The Banking Industry has witnessed a huge transformation during the last decade due to technological changes. The advent of information systems (IT) since 1980s, 1990s and early 2000s changed the banks in terms of application of IT in different business process form various perspectives such as cost reduction, revenue generation, fraud detection security etc. Similarly, analytics in banking is also meant for transforming such institution.
Financial metrics and KPIs provide effective measures for banks' overall performance. Banks need to move beyond standard business reporting and sales forecasting, in order to reach their strategic goals. Datamining, multivariate descriptive analytics and predictive analytics help banks to extract intelligent visions and quantifiable predictions that covers all types of customer behaviors such as account opening and closing, transactions, defaults if any and customer exit. With the continuous increase in competition, regulatory changes, fraud and cyber security threats, banks face tremendous pressure to improve operating efficiencies and grow wallet shares to sustain in the market. So, analytics is found to be the solution for better operating efficiency and proper customer engagement and also in mitigating risk as well as optimizing the deployment and utilization of banks resources.
Big Data Analytics:
Big Data Analytics is an extremely huge and varied data sets which are handled, analyzed, managed and validated through data management tools to make informed decisions. The data sets may be unstructured, semi-structured and structured; metadata from internet, social media data; web browser history and responses to surveys; machine data from Internet of Things (IoT) etc. which are in the form of five V's such as Volume, Velocity, Variety, Veracity and Value. Analytics is an encompassing and multidimensional field which uses mathematics, statistics, predictive modeling and machine learning techniques to find the meaningful patterns and have knowledge of the data so recorded.
Banking Industry comprises of enormous transactional data that is required to be properly managed, scrutinized, evaluated and utilized for the benefit of the banks and its customers.
Analytics in Banking:
Due to technological advancement there is no much interaction of customers and bankers at least to ensure that the current customer is well satisfied with their services so as to retain them. For banks customer acquisition is costlier than retaining old ones. Customers may be requiring varied services such as discounts on purchases, convenience, simplified home buying, personalized services, information and alerts etc. The traditional tools are not sufficient to process the data for all types of decision-making. Hence, banks are using data analytics in an efficient manner so as to enhance their customer value with better and faster decisions and also to maximize their revenue.
Benefits of Analytics in Banking:
There are several benefits of Analytics in Banking, like,
* Better understanding of customer behavior and responding to changes in preferences faster.
* Meeting regulatory requirements and addressing the setbacks on real time basis
* Improved product design and overall product portfolio optimization
* Increase transparency
* Develop a risk adjusted view of performance
* Manage fraud effectively
* Measure customer and product profitability
* Identify high potential prospects and customers
* Improve the ability to target products and services to prospective customers
* Enhance specific elements of the offer like product pricing, channeling etc.
Stages of Banking Analytics:
The basic aim of Banks is to acquire customers, retain and finally develop them. For this they go with the sentiment analysis, 360-degree customer analysis along with customer segmentation, best offers for them product management and design targeted marketed programs to reach them. These activities are supported by data analytics. This involves a series of stages of maintaining data and processing them to reach the informed decisions of the bankers at regular intervals.
This involves building data warehouse and report the current situation. Here only raw information is gathered which is both structured and unstructured and which is collected from various sources.
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