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
 

Komentar

Posting Komentar

Postingan populer dari blog ini

 Application of ICT use in schools in Nigeria  The adoption and use of ICTs in schools have a positive impact on teaching, learning, and research. Despite the roles ICTs can play in education, secondary schools in Nigeria have yet to extensively adopt them for teaching and learning. Efforts geared towards integration of ICTs into the secondary school system, have not had much impact. Problems such as poor policy and project implementation strategies, and limited or poor information infrastructure militate against these efforts. In order to ensure that ICTs are widely adopted and used in Nigeria's secondary school system, the following efforts should be taken. Government should ensure that ICT policy statements are translated into reality. An ICT policy implementation commission should be created. This commission should be funded and given the power to provide ICT facilities in the schools and monitor their use. All secondary schools should be made beneficiarie
Baca selengkapnya

assignment 6 comparison of all 4 method

Decision tree C4.5 C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. C4.5 is an extension of Quinlan's earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier. Authors of the Weka machine learning software described the C4.5 algorithm as "a landmark decision tree program that is probably the machine learning workhorse most widely used in practice to date". advantages: •  Build models that could easily interpreted •  Easy to implement •  It can use categorical and continuous values •  Deals with noise disadvantages: •   Small variation in data can lead to different decision trees (especially when the variables are close to each other in value) •  Does not work very well on a small training set Naive Bayes In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on
Baca selengkapnya

BIG DATA FINAL ASSIGNMENT - ABALONE SIZE COMPARISON USING BIG DATA

Gambar
Background Abalone is a sea creature that many hunt for their meat and their shell. many has said that Abalone taste like no other sea creature the meat of an Abalone taste so good that many people want it and even people also use the shell of an Abalone as a jewellery, but because they usually lived in the seabed its hard for sailors to even hunt them and this makes Abalone so expensive and many people don't even know this creature exists the goal of this research is to introduce what is abalone and to describe what are the difference between immature, male & female Abalone using big data   Introduction Abalone are marine snails. Abalone vary in size from 20 millimeters (0.79 in) ( Haliotis pulcherrima ) to 200 millimeters (7.9 in) while Haliotis rufescens is the largest of the genus at 12 inches (30 cm). The shell of abalones is convex, rounded to oval in shape, and may be highly arched or very flattened. The shell of the majority of species has a small, flat spi
Baca selengkapnya