Data science is already changing how the finance world works. The financial sector is a hub of data; therefore, the application of data science techniques has been revolutionary for the field. This application has made major changes in fraud prevention, risk management, credit allocation, and customer analytics. By updating from traditional rule-based models that flagged unusual singular transactions to machine learning algorithms that correlate user behavior and their likelihood of fraud, detection of actual fraud has increased, and false positives have decreased, thus overall improving the efficiency of fraud detection. In risk management, being able to consider and analyze big sets of data in allows for better identification of and planning for risk. As we’ve already seen in class in articles like Joshua Blumenstock’s “Don’t Forget People in the Use of Big Data for Development”, there has been success in using different data science techniques for credit allocation. Using customer analytics, firms can access consumer demographics and purchase behavior, which can then be used to understand market trends and make predictions. These are only a few examples of how data science is already being used in finance, and new techniques are being put in use everyday.