Analytics Insights of Financial Data Using the Hadoop Platform

Financial transactions are evolving over time in finance services industry such as banking, and insurance and thriving their presence over various platforms generating huge amount of data per day based on addition of number of users and their activities giving full potential to target them with right opportunities after making the data storage and retrieval process fast and robust which can further be used for various kind of analysis activities for the customers in order to make clear and right business decisions.

Over a period, financial services industries produced enormous amount and varieties of data on a rapid scale which is leading to a new generation of data analytics platform called Big Data Analytics.

Hadoop provides an open source platform to store data in a Hadoop distributed file system (HDFS) and it can be processed and retrieved by Map Reduce in a same distributed manner on a large scale, YARN allows the management of computational resources present in the cluster and scheduling the users’ application.

Apache spark gives the computational framework for processing real time data with a blazing speed and its scheduling by reducing the read/write latency in the memory computation. It can handle different tasks such as batch program, iterative codes, interactive database queries and streaming data. Spark supports various APIs in Java, python and R which can be helpful in performing any analytical practice in form of Machine Learning and Deep Learning by using MLlib library. Data extraction in SQL can be performed using spark SQL which can be helpful for processing streaming data as well.

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Scope of Analytics on financial data

It all starts with descriptive analytics then predictive analytics to prescriptive analytics based on the data we have , with robust data storage and processing with the evolution of new technologies like IoT, ,Robotics, Chatbots and blockchain , we can easily create a data lake which comprises data from different sources for any particular subject which can further be used for concrete data extraction for any analytical process , big data analytics has provided a new face to financial industries to practice advance techniques for any analytical process and it can be for banks , insurance organizations and payment bodies to know the behaviour of their customers which can be helpful for better decision making , following are the different aspects in which big data analytics can leveraged

• It can be used for customer analytics by making customer 360 degree view which can help for better decision making by analysing customers touch points and preferences throughout the lifecycle
• Risk Analytics which can help to generate a credit risk score for identifying the potential if loan can be granted or not
• The social analytics provides the inner view for cross-selling opportunities which can help in preventing

Frauds by analysing the customers actions

• The analysis of customer interaction over multiple channels can help the bank to present some personalized offers based on events, date and trend in the actions performed in time interval taken into consideration
• The sensor data from IoT devices can be analysed for a better understanding of the customer behaviour pattern and can be helpful in giving recommendation for any particular action
• Different scope of cross-selling opportunities can be made based in the footprints on the bank’s website and spend pattern analysis of customers actions in banks
• An analysis can be done based on the offline interaction of the customer with the bank through the ATM data, credit or debit card transaction data by doing a deep lookup into the transactions and closely monitoring the monetary activities done by the customer, which can further be leveraged in making different business problem scenarios and its solution through analytics
• The data gathered from online and offline interaction with the bank and insurance organizations can be analysed for churn prediction, market basket analysis, increasing the customer life cycle, customer segmentation and making better recommendations based on customer actions
• The security analytics helps to provide a fraud-free environment, which helps in building the brand community which creates a healthy relationship between customers and company
• With the help of real-time analysis, insurers now can make daily adjustments to premium rates, premium strategies and underwriting limits by combining internal data for policies and regulations with external data from different media platform in order to optimize their finances and instant payments

Data Visualization and Reporting

The main goal of Data Visualization is to communicate information clearly and efficiently to users, involving the creation and study of the visual representation of considering 3Vs (Volume, Velocity and Veracity. The main purpose of visualization to make it viable to human recognition , it can be in different format including numerical and non numerical presented over some measures , it can be both homogenous and heterogenous , therefore visualization tools has been categorized into 4 different categories which are as follows-

Information Visualization tools- Tableau, Inforgram ,Charblocks Plottly4,, D3.js, Ember Charts6, Google charts7, FusionCharts, chart.js, Leaflet, Chartist.js, n3-charts , Sigma JS, Polymaps14, Processing.js are some of the dynamic information visualization tools available in various forms to show data through various barcharts, line charts, histogram , pie charts, word clouds and other important measures

Data Visualization tools – Timeline , Canvas, Commetrix, Cuttlefish, Cytoscape , Gephi, Graph-tool, Graphviz , JUNG, Keynetiq, Netlytic, NetMiner , Network Workbench, NodeXL , Pajek, Statnet , Tulip, Visone , Sentinel Visualizer are some of the dynamic visualization tools helpful in visualizing dynamic networks , geospatial mapping , graphs and domain specific visualization demands.

Business Intelligent and Visualization tools SocNetV and Sentinel Visualizer is a cross-platform, user-friendly tool for the analysis and visualization of Social Networks and Sentinel Visualizer is used for Advanced Link Analysis, Data Visualization, Geospatial Mapping, and SNA. Its database driven data visualization platform which lets the user quickly see multi-level links among entities and model different relationship types.

Scientific Visualization tools – helps in visualizing the navigation for distributed system with high scalability