mediums through which consumers communicate has resulted in financial institutes needing to know what their consumers are saying about their products & experiences so as to ensure consumer satisfaction. Banking sectors can use the MapR Distribution for Hadoop to evaluate comments on social media or feedback sites helping them to quickly respond to positive or negative comments.

Customer Segmentation Analysis: Financial sectors can create a more meaningful as well as efficient context for marketing to consumers if they can describe distinct categories or segments in which every customer belongs. Usually, these segments are described on the basis of demographic details, but the more unified and useful segments are also described by customer behavior. By using the MapR Distribution for Hadoop to gather & analyze all data, banks can define better customer segments. These data include daily transaction, interaction from multiple customer touchpoints, home value data and merchant records. Financial sectors can examine these data sets ton group consumers into one or more segments on the basis of their requirements in terms of banking products as well as services and map their promotion, sales and marketing campaigns accordingly.

With such insights, not just banks can respond to emerging problems with their consumers but gain get a better understanding of the products and services that the consumer find valuable.

Counterparty Risk Analysis: Whenever an organization starts business transactions with another party, the risk of doing business with that very party must be evaluated in terms of the deal. Since measuring counterparty risk needs more than calculating a formula, organizations usually run long & complex to get a complete picture of risk coverage at many points in time. These replications need huge volumes of data, massive parallel computing power, and system reliability to make sure organizations can carry on with business operations with no downtime. The MapR Distribution of Hadoop offers the scalability, performance, reliability and easy access as well as delivery of data to compel the key components of a counterparty risk analytics system.

Risk Aggregation: Big Data Hadoop can be used to collect and process risk data to- satisfy risk reporting needs, slice and dice financial reports and measure financial performance against risk tolerance. The MapR Distribution can benefit risk managers as they can execute on-demand historical analysis of risk data and get real-time alerts when restrictions are violated.

Credit Risk Management: Owing to the universal financial crisis, there are now a tad more stern rules for deciding whether or not to furnish a customer with loan, so banks call for more accurate ways to decide risks involved in a person’s credit. A number of quantitative factors are used in credit risk analysis and credit scoring. The MapR Distribution of Hadoop enables financial sectors to pull in consumer data on everything from deposit to customer service emails to credit card purchase history so that a holistic view of their information could be gained. With this ecosystem, banks now have the tools they require to construct an in-depth analysis of their customers so that they can suitably provide accurate credit scoring well as analysis.

Now you’re aware of the reason why big players in financial sectors are garnering the potentials of Big Data Hadoop.

 

 

 

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