FALL 2018
A research proposal submitted to the Chandaria School of Business in partial fulfilment of the requirements for the Degree of Masters in Business Administration (MBA).

FALL 2018

ABSTRACTThe study is an investigation into adoption of big data analytics in the banking sector in Kenya. Specifically the study aims to establish whether big data analytics has been adopted in the banking sector in Kenya, to examine the application of big data analytics in the banking sector in Kenya and to establish the effect of big data analytics on performance of banking sector in Kenya.

TOC o “1-3” h z u ABSTRACT PAGEREF _Toc526859016 h iiiCHAPTER ONE PAGEREF _Toc526859017 h 1INTRODUCTION PAGEREF _Toc526859018 h 11.1Background of the Study PAGEREF _Toc526859019 h 11.2 Problem Statement PAGEREF _Toc526859020 h 51.3 General Objective PAGEREF _Toc526859021 h 61.4 Specific objectives PAGEREF _Toc526859022 h 61.5 Significance of the Study PAGEREF _Toc526859023 h 71.5.1 Bank Managers PAGEREF _Toc526859024 h 71.5.2 Policy makers PAGEREF _Toc526859025 h 71.5.3 Academicians and Researchers PAGEREF _Toc526859026 h 71.6 Scope of the Study PAGEREF _Toc526859027 h 71.7 Definition of Terms PAGEREF _Toc526859028 h 81.8 Chapter Summary PAGEREF _Toc526859029 h 8REFERENCE PAGEREF _Toc526859030 h 9

CHAPTER ONEINTRODUCTIONBackground of the StudyThe amount of data been generated every day is growing exponentially and this comes from modern technologies and is referred to as Big Data. with the Big data refers to groups of data sets that have been combined such that their volume, variability and velocity (growth rate) make them difficult to be captured, stored and processed for analysis by conventional/ traditional technologies within a specific time-frame (Slack, 2012). Big data is characterized as being too big, moves with too much speed or does not fit into traditional database architecture structures (Dumbill, 2012). Big data in business has a variety of uses that make it fundamental in ensuring that financial institutions remain relevant in today’s competitive arena. Latest technology behind Big Data is Hadoop. Hadoop is a software ecosystem designed to allow the query and statistical analysis of large and semi-structured data. Hadoop’s ability and flexibility to handle increasingly complex data has unlocked new opportunities for extracting value and business insights from potentially massive amounts of organizational internal data (Davenport, 2012).

The development of big data analytics (BDA) technology has transformed business decision making process and results to be more data driven. BDA influences how businesses interact with customers by helping them build long term relationship, realize value and incorporate all sources of data (United Business Media, 2013). It is a technology that helps to receive and store data generated from a multiple of sources which include digital processes, social media, sensors and mobile devises and use analytics to gain useful insight on customer behavior. The advancement made in both storage technology and computing power has made it feasible to collect and store this data (Yan, 2013).

Big data analytics (BDA) is a holistic approach and system to manage, process and analyze huge amount of data in order to create value by providing a useful information from hidden patterns to measuring performance and increase competitive advantages (Wamba, Gunasekaran, Akter, Ren, Dubey & Childe, 2017; Elgendy & Elragal, 2014). BDA can also be technologies that are concerned with how to create insightful trends in business intelligence (Chen, Chiang & Storey 2012; Russom, 2011). Gupta and Chaudhari (2015) believed that the future can be predicted by analyzing big data which is classified as an important factor to society and business. Banking industry generates a huge volume of data on a day to day basis. To differentiate itself from the competition, banks are increasingly adopting big data analytics as part of their core strategy. Analytics will be the critical game changer for the African banks in future. 
Yiu (2012) states that organizations gain a competitive advantage and improved organizational performance due to using the tools and technologies for analyzing their big data. BDA also helps organizations strengthen customer relationship management, enlightening the management of their operations and the operational efficiency risk which has a big impact in their performance (Kiron, 2013). BDA is predicted to have remarkable effects inside an assortment of industries such as main retail organizations which are currently forcing big data abilities to develop the customer knowledge, and decrease fraud (Tweney, 2013). BDA is predicted to cut the cost of operation (Liu, 2014). Moreover, Davenport, Barth and Bean (2012) and Wilkins (2013) mention that BDA play a role in strengthening businesses. The high operational and strategic potential of BDA give it a major role to enable an enhanced business efficiency and effectiveness. According to the study in (Chen and Zhang, 2014), several important advantages for business can be attained when adopting and using big data and analytics such as increasing operational efficiency, informing strategic direction, developing better customer service, identifying and developing new products and services, identifying new customers and markets.

Big Data Analytics in banking sector helps early detection cases of high risk accounts thereby helping in risk management thereby by reducing cases of frauds and defaults. It also helps in increasing efficiency in terms of dealing with fraud cases. Urban (2014) found that big data analytics have become an essential part of any strategy to help detect and prevent financial crime, owing to the ever evolving attack methods used by criminals exploiting multichannel vulnerabilities to compromise technology systems. Big data has enabled banks to implement real time analytics on a large scale to meet the growing threats. Kumar (2015) states that fraud has caused more than $1.744 billion in losses annually while the banking industry continues to spend millions each year on technologies aimed to reduce fraud and retain customers. The author proposes a comprehensive fraud detection technique that detects both known and novel fraud instances as they occur in real time, with a higher level of accuracy using distributed Hadoop-based platforms that make it possible to cost-effectively and efficiently store and pro-cess large data sets.

Pramanick (2013) states that banks are always at risk of losing customers and need strategies that are dependent in identifying the right action to the right customer. Thus banks should invest in customer analytics that effectively segment their customers. This will assist in determining pricing, products and services, the right customer approach and marketing methods. Morabito (2015) adds that big data enabled marketing automation will assist banks in servicing individual customer needs while keeping the marketing costs low, enabling a personalized experience at a good return on investment.

A major US bank reduced its loan default calculation time for a mortgage book of more than 10 million loans from 96 hours to just four. JP Morgan Chase an American multinational investment bank and financial services company generates a vast amount of credit card information and other transactional data about its US-based customers. 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 seconds (Gandomi & Haider, 2015). In India Verma (2017) found that firm’s intention to adopt BDS can be positively affected by the quality and benefits of BDS. Surprisingly, a firm’s absorptive capacity in utilizing big data and risks and costs associated with implementation and maintenance does not impact the adoption intention of BDS. Deutsche Bank a German investment bank and financial services company uses Big Data and has been performing well by making significant investments across all areas of the Bank. Deutsche Bank currently has multiple production Hadoop platforms available through Open Source, enabling a decreased cost in terms of data processing (Shin, 2014). Capgemini Consulting (2014) reports that 60% of financial institutions in North America believe that big data analytics offer a significant competitive advantage and 90% think that successful big data initiatives will define the winners in the future. However, only 37% of banks have hands on experience with live big data implementations, while the rest are still focusing on pilots and experiments.

According to Miemoukanda (2017) over the past few years, organizations in South Africa have started to realize the potential of Big Data and analytics, and this rising awareness helps to accelerate the adoption of these technologies across all verticals in the coming years. However, limited IT budgets and the dearth of skilled resources impede Big Data and analytics initiatives across organizations in the country. Organizations in South Africa would consider internally developing skills by sharing resources, undertaking training programs, and partnering with vendors. South Africa’s Nedbank Market Edge product enabled its corporate merchant clients to access data about their customers buying patterns which helped them in increasing their overall profitability. In Tanzania with 72 per cent of the population making use of mobile banking which generates lot of data about customer’s financial transactions. Banks are using clients’ mobile money statements to verify their financial position in order to provide them loans.

In Africa, the most visible cases of big data use have been in Kenya, where the number of bank accounts opened between 2007 and 2012 has grown four-fold, and this has largely been as a result of mobile money banking (Stefanski, 2012). The result is that more and more data is being churned out. The International Data Group (IDG) conducted a research that concluded that Kenya is definitely moving towards the big data revolution through the inception of big data projects and partnerships with major multi-national companies with similar interests (Tredger, 2013).

There are 42 registered commercial banks in Kenya (CBK, 2013). Commercial banks undergo a series of challenges due to the nature of their business – handling large sums of money (Zikopoulos, Eaton, Deutsch, Deroos ; Lapis 2011). Government regulations provide a challenge to commercial banks. The Republic of Kenya’s banking industry is governed by the Companies Act, the Central Bank of Kenya Act and the Banking Act Laws of Kenya Chapter 488 (Government of Kenya, 2011). The bill capping interest rates in Kenya that was signed by the president dealt a major blow to commercial banks across the country. The high interest rates that commercial banks used to charge have been scrapped resulting in less income for them and obviously slower growth in terms of their asset base (Daily Nation, 2016). Fraud is a major challenge that hampers progress in the industry. Big data analytics would be of great benefit in building fraud models to curb this menace (Zikopoulos et al., 2011). The non-detection of the fraud itself, lack of sharing the data with other banking as well as not sharing information about fraud with the general public are some of the reasons that allow criminals to get away scot-free and become repeat offenders (Wahito, 2015).

1.2 Problem Statement
The big data revolution happening in and around 21st century has found a resonance with banking firms, considering the valuable data they’ve been storing since many decades. This data has now unlocked secrets of money movements, helped prevent major disasters and thefts and understand consumer behaviour. Banks reap the most benefits from big data as they now can extract good information quickly and easily from their data and convert it into meaningful benefits for themselves and their customers (Barker, D’Amato & Sheridon, 2011). Banks internationally are beginning to harness the power of data in order to derive utility across various spheres of their functioning, ranging from sentiment analysis, product cross selling, regulatory compliances management, reputational risk management, financial crime management and much more (Dhar, 2014).
Big data management and analytics is a very crucial tool in risk management in banking sector however, utilization of big data analytics has challenges. Volume is a one of the challenges of utilizing big data analytics and it translates to the amount of data that is available. The challenge is how to maximize the use of all the data. Lack of enough skilled personnel to carry out the data analytics plus testing the accuracy of the data itself is challenges as well (Krishnan, 2013). Getting the right technology to carry out data analytics is a costly exercise and puts an added strain on a firm’s budget (McAfee ; Brynjolfsson, 2012). With so many different types of data available managing sheer volume of data is one of the biggest challenges for banking industry. Again, the challenge makes itself apparent when trying to sort through data that is useful and data that is not. Banks now have to filter through much more data to identify fraud. Analysing traditional customer data is not enough as most customer interactions now occur through the Web, mobile apps and social media. To gain a competitive edge, banks need to leverage big data to better comply with regulations, detect and prevent fraud, determine customer behaviour, increase sales, develop data-driven products and much more. Major issue related to data analytics in developing countries concerns over the relevance of the data in, its representativeness, its reliability as well as the overarching privacy issues of utilizing personal data.

A study on big data analytics indicates that 91% of fortune 1,000 companies in the world have invested heavily on big data analytics in order to ensure that they remain ahead of the competition (Kiron et al., 2014). The Common Wealth Bank of Australia uses big data analytics for customer risk assessment by looking at the cash flow performance of its clients, allowing them to offer advice on the best way to mitigate their risks (Eyers, 2014). Chinese ecommerce company, ‘Alibaba’ realized that added security measures were required after carrying out big data analytics on user accounts that had been hacked. The company introduced five verification stages that an online customer has to pass in order to be allowed to proceed with their purchase (Kaushik, 2016). Malaka and Brown (2015) evaluated the challenges to the Organisational adoption of big data analytics: a case study in the South African Telecommunications Industry. Nwanga, Onwuka, Aibinu and Ubadike (2015) studied the impact of big data analytics to Nigerian mobile phone industry. Ochieng (2015) did a study on the adoption of big data analytics by supermarkets in Kisumu County. From the empirical literature it is evident that there is limited literature on big data analytics in Kenya. With the challenges facing data analytics and the limited research done the study will investigate the adoption of big data analytics in the banking sector in Kenya.

1.3 General ObjectiveThe general objective of this study is to investigate the adoption of big data analytics in the banking sector in Kenya
1.4 Specific objectivesThe research objectives will be;
1.4.1 To establish whether big data analytics has been adopted in the banking sector in Kenya
1.4.2 To examine the application of big data analytics in the banking sector in Kenya
1.4.3 To establish the effect of big data analytics on performance of banking sector in Kenya
1.5 Significance of the StudyThe study will be important to the following stakeholders;
1.5.1 Bank ManagersThe findings of the study will provide an understanding about big data analytics. These include the importance, usage and challenges of big data analytics. Bank managers can make use of the findings to guide them to adopt big data analytics so as to improve their organization performance.

1.5.2 Policy makersThe policy makers in the banking industry will also benefit from the study. They will understand the importance of big data analytics in the banking industry. They will be able to formulate policies that encourage the adoption of big data analytics. This will promote the growth of the banking industry
1.5.3 Academicians and ResearchersThe study will add to the body of knowledge on adoption of big data analytics in the banking sector in Kenya. The study can be used by researchers and academicians as a reference in their future studies. Other studies can be based on this research.

1.6 Scope of the StudyThe study is an investigation into adoption of big data analytics in the banking sector in Kenya. The study will be conducted in commercial banks in Nairobi County. The specific objectives are to establish whether big data analytics has been adopted in the banking sector in Kenya, to examine the application of big data analytics in the banking sector in Kenya and to establish the effect of big data analytics on performance of banking sector in Kenya. The target population will be management level employees in the commercial banks. The study will be conducted between October 2018 to February 2019.

1.7 Definition of TermsBanking sector: Institutions of finance that accept money from the public that is deposited and paid back, at the end of a time-frame that is fixed. Banks also use funds held after being deposited or part of the amount by investing, lending it out, or using it for a variety of alternative legal ways for purposes of the account, at the risk of the person making use of the money (Central Bank of Kenya, 2013).

Big data analytics: It is a term encompassing the new methods, tools and technologies for collecting, managing and analysing in real-time the vast increase in both structured and unstructured data for insightful and effective decision making.

Big data: It is an aggregate of data sets that are large and complex, thus overwhelming the traditional data mining tools.

1.8 Chapter SummaryChapter one has discussed the background information of the study, the statement of problem, general objective of the study, specific objectives, significance of the study and definition of terms. Chapter two will cover the literature review; it will include the theoretical review, the empirical review and the conceptual framework.

REFERENCEBarker, K.J., Jackie D’Amato, J., & Sheridon, P. (2011). Credit card fraud: awareness and prevention, Journal of Financial Crime, 15(4), 398-410.

Capbgemini Consulting. (2014). Big data alchemy: How can banks maximize the value of their customer data? Capgem-ini Group.

Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4), 1165-1188
Chen, S. M., & Tan, J. M. (1994). Handling multi-criteria fuzzy decision-making problems based on vague set theory. Fuzzy sets and systems, 67(2), 163-172.
Davenport, T. H. (2012). Enterprise analytics: Optimize performance, process, and decisions through big data. Upper Saddle River, New Jersey: FT Press Operations Management.

Davenport, T. H., Barth, P., & Bean, R. (2012). How ‘big data’is different. MIT Sloan Management Review, 54, 43–46.
Dhar V. (2014). Big Data. Management Journal, 2(2): 65-67.

Dumbill, E. (2012). Planning for Big Data. A CIO’s Handbook to the Changing Data Landscape. O’Reilly Media, Inc.

Elgendy, N., & Elragal, A. (2014, July). Big data analytics: a literature review paper. In Industrial Conference on Data Mining (214-227). Springer, Cham.
Gandomi A & Haider M (2015). Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manag, 35(2):137–144
Gupta, S., & Chaudari, M. S. (2015). Big Data issues and challenges. International Journal on Recent and Innovation Trends in Computing and Communication, 3(2), 062– 066.
Kiron, D. (2013). Organizational alignment is key to big data success. MIT Sloan Management Review, 54, 1.
Kumar, N. (2015, March 13). New age fraud analytics: Machine learning on Hadoop. Retrieved from, Y. (2014). Big data and predictive business analytics. The Journal of Business Forecasting, 33, 40–42.

Malaka, I. & Brown, I. (2015). Challenges to the Organisational adoption of big data analytics: a case study in the South African Telecommunications Industry. Annual Research Conference on South African Institute of Computer Scientists and Information Technologists, Article No. 27
Miemoukanda, M. (2017). The State of Big Data and Analytics in South Africa: Driving Digital Transformation. Market Perspective – Doc # CEMA42083917
Morabito, V. (2015). Big data and analytics: Strategic and organizational impacts. Springer.

Nwanga, M.E., Onwuka, E.N., Aibinu, A.M. & Ubadike, O.C.(2015). Impact of Big Data Analytics to Nigerian Mobile Phone Industry. Proceedings of the 2015 International Conference on Industrial Engineering and Operations Management Dubai, United Arab Emirates (UAE).

Ochieng, G.O. (2015). The Adoption of Big Data Analytics by Supermarkets in Kisumu County. Unpublished Thesis, University of Nairobi.

Pramanick, S. (2013). Analytics in Banking Services. Retrieved from IBM Big data analytics hub website
Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19, 40.
Shin, D. (2014). A socio-technical framework for internet-of-things design. Telematics and Informatics, 31(4), 322–339.

Slack, E. (2012). What is big data? Retrieved from http://www.storageswitzerland. com/Articles/Entries/2012/8/3_What_is_Big_Data.html
Tweney, D. (2013). Walmart scoops up Inkiru to bolster its ‘big data’ capabilities online online. Available
United Business Media (2013). Acquire, Grow & Retain Customers: The Business Imperative for Big Data & analytics
Urban, M. (2014, December 18). Big data analytics in the fight against financial crime. Retrieved from, S. (2017). The Adoption of Big Data Services by Manufacturing Firms: An Empirical Investigation in India. Journal of Information Systems and Technology Management 14(1), 39-68.
Wahito, M. (2015). Insurance firms lost Sh324million to fraud in 2015 November 30. Retrieved 2015, from sh324million-to-fraud-in-2015/Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
Wilkins, J. (2013). Big data and its impact on manufacturing online. Available http://
Yan, J. (2013). Big Data, Bigger Opportunities Data. Government’s roles: Promote, lead, contribute, and collaborate in the era of big data. London, Mc Grawhill.
Yiu, C. (2012). The big data opportunity: making government faster, smarter and more personal. Policy exchange (London).
Zikopoulos P., Eaton C., Deutsch T., Deroos D., ; Lapis G. (2011). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw Hill Professional.