How is Data Science Used in Banking?
Traditionally, old-fashioned banking included using checks for payments or making deposits through bank visits. In the present day, with the growing popularity of digital banking, each action could be made much more efficiently with the help of new technologies.
However, the consequences of these innovations now include the growth of theft, fraud, and other crimes. In response to this, banks and financial institutions have adopted extensive measures using data science to ensure that customer’s financial data are safe.
Roles of Data Scientists in Banking
Fraud Detection → Data Scientists utilize data science and machine learning techniques to detect and prevent fraudulent activities. By analyzing consumer data, banks are able to catch discrepancies when fraudsters attempt to make purchases with stolen cards.
Consumer Data → By collecting and analyzing big consumer data, banks are able to vastly improve their decision-making. Some examples of crucial consumer data include transactions, purchase histories, credit card statements, demographics, and payments.
Risk Assessment → One of the most important tasks of banks as lenders is to assess the risks of borrowers. Instead of meticulously analyzing and calculating individual risk scores, data scientists at financial institutions are able to detect hazardous assets and provide more insightful financial advice to borrowers.
Consumer Lifetime Value (CLV) → Many banks utilize consumer lifetime values to better maintain mutually beneficial relationships. By utilizing data models and analytics, banks are able to better understand customers and create customer retention strategies.
Recommendation Engines → Data Scientists use data and machine learning algorithms to identify customer profiles to create personalized recommendations and avoid the repetition of offers.
How Large Financial Institutions Utilize Data
Large banks like JP Morgan Chase utilize staggering amounts of data. In fact, Chase utilizes more than 150 petabytes of data holding approximately 3.5 billion users. With this amount of seemingly endless data, JP Morgan utilizes data models and analytics tools like ‘Hadoop’ to sort through and make use of the data. Without data science, it would be impossible for large financial institutions like Chase to analyze large amounts of data cohesively.
Conclusion:
Banks and financial institutions can better excel in customer service, prediction and profiling, marketing, and many other areas with the help of data science techniques. Data science plays crucial roles across many industries, making it one of the most lucrative skills to have.
