Data science in banking sector
- 2. INTRODUCTION • Banking is an industry that handles cash, credit, and other financial transactions for individual consumers and businesses alike. • A bank is a financial institution that is licensed to accept checking and savings make loans. Banks also provide related services such as individual retirement (IRAs), certificates of deposit (CDs), currency exchange, and safe deposit boxes. • The implementation of Data Science in banking is changing the face of the industry rapidly. Each and every bank is searching for better ways that will understand the customers for increasing customer loyality by providing more operational efficiency.
- 3. • With Data Science in banking, Banks utilize the data from customer transactions, previous history, trends, communication, and loyalty • Extracting insights from such a large amount of data is a great challenge because this data is mostly unstructured which is difficult to deal with. • Various methods of data analysis like data fusion and integration, Machine Learning, Natural Language Processing, signal processing, etc. can be used for this this purpose. • Banks are using Data Science for performing various important tasks like Fraud detection, Customer Segmentation, etc.
- 4. Use Cases of Data Science in Banking 1. Fraud Detection Fraud Detection is a very crucial matter for Banking Industries. The biggest concern sector is to ensure the complete security of the customers and employees. So here plays a major role by monitoring and analyzing the different banking activities of the that they can detect any suspicious or malicious activity. The major steps included in Detection process are: * Collecting a large number of data samples for training and testing the model * Training the model for making predictions. * Testing the accuracy of the results and deployment. Data Scientists need to have their hands on various data mining techniques like clustering, classification, etc. just for working with different datasets and extracting meaningful insights that can be applied to real-time banking problems
- 5. 2 . Managing Customer Data Banks have massive datasets to manage, so, collecting, analyzing, and storing such an of data is difficult. Thus, various banking organizations are using various tools and Data Science and Machine learning just for transforming this data into such a format for knowing their clients better for devising new strategies for better revenue generation. different Machine Learning algorithms can help banks to derive new opportunities for generation and take some important data-driven decisions 3 . Risk Management Risk management in banks has changed substantially in the last decade as new The regulations have also gone stricter post-global financial crisis. The adoption of enabling new risk, management models. Machine learning technologies can identify nonlinear patterns in large volumes of data and help create models with higher data models also self-learn with every bit of every data and pattern to improve their power with time.
- 6. 4 . Marketing & Sales The key to success in marketing is to customize an offer that suits particular and need. Data science in banking can help create a personalized window for every dividing the data into demographical, geographical and historical data sets. These deeper insights regarding how a customer responds to an offer/promotion. Therefore, make personalized outreach to interact with customers. Machine learning helps in powerful recommendation engines that can create upsell/cross-sell opportunities for 5 . Real-time analytics High quality real-time predictive data analysis allows businesses that use its power changes in the market. Today, banks that do not skimp on the introduction of transactions, changes in credit ratings, new legislative initiatives, and thousands of affecting market conditions and react almost instantly.
- 7. 6 . Automation of communication with clients and the expansion of communication channels Mobile communications, social networks, e-mail, instant messengers - a must communicate with its customers through any channels convenient for true for financial institutions. Chatbots, electronic assistants, expert systems – today, a wide variety of interaction with the consumer allows you to relieve the company's staff from and increase communication efficiency.
- 8. Conclusion Different applications of Data Science in banking, we can say that Data Science is helping all the leading banking organizations. It helps in keeping up with the competition and providing better services to their customers. The methodologies and tools offered by data science can improve the accuracy of risk management, customer service quality, as well as automate and accelerate many business processes, increasing the overall efficiency of the company. To keep pace with the times and increase profitability, it is important to adopt new methods and algorithms for working with information on time.