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 list 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.
Machine Learning is crucial for effective detection and prevention of fraud involving credit cards, accounting, insurance, and more. Proactive fraud detection in banking is essential for providing security to customers 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.
An example of efficient fraud detection is when some unusually high transactions occur and the bank's fraud prevention system is set up to put them on hold until the account holder confirms the deal. For new accounts, fraud detection algorithms can investigate unusually high purchases of popular items, or multiple accounts opened in a short period with similar data.
Banks are obliged 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. 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 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.
That's why risk modeling appears extremely substantial for banks and is best assessed with more information in hand and data science tools in reserve. Now, through the power of Big Data, innovators in the industry are leveraging new technology for effective risk modeling and therefore better data-driven decisions.
The key to success in marketing is to make a customized offer that suits the particular client’s needs and preferences. Data analytics enables us to create personalized marketing that offers the right product to the right person at the right time on the right device. Data mining is widely used for target selection to identify the potential customers for a new product.
Data scientists utilize the behavioral, demographic, and historical purchase data to build a model that predicts the probability of a customer’s response to a promotion or an offer. Therefore, banks can make an efficient, personalized outreach and improve their relationships with customers.
Customer lifetime value (CLV) is a prediction of all the value a business will derive from their entire relationship with a customer.
