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 ie credit card info, accounts, clients' history etc, and also from external sources ie as internet banking data, social media, mobile wallets etc. Managing all this data is challenging yet cruel 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.
• Lifetime Value Prediction: Data Science techniques provide better insight into clients' acquisition and attrition, usage of banking products, and other investments etc, and help banks assess the lifetime value of a customer. This way banks can identify their profitable customers and strive to create a better relationship with them.
• Risk Modeling: Investments are all about minimizing risks, and this can be achieved by assessing more information through Data Science tools. Banks are now leveraging on new technology for better prediction of market trends and decision-making.
• Fraud Detection: Banks are obligated to safeguard themselves and their customers against fraudulent activities. Utilizing machine learning algorithms can help to and prevent frauds related to credit cards, insurances etc. With predictive and real-time analysis, banks can predict the anomalies in spending or withdrawals that can lead to fraud and can take actions in advance.
Banks Need Data Science
There's no denying that applications of Data Science, Machine Learning and Artificial Intelligence is increasing at a rapid speed in the financial world. With more and more people getting financially educated and taking interests in banking systems, the amount of data is exploding at an exponential rate, and banks need Data Scientists in large numbers to help them with the job.
How Can You Become a Financial Data Scientist?
Data Science is a challenging yet exciting field of study. Thorough knowledge of mathematics, computer science and business is imperative in order to find the job of a Data Scientist. Keeping this in mind, the training has been designed to cover all the concepts and tools applied in Data Science with lifetime access to videos and numerous webinars. Multiple evaluations and projects not only test what students have learned, but also prepare them to work in the real banking environment.