Big data analytics has been a buzzword in the finance industry for quite some time now. It is the process of examining large and complex data sets to uncover hidden patterns, correlations, and insights that can be used to make better business decisions. The finance industry is one of the sectors that can benefit the most from big data analytics. In this article, we will discuss a case study of how big data analytics has been used in the finance industry to improve business operations.
The case study we will be discussing is about a large financial institution that wanted to improve its customer service. The institution had a vast amount of data, but it was not being used effectively. The data was scattered across different systems, and it was challenging to analyze it in a meaningful way. The institution decided to implement a big data analytics solution to address this issue.
The first step was to gather all the data in one place. The institution used a data lake to store all the data. A data lake is a centralized repository that allows for the storage of structured and unstructured data at any scale. The data lake allowed the institution to store all its data in one place, making it easier to analyze.
The next step was to clean the data. The institution had a lot of duplicate and incomplete data, which could have skewed the results of the analysis. The data was cleaned using data cleansing tools, which helped to remove duplicates and fill in missing data.
Once the data was cleaned, the institution used big data analytics tools to analyze the data. The tools used included machine learning algorithms, predictive analytics, and data visualization tools. The machine learning algorithms were used to identify patterns in the data, while predictive analytics was used to forecast future trends. Data visualization tools were used to present the data in a meaningful way.
The analysis revealed that the institution’s customer service was not meeting the expectations of its customers. The analysis showed that customers were experiencing long wait times when calling the customer service center. The analysis also showed that customers were not getting their issues resolved on the first call.
The institution used the insights gained from the analysis to improve its customer service. The institution implemented a chatbot that could handle simple customer queries, reducing the number of calls to the customer service center. The institution also implemented a system that allowed customers to schedule a call back, reducing wait times. The institution also trained its customer service representatives to handle more complex queries, reducing the need for customers to call back.
The results of the implementation were impressive. The institution saw a significant reduction in the number of calls to the customer service center. The wait times were reduced, and customers were getting their issues resolved on the first call. The institution also saw an increase in customer satisfaction ratings.
In conclusion, big data analytics has the potential to revolutionize the finance industry. The case study discussed in this article shows how big data analytics can be used to improve customer service in the finance industry. The implementation of a big data analytics solution allowed the institution to gather all its data in one place, clean the data, and analyze it using machine learning algorithms, predictive analytics, and data visualization tools. The insights gained from the analysis were used to improve customer service, resulting in a significant reduction in the number of calls to the customer service center, reduced wait times, and an increase in customer satisfaction ratings.