Banking Sector Benefits From Analytics

Data analytics in banking is critical in today's data-driven environment for driving enterprises forward, improving efficiency, increasing returns, and achieving corporate goals. For the uninitiated, data analytics is the process of extracting, interpreting, and communicating actionable insights from data to aid decision-making.

The global market for big data and business analytics was valued at $169 billion in 2018 and is predicted to reach $274 billion by 2022. The number of new data analytics apps and technologies is increasing daily.

Revenue from banking and financial services applications, where the digitization of numerous services has created vast volumes of big data, is expected to account for roughly 13% of this industry, or $22 billion.

With the correct AI and cloud technologies, this data, both organized and unstructured (such as consumer comments), can be managed efficiently. It can help you generate new revenue and maximize existing revenue by improving your business processes from the perspective of your customers.

The different avenues which are benefit from the implementation of data analytics in the banking sector are as given below:

Marketing and sales in the banking business, analytics are now driving direct marketing and sales activities, demonstrating which initiatives will yield the biggest returns and how customer segmentation across categories may make cross-vertical marketing easier to handle.

Cross-selling opportunities can also be identified using data analytics, allowing banks to better bundle services and build product bundles that appeal to customers at different stages of their lives. Consumers on the verge of purchasing a home, starting a family, or investing their money can be identified and targeted using precise language and imagery.

• • Customer service Customer support services is another area where data shines. Continuous data collection gives knowledge and insight into the issues that consumers face. A knowledge base can be built to provide a self-service option if data shows that similar queries are routinely addressed to a specific service department. The user experience can be improved by quickly resolving their issues utilizing data gained from previous interactions.
• • Security and fraud Fraud prevention and detection are one of the most significant ways that data analysis has impacted the financial services business. Machine learning and AI's capacity to recognize trends in customer behaviour and transactional data allow anomalies to be identified and probed promptly. This enables the early detection of potentially fraudulent behaviour, allowing banks to reduce fraud-related costs while also increasing client trust. Banks are putting more emphasis on data analytics than ever before to acquire a competitive advantage.Data analytics' importance in the banking and financial services industry has been recognized on a larger scale than ever before, and many major banks have already begun to reap the benefits.