Data science is no more a trend for banking world; it is rather a need to stay ahead in the competitive market. Banks need to understand that big data technologies can be helpful to them and make them focus on their resources efficiently, enhance performance, and make sound decisions.
In this article, we will share a list of data science use cases in banking industry. This will give you an idea of how can financial institutions can work with bulk data and use them efficiently.
Fraud Detection
Machine Learning is an ideal solution for effective detection and fraud prevention. It can protect customers and employees of banks with its proactive fraud detection ability. ML finds fraud in a limited time so that banks can restrict account activity faster to minimize losses. When the banking institution implements a series of fraud detection schemes, it can have required level of protection and prevent losses.
The main steps involved in fraud detection are –
- Getting data samplings for preliminary testing and model estimation
- Estimating model
- Testing and deployment
As there are different data sets available, each requires separate training and fine-tuning which is done with the help of data scientists. Changing the deep theoretical knowledge into practical apps requires strong expertise in data mining techniques. Such as clustering, forecasting, association, and classification.
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Customer data handling
Banks operations are carried out on the basis of collected, analyzed, and stored data. Machine Learning and Data Science tools can be used to transform the process into a possibility to learn more about the account holders or clients to initiate revenue growth.
Nowadays, digital banking is gaining popularity and has become a widely adopted technology around the world. The data scientist team is there to segregate relevant data from the collected customer data. Data scientists apply precise machine learning models to unleash more opportunities for generating revenue for banks by separating and processing client-centric data to enhance decision making. The data owned by data scientists include details about customer behaviors, interactions, and preferences.
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Risk Modeling For Investment Banks
Investment banks take risk modeling as a high priority as it assists them in regulating all financial activities and play a key role to decide prices of financial instruments. Evaluation of companies’ worth is done by investment banking to create capital in corporate financing. Carry out corporate restructuring or reorganizations, easy acquisitions, and facilitate mergers and for investment purposes.
Big data helps innovators in leveraging the latest technology for effective risk modeling and enhanced data-driven decision outcomes.
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Better and personalized marketing
Successful marketing offers more personalized solutions that meet the client’s needs and preferences. With data analytics Solutions, banking institutions can create personalized marketing that delivers the exact product desired by the client at the right time on the right device. It uses data mining technology to target selection to detect potential customers for the latest product.
Data scientists take help of behavioral, historical, and demographical data to create a model that predicts the response of customers to the offer. So, banks can make more personalized outreach and enhance their relationships with their clients.
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Process automation
Banks are now using robotic process automation (RPA) technology to eliminate human error and restructure the workforce to put pressing tasks on mainstream list. For instance, the use of chatbots is the best example of automating tasks with which banks are able to provide quick and reliable assistance to online customers. With the help of AI-enabled mobile and web chatbots, banks can instantly interact with the customers and this decreases the need for human assistance.
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Lifetime value prediction
CLV (Customer Lifetime Value) is the term that can be defined as prediction of all the value derived by the business from their lifetime relationship with a customer. The significance of CLV is growing with pace as it helps in creating and retaining relationships with selected customers. Which will bring benefits to the banking institution and higher profitability and business growth.
It is challenging for banks to acquire and retain profitable customers. The competition level is high and so, banks require a 360-degree view of each customer in order to focus on their resources better. Here is why they need data science. Data experts will collect the related data which later gets cleaned and manipulated to become useful and meaningful to the banking employees.
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Customer Support
Providing service is the USP to have loyal customers for any business. It is part of customer service and it’s a broad concept in the banking industry. All banks are service-based businesses and thus customer support is an inevitable part. It includes replying to customers’ queries and complaints timely and getting engaged with the customers. Data science is helpful as it makes the process better automated, personal, direct, precise, and productive.
It is high time that the banking world should realize the competitive advantage of data science and should integrate the technology in their decision-making process and develop strategies on the basis of actionable insights through client’s data.