Using data science in the banking industry is more than a trend, it has become a necessity to keep up with the competition. Banks have to realize that big data technologies can help them focus their resources efficiently, make smarter decisions, and improve performance.
Here is a lit of data science use cases in banking area which we have combined to give you an idea how can you work with your significant amounts of data and how to use it effectively.
Fraud detection
Machine learning is crucial for effective detection and prevention and fraud involving credit cards, accounting, insurance, and more. Proactive fraud detection in banking is essential for providing security to consumers and employees. The sooner a bank detects fraud, the faster it can restrict account activity to minimize loses. By implementing a series of fraud detection schemes banks can achieve necessary protection and avoid significant loses.
Since every data set is different, each requires individual training and fine-tuning by data scientists. Transforming the deep theoretical knowledge into practical applications demands expertise in data-mining techniques, such as association, clustering, forecasting, and classification.
Managing customer data
Banks are obligated to collect, analyze and store massive amounts of data. But rather than viewing this as just a compliance exercise, machine learning and data science tools can transform this into a possibility to learn more about their clients to drive new revenue opportunities.
Nowadays, digital banking is becoming more popular and widely used. This creates terabytes of customer data, thus the first step of data scientists team is to isolate truly relevant data, thus the first step of data scientist tea is to isolate truly relevant data. After that, being armed with information about customer behaviors, interactions, and preferences, data specialists with the help of accurate machine learning models can unlock new revenue opportunities for banks by isolating and processing only this most relevant clients’ information to improve business decision-making.
Risk modeling for investment banks
Risk modeling is a high priority for investment banks, as it helps to regulate financial activities and plays the most important role when pricing financial instruments. Investment banking evaluates the worth of companies to create capital in corporate financing, facilitate mergers and acquisitions, conduct corporate restructuring or reorganizations, and for investment purposes.