The banking industry has changed a lot in the past decade. Financial organizations are looking to improve their processes and provide even better services to their customers. Luckily, banking is one of those industries where technology can help a lot.
No matter if we are talking about revenue allocation, product development, outreach, or risk management, there is a use for data science. This shows that data science application in banking isn’t just one of those trends that will pass quickly.
Banks need data science to stay competitive. It’s all about making better decisions, allocating resources the right way, and increasing performance results. Today, we are going to talk about six primary reasons why data science has become so essential in banking.
It Helps a Lot with Risk Modeling
Risk modeling is one of the top priorities for the whole industry. Through this process, banks can assess their performance through new strategies. One of the essential methods is credit risk modeling. It involves analyzing how a particular loan can be repaid.
When it comes to credits, there is always a chance that borrowers can’t repay their loans. However, it’s not easy to calculate this chance because many factors need to be considered. Risk modeling allows banks to develop a realistic lending scheme and default rates.
Data science and big data combined can be used by banks to find defaulters before loans get sanctioned in high-risk situations. Risk modeling can also be used for overall banking functions. With data analysis tools, banks can analyze performance and recognize critical areas that can be improved.
Makes It Easier to Detect Fraud
Data science has incorporated machine learning technology, which has the power to go through large volumes of data and spot desired patterns or irregularities. In banking, the only way to detect fraud is by monitoring the activity of users and recognizing harmful behavior.
At the same time, online banking, e-commerce, and the internet overall have made banking fraud even more prevalent. Data science incorporates the use of predictive analytics and machine learning in different tools that can be used for recognizing patterns and trends that resemble fraud.
Data is first gathered using some software. This data is then analyzed through various algorithms powered by machine learning. In the end, when potentially malicious patterns have been recognized, these tools can also be used to verify fraud and confirm illegal activity.
Improves Customer Data Management
Banks provide services to their customers. Customer accounts need to be analyzed, controlled, and managed properly. All of their information and transactions need to be stored. Managing customer data in an efficient way is crucial for a bank’s success.
However, with data science, not only can banks manage customer data in a better way, but they can also learn more about their customers through their data. Large volumes of customer data are useful sources of information if you know where to look and have quality technology that can do this.
This is where machine learning comes into play once again. Banks can learn how their customers interact, what their preferences are, and how they behave. This can help banks learn where their processes are cluttered, see which services their customers like the most, and even spot new revenue sources for the future.
Improves Financial Modeling Practices
Bank financial modeling is a story of its own. Other businesses either sell products or their services while banks use the money to make even more money. Banks get payments from one borrower only to lend that same money to someone else.
This means that banks have a lot of factors that they need to track to make their financial models accurate. With data science, they can be closer to reality, more precise, and robust. The focus of data science is to get insights out of data, and this is very useful for financial analysis and modal assumptions.
Data science also allows automation of various modeling processes and reduces errors. Furthermore, data science specialists use new software solutions that have far more advanced features than MS Excel, which is usually used for financial modeling.
Predictive Analytics in Real-Time
This type of analytics uses computations to predict events that will happen in the future. Predictive analytics relies on machine learning to get the desired results. The increased amount of data gives countless use cases that can be analyzed to get valuable predictions.
Real-time analytics is focused on helping customers recognize their current problems. On the other hand, predictive analytics can give suggestions on how specific issues can be dealt with. This can help develop banking apps that warn customers about their potential matters, tell them about negative trends, and so on.
On the other hand, these analytics can also be used internally to help improve banking processes. Banks can apply them to their own workflow to recognize potential issues and develop more robust strategies.
Doing Customer Segmentation
Customer segmentation was out of reach to banks for a long time. This marketing technique involves splitting customers into groups based on their characteristics, like age, location, behavior, and so on. Data science has many techniques that can be used for customer segmentation.
Some of them are logistic regression, decision trees, clustering, and others. Through them, the bank can discover low and high-value segments of its customers. Down the line, this will lead to better marketing resource allocations and improving the point of approach for each customer group.
At the same time, customer segmentation also improves customer services, and this leads to improved customer loyalty.
Summary
Data science offers many benefits to the banking world. However, this technique has been adopted across many other sectors. This shows that it has proven value and that it isn’t just some buzzword you will forget in a few years. Consider implementing data science into your business operations and see how much your organization will benefit.
