Why Banks Need Data Science?
The financial crisis of 2008 was the result of speculating future without applying any analytics and staking too much on assets which were bound to deplete in value. This is the reason why banks became one of the earliest adopters of Data Science techniques for processing and security so as to prevent such situation from occurring again in future. Banks collect data from both internal sources i.e. credit card info, accounts, clients’ history etc, and also from external sources i.e. as internet banking data, social media, mobile wallets etc. Managing all this data is challenging yet crucial in the areas of customer service, fraud detection, understanding customers’ sentiment etc.
Applications of Data Science in Banking
• Managing Customer Data: Banks collect a large amount of data from multiple sources and with machine learning algorithms to this data, they can learn a lot about their customers. They can understand their customers’ behaviors, social interactions, spending patterns etc. and apply the results in order to improve their decision-making.
• Customer Segmentation: Customer segmentation is important for using marketing resources efficiently and improving customer service. Machine learning has so many classifying algorithms such as clustering, decision-trees, regression which can help banks categorize their customer based on customers’ life-time-value, behaviors, shopping patterns etc.
• Personalized Marketing: Data analytics help banks utilize customers’ historical data and predict a particular customer’s response to new plans and offers. This way, banks can create multiple and efficient market campaigns and target the right customers at the right time.