effectively segment customers based on more accurate profiles
Posted: Mon Jan 06, 2025 10:09 am
In general, the use of Big Data helps banks to meet these expectations. Data can be exploited through advanced analytics and using cutting-edge technologies, such as artificial intelligence and machine learning in at least three key areas of intervention .
Personalization and convenience. In the case of loan requests, Big israel whatsapp resource Data allows banks to speed up checks on potential credit risks (responses tend to become faster); Big Data provides a framework through which it is possible to understand the ways in which customers prefer to interact with the bank . Thanks to the available information, services are also personalized across all channels managed by the company.
Protection. Big Data helps banks detect fraud before irreparable damage occurs, provides support in monitoring suspicious transactions, increases the security of investments and the privacy of customer accounts.
Creating relevant offers. Thanks to Big Data, banks can anticipate customers' financial goals and develop proposals that are in line with them, target consultants and salespeople who have proven to be more easily attuned to each customer, and offer specific discounts suited to meet personal needs and interests.
Banking sector players are learning to use Big Data effectively to increase IT security , enhance customer loyalty processes , create targeted communications and useful content for increasingly profiled targets, and design personalized and innovative offers . The most widely adopted strategies, which prove to be particularly effective, are those that:
include cross-selling and up-selling initiatives
aim to improve service delivery based on feedback
identify spending patterns and formulate personalized offers
assess risk against compliance standards
produce timely reports to help manage and prevent fraud
identify the main channels through which the customer makes transactions (for example: credit card, debit card or ATM withdrawals)
Since 2008, Big Data analytics in banking have enjoyed growing interest and popularity. As banks began to digitize their operational processes, they also needed specific tools to monitor, track and interpret data and to build actions and initiatives starting from the knowledge acquired through the processed information. Thanks to technological evolution, overall performance has also significantly improved, positively impacting revenues, increasing profitability and pushing the entire banking sector into a further growth cycle .
Personalization and convenience. In the case of loan requests, Big israel whatsapp resource Data allows banks to speed up checks on potential credit risks (responses tend to become faster); Big Data provides a framework through which it is possible to understand the ways in which customers prefer to interact with the bank . Thanks to the available information, services are also personalized across all channels managed by the company.
Protection. Big Data helps banks detect fraud before irreparable damage occurs, provides support in monitoring suspicious transactions, increases the security of investments and the privacy of customer accounts.
Creating relevant offers. Thanks to Big Data, banks can anticipate customers' financial goals and develop proposals that are in line with them, target consultants and salespeople who have proven to be more easily attuned to each customer, and offer specific discounts suited to meet personal needs and interests.
Banking sector players are learning to use Big Data effectively to increase IT security , enhance customer loyalty processes , create targeted communications and useful content for increasingly profiled targets, and design personalized and innovative offers . The most widely adopted strategies, which prove to be particularly effective, are those that:
include cross-selling and up-selling initiatives
aim to improve service delivery based on feedback
identify spending patterns and formulate personalized offers
assess risk against compliance standards
produce timely reports to help manage and prevent fraud
identify the main channels through which the customer makes transactions (for example: credit card, debit card or ATM withdrawals)
Since 2008, Big Data analytics in banking have enjoyed growing interest and popularity. As banks began to digitize their operational processes, they also needed specific tools to monitor, track and interpret data and to build actions and initiatives starting from the knowledge acquired through the processed information. Thanks to technological evolution, overall performance has also significantly improved, positively impacting revenues, increasing profitability and pushing the entire banking sector into a further growth cycle .