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P { margin-bottom: 0.21cm; }A:link { }CustomerAnalytics and Big Data:-Customersare considered the heart of every industry. In banking domain, themost valuable asset is the customer.

Customer should be considered asthe focal point of the entire process. Banking Industry is shiftingits focus from products to customers. It is in the race of becomingcustomer- centric. With the increasing amount of data generated byboth internal as well as external sources, banks should get anenterprise view of the customer.

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This huge amount of amount can helpin the decision making and gaining deeper insights about thecustomer. Banks should have a 360 degree view of the customer. Withtraditional data collection and analysis methods, even 180 degreeview of the customer was not available. Innovations in technologiescan help the customer to be a touch away from any service provided bythe organization. These touch points are the points of contact whencustomer needs a service or support from the organization. The numberof touch points is proliferated which makes it even more difficult toaggregate and analyse data from heterogenous sources.

It is veryhard for the data scientists to mine and analyze the huge mountainsof data accordingly. Different techniques of analysis are providedwhich will help in the exploration of useful insights about customer priorities. These techniques are used in different stages ofcustomer analytics.1.Customer SegmentationCustomersegmentation or Client Segmentation is the process of the division ofcustomer sample space into groups or clusters that are related toeach other in a specific manner. The inter and intra relatingattributes may include customer’s bank deposit details, age , gender,number of dependents, transaction patterns etc. Banks are nowadaysdata driven, so it becomes important to collect and gatherappropriate type of data.

Data gathering should be taken intocoinsideration as data is arriving from heterogenous sources such asInternet Banking, Credit card details, ATM transactions, etc. Thenproper method should be developed for analysis. Depending on thevolume, dimension and schema of data, appropriate techniques foranalysis should be used. Clustering is one of the commonly usedtechniques for customer segmentation. It is an unsupervised machinelearning technique that tries to find gruopings of similar datapoints in the input sample space. This technique outputs a group ofcustomers who are separated by a varied set of attribute values. Theresults of customer segmentation should be communicated properlyamong all the applicable domains. The main aim of customersegmentation is to make organizations understand that every customeris different from every other customer.

It will provide deepunderstanding about the demands, priorities and preferences of thecustomer. It is obvious that the results of marketing will betremenduos if addressed to potential customers. Customersegmentation defines diiferntiators that partrition clients intodifferent target groups.

Demography is a well known partitionfactor. It includes age, gender, religion, race, intellectual level,family income etc. Other factors include geography, psychography andbehavior. Segmentation of customers help the organizations in crossselling and upselling of the products. This kind of marketing ispersonal to customer adding appreciation and loyalty towards brandfrom the customer side. Impersonal and unrelated makrting materialsswitches off the interest of the customer. 2.

Prediction of the customer plansAfterthe customer segmentation, next important step is the prediction ofcustomer actions. Once the understanding of the customer is done, itwill infuse business agility. Segmentation of customers yields different customer groups. For prediction, all the historical datarelated to a particular group should be deeply analysed. More thehistorical data available, more accurate will be the prediction. Herethe issue of storing massive data sets come in picture.

Earlier asthe cost of storage space was very high, after the specific period oftime, all past records of customers were deleted. This was the majorhurdle in accurate prediction of data.With the evolution of cloudcomputing which follows a simple rule of pay as per use helpedorganizations to keep their data on clouds in minimum availableprices. The biggest advantage of predicting customer actions is thatit helps to detect and prevent customer churns.

It is revealed by theorganizations that the cost of gaining new customers is much morehigher than maintaining the existing customer. Maintining theexisting customer involves a series of steps which includeidentifying customers data and its relationships. Data should beanalysed in such a way that correlation between different customerscan be found, if it exists. Different prediction techniques areavailable which are used depending on the type of data. For example,if the analyst is interested in the linear relationship between thedependent and the response variable, regression is used.

If the datapoints in the input sample space are multiple, affect of on variableon all other variables in the input space can be analysed bytechniques like Support Vector Machines and Bayesian networks. If weare dealing with mixed mode data, decision trees is an appropriatechoice. These techniques will refect the already present outliers inthe data. Depending on the context, either outliers are discarded orgiven some special attention. If outliers are reflected in thenetwork traffic, it reveals the presence of mailicious user in thenetwork which needs special attention. This leads to the discovery offrauds in the organization. Frauds are becoming sophiticated soshould be the procedures to deal with them.

FraudDetection In Banking with Big Data:-Analyzinghuge and enormous amounts of data helps in the detection of fraudsin the banking domain. It helps the organisation to fight against thevulnerabilities by using proper analytics. Banking Industry is mostvulnerable to frauds. If a bank indulges in a fraud at some point,customers mostly refrain themselves from doing businesses and usingservices from the bank in the future. Neither frauds nor frauddesigns and tactics are traditional. Well bred and jaded fraudprograms force organizations to respond in a new and differentmanner.

Statistics reveals that nearly seventy one percent ofcustomers switch their banks due to the frauds. Frauds cannot creepinto the organisation suddenly. Banks can analyse the streamingtransactional data in real time.Transactional behavior can help todetect the unusual behavior of the user which can lead to somemailicious activity. Banks can analyze petabytes and zetabytes ofhistorical data which can help in the future prediction accurately.

These analytics can give the patterns of frauds which has happenedearlier. This can help to detect frauds and stop them as soon asthey are about to occur, helping greatly to an organization as itdoes not cause any serious damage. The best advantage of detectingfraud at early satge is its low cost and expenses. Elegance of aneffective analysis is to predict the fraud before it happens or whenit strats happening.

After fraud prediction, proper method should beemployed and the roots of fraud should be investigated. Theseinvestigations would turn fraud intelligence into actions. Usage ofmore internet banking, credit cards and atm cards open doors to moreproduction of data. At the same time, it provides a chance ofpenetration by the hackers. Undoubtedly, the quantity of the dataproduced by the bank is giant and the structure of data poroduced bybanks is complex.

Traditionally, future decision making in banks wasimproved by digging and mining the already available internalinformation about the customer. Internal information and periodicreports cannot help in leveraging the insights about the frauds andmalicious activitities. Special mechanisms should be employed topredict, detect and investigate frauds. The mechanisms should beagile because the situation of organization after fraud pediction isuntenable. This agility in mechanisms is provided by big dataanalytics. It monitors the network traffic and system logscontinously.

There was a big breach in storing the datatraditionally. User logs and network traffic records were deletedafter a specific amount of time. As a result, organization couldnever visualize an overall picture of the transactions of customers .

Big data and cloud computing are two closely inter related terms.Cloud Computing provides an easy way to save the data on cloud inmininmum possible rates. This helps in storing all the data without any need to delete some part of it. Understanding customer viewsItis important for the organizations to know how the brand name, brandimage, services and products are percieved by the customer.Organizations are keen to know whether the customer is satisfied withthe existing services. Organizations are happy to welcomerecommendations from the customer side. Many organizations conductsurveys online as well as offline to know the customer views.

It isobserved that surveys refect what customers say. These statementscould be different from the real actions of customers. Strategies ofthe competitators cannot be exactly known. To increase the stabilityof the organization in the agile market, it is important tounderstand the customers.

Customer are the only entities which candrive the business to unimaginable heights.

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