Sample Statistics Paper on Data Acquisition and Analytics

According to big Clarke (2016), data is a large volume of data in a field that extracts information and analyzes it in a systematic manner that could be cumbersome when dealt with by traditional software. Big data analytics have several benefits that include portfolio risk reevaluation, increasing customer loyalty as well as improving customer engagement, sorting out the real time issues, and identifying the root causes of failures. Some analyses are done on these data to create business value and find the patterns.

The data has helped to understand consumer behavior, prevent thefts and disasters. Besides, it has helped the banking firms to unlock the movement of money and extraction of good information in a quick and timely manner hence becoming meaningful to the consumers. Globally, banks are harnessing the power of data to various spheres such as financial crime management, regulatory compliance management, and reputational risk management.

The impact of big data on the financial sector is difficult to estimate as volumes of customer data (withdrawals, deposits) at ATMs, and purchases are done online. Enormous investment has taken place in data collection and processing. Several factors have led to the rise of big data in the current situation such as a change in customer expectations and behavior, a large amount of input data that have been contributed by technological evolutions, new regulatory pressure requiring banks to disclose more granular and diverse data to regulators and central banks. Also, cybersecurity is on the rise thus banks are required to protect the most valuable assets. Another contributory factor in the increase is the processing of diverse and complex data in real-time due to technological advances.

Big data is characterized by a vast quantity of data (petabytes or terabytes) which cannot be handled by data processing tools of the tradition. The velocity of data processing is quick since the data is required in real-time and multiple of different structured data is required to be availed in form of blogs, tweets, text, audio, video, or even another social status. Big data helps in the prevention and detection of cyber fraud, improves credit scoring from corporate and private customers through credit models, and improves the prevention and detection of fraud on new insurance policy (data not in reality matching). In cases of legal matters where volumes of data are required, it facilitates preparation and reaction to avoid fines and suctions (Hasham, Joshi, and Mikkelsen, 2019).

According to Guo, (2017) feedback processes help identify the gaps in services rendered for any organization and if done on regular basis. This helps to collect feedback from customers from those that have used the online platform and those that have visited the bank branches. Transaction nature is an important parameter in understanding the habits and needs of the consumers and is considered in credit and debit transactions, transfers and allowance received, and consumer behavior analysis (Mittal and Tyangi, 2020).  Through the use of transaction data, we can be able to determine the kind of financial products that can be sold to consumers by the banks. Customers perceive speed as a quality parameter whenever the speed of delivery is improved.

Through the use of consumer behavior analysis helps to improve the capacity of spending through the use of cards and we can be able to infer if an individual is a potential loan applicant. The data can be used to know if the person can be issued with a credit card or have an increased credit limit and if the person can be extended with more offers.

Across various spheres of the banking sector, big data is being implemented that helps to improve active and passive security systems that enable the delivery of better services to customers. The data mining technique can be used by the banks and extend to cover and improve analysis quality. The data has been obtained through the use of sentimental analysis and analyzed transactions from the banking sector where the bank can be able to strengthen any type of attack and data security.

 

 

 

References

Hasham, S., Joshi, S., & Mikkelsen, D. (2019). Financial Crime And Fraud In The Age of Cyber Security. McKinsey & Company.

Mittal, S., & Tyagi, S. (2020). Computational Techniques for Real-Time Credit Card Fraud Detection. In Handbook of Computer Networks and Cyber Security (pp. 653-681).

Springer, Cham. Guo, Y. (2017). Implementing relationship banking strategies and techniques and improving customer value. Finance and Market2(2).