Technology is transforming the way banks and their relationships with customers are run, including disrupting the use of AI calculations in areas such as fraud detection and prevention and personalisation.
Written by Fausto Gerado, Business Value Architect at SAS
The routine of bank management has started to change radically in recent times. Insights that required hours of product analysis and data analysis now flow in minutes on your computer. This is not science fiction, but just a rollback of a transformation developed through Generative IA, a tool that does not help improve processes, but redefines a form such as banking and relates to its customers.
IA, also understood as GenAI, was created to redefine the global banking panorama of innovation, and the numbers confirm this trend. Secondly regarding the latest SAS, 60% of global banks are adopting this technology, while 38% of others plan to implement in the next two years. This is the speed of financial deposit or setup over the course of the technological revolution, along with the security market.
The movement that began in IT and marketing departments is rapidly expanding into areas such as sales, finance, and customer follow-up. The logic for accelerating this step is simple: there are large amounts of data and manual processes, and there is potential for intelligent automation. HR, compliance, audit, and back-office departments have also discovered that they can eliminate recurring costs and increase operational efficiency that matters with the creation of IA and are committing to the technology for the long term.
The question has to go back to investing
As with all technology that attracts the interest of decision enthusiasts, one of the parents looking to maximize returns regarding a good investment, is more understanding like it Return on investmentOr return on investment. However, the use cases that show the greatest ROI lie in two critical areas: real-time fraud detection and prevention, and accelerating the full analytical cycle.
When thinking about ROI, it is important to know that the ability to instantly identify suspicious people and come up with new ways to defraud through the created frauds represents a high-quality asset for protecting banking assets. At the same time, a significant reduction in the time required to develop, test and implement analytical models allows organizations to quickly respond to market changes.
You should remember that the traditional development cycle that would help you create and validate pre-prototypes can now be finished in a matter of weeks. This rapid connection to GenAI is central in an environment with ever-increasing new types of fraud and competition that demand immediate response. When a new criminal method emerges, the banks used by the CIA can develop forms of mitigation for these crimes in these days, not months.
There are ways to manage the success of initiatives, which is to define specific indicators for each use case. End-user satisfaction, whether an external or internal customer, is a good measure of the effectiveness of the solution. Operational efficiency, along with reduced processing times and customs processes, are in addition to tangible measures of good value. These indicators should be tailored to the specific context of each implementation, as different departments have different needs and challenges.
Naturally integrating traditional models with generative capabilities, creating stronger hybrid systems. Creating IA does not replace existing methodologies, but it increases the level of analytical maturity in organizations. Its data processing capabilities are not built up (in the form of text, images or audio clips) on a large scale or wide range of information that can be used to make a complete decision. This integration results from more accurate models and more certain answers.
Challenges and opportunities of GenAI
The transformation we are discussing here knows no legal receipts. We have discussions at the executive level about the concern for privacy and data protection, especially given that banks handle very sensitive information. Accuracy in answering calls and approving systems is additionally among the setup tools, which are customized with precisely controlled processes. Modern GenAI solutions include strong analytics governance, scalability, and regulatory transparency to help mitigate these risks.
Manufacturers are another elegant solution to privacy issues. Through artificial databases that imitate properties from real data without exporting personal information, banks can speed up model development without compromising security. This reduction can solve the issue of obtaining data for training and ensure privacy protection for customers.
In the progress phase of creating an IA, there is a question of internal development reverse The third solutions are always relevant. The decision must take into account the overall allocation of ownership, including hardware, maintenance, and specialized human resources. Internal innovation capacity can be compared to what is offered by specialist providers, but with a better strategy that combines robust technology platforms and in-house know-how with enterprise-specific processes, creating customized solutions to preserve interno intellectual capital.
The Brazilian bank is very agile with new technologies and is beneficial in the near term of this evolutionary phase of GenAI. As we have implemented the success of PIX, for example in terms of the ability to rapidly innovate and be effective, Brazilian banks are frequently implementing digital solutions globally, or positioning funds as a laboratory for financial innovation.
The future encourages expansion of capabilities in few areas that can be explored. Personalizing offers using data does not allow for truly individualized suggestions. By continuing your decision-making processes, with automatic suggestions for next steps, you should change the experience of a large number of employees. Fraud detection is evolving into fraudulent systems that predict criminal attempts before these cases occur.
IA also creates professional opportunities, freeing human resources from operational tasks for combined high-value strategic activities. This transformation in banking aims to reorganize the nature of functions, analysis of priorities, creativity, and relationships between courses.
Adoption of GenAI in banks is not the most frequently asked question about “se”, “quanto” and “como”. And organizations that manage this transformation through a clear strategy, proper governance and focus on use cases will return to financial optimization in the new digital ecosystem.
