Assignment 1 Big Data and Data Analytics

                                           
Big data implementation in the banking industries
·       Objective
The financial industry is one of the most data driven of industries. At the end of 2012. It was estimated that financial and securities organisation were managing 3.8 petabytes of data per firm. Data sets have grown immensely in terms of size, type and complexity and are difficult to work on using traditional database management tools. Many large financial and banking institution are reaching the upper limits of their legacy system and are now seeking fresh analytics and framework solution

·       Problems
Regulators are demanding greater transparency, customers want a more relevant and personalized experience, and CEOs are looking for sustainable growth opportunities. A common thread in all of these issues is data. Big data. What is big data? Forrester puts it succinctly in saying “big data encompasses techniques and technologies that make capturing value from data at an extreme scale economical”.

·       Solution
Big data is all about liberating information from variable sources and formats, which is large in volume and broad in variety, in order to become more efficient and gain valuable insight to maximize opportunities. The keyword is ‘value’. Technology silos and ever-expanding amounts of data make effective data management a massive challenge. Banks have an abundance of data, but few are able to know their customers by analysing that data. For some banks their survival depends on it, for others that can indeed capture value, it extends their capabilities to a more sophisticated level of risk management, marketing optimization, and sales efficiency, which in turn gives them an advantage over their competitors.

·       Methodology
Big Data technology has four key aspects Infrastructure Data Storage Data Processing and Management, and Data Analytics

Infrastructure: The key to big data infrastructure is scalability and flexibility to handle petabytes of data, so the cloud becomes a natural choice
Data Storage: Traditional, legacy systems and methods of storage are sub- optimal due to price and scalability restrictions. New methods of storage, particularly NoSQL and DFS (Distributed File System) represent the paradigm shift in the storage arena.
Data Processing and Management: This framework is used for processing a growing mass of data in parallel.
Data Analytics: This is the area which provides visualisation and predictive analytics.

·       Measurement

Retail banking cases
As with many large banks that have had to go through acquisitions that further muddle traditionally siloed banking operations, A large US retail bank faced the challenge of aggregating all available information about a single customer. Information about checking accounts, mortgages and wealth management for the same individual were stored in different information management systems, preventing it from leveraging analytic capabilities that could have helped account representatives provide customers with better service and understand cross-sell opportunities. Banks will also be able to offer lower interest rates by utilising Big Data to reduce credit card fraud risks, thereby reducing overheads

Corporate banking use cases
JP Morgan Chase generates a vast amount of credit card information and other transactional data about its US-based customers. Along with publicly-available economic statistics from the US government, JPMorgan Chase uses new analytic capabilities to develop proprietary insights into consumer trends, and in turn offers those reports to the bank’s clients. The Big Data analytic technology has allowed the bank to break down the consumer market into smaller segments, even into single individuals, and for reports to be generated in second

Citi, for its part, is also experimenting with new ways of offering commercial customers transaction data aggregated from its global customer base, which can be used to identify new trade patterns. The data could, for example, reveal indicators of what might be the next big cities in the emerging markets. According to an executive, who manages internal operations and technology at Citi, the bank shared such information with a large Spanish clothing company, which was able to determine where to open a new manufacturing facility and several new outlets

Another set of data analytics currently used by the Bank is the matching algorithm, which enables the business to gain greater visibility on its performance. Other data analytic abilities include profiling of data to identify abnormal information through rule-based algorithms, or “teaching” a machine what is abnormal and normal so it can quickly flag errors and minimise false positives, which is mainly applied in activity monitoring and anti-money laundering processes.. In terms of reporting capabilities, reports can now be generated from the data source itself, where the data is stored without having to create dedicated reporting systems.

·       Accuracy
Overall, 62% of banks believe that managing and analysing big data is critical to their success. However, only 29% report that they are currently extracting enough commercial value from data. This means there is significant upside, and it may be some time before Big Data becomes part of the standard business dynamic. The technical key to successful usage of Big Data and digitisation of business processes is the ability of the organisation to collect and process all the required data, and to inject this data into its business processes in real-time - or more accurately, in right-time.

Therefore, rather than jump into Big Data blindly, a more pragmatic approach would be to test the water first, prioritise investments and use this process to  determine how prepared the organisation is for a Big Data transformation, as well as how fast and how deep it can go. Ask the right questions, focus on business problems and, above all, consider multiple insights to avoid any Big Data traps or pitfalls.

 



 REFFERENCES
  • httpn://onetree-solutions.com/big-data-in-the-banking-sector
  • http://spotfire.tibco.com/blog/?p=12860
  • http://www.marketresearchreports.biz/sample/sample/20704
 

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