Getting most of banking data
As the banking industry continues to move towards digitalization, it’s important for banks to keep up with the latest technologies to stay competitive. In this article, we will explore the machine data revolution and how it can support artificial intelligence (AI) and machine learning (ML) models.
Banks have a tremendous opportunity to unleash the digital potential of the industry through the use of new data-driven technologies such as cloud, AI, and ML. These technologies can create automated, real-time, and at-scale decisions, which can dramatically improve results for banks and their customers. However, most banks’ enterprise data architecture is not designed to support rapid and consistent development of AI and ML. Banks need to reorganize their data architecture end-to-end to implement automated, machine-based decisions in their primary processes.
Currently, banks perform a lot of data analytics to support human decision-making, and the data architecture of banks reflects this way of working. Data is often organized in manually crafted spreadsheets with clean tables and rows, which is convenient for humans but not optimal for the deployment of ML and AI models. Machines can extract low levels of statistical significance across massive volumes of structured and unstructured data. They work around the clock and can make decisions at scale and in real-time.
Reengineering data value chains to support AI and ML’s possibilities is a complex task that can take up years. New technologies and approaches can support this process, including advanced data capture and structuring capabilities, next-generation cloud-based data stores, and analytics to identify connections among random data. Together, these tools and techniques can help organizations turn growing volumes of data into a valuable asset to support automated decision-making.
To reorganize the data architecture of banks, a clear strategy, planning, and management are required. Banks need to understand what kind of data they have, how it’s stored, what the quality is, and how it can be valuable. After that, banks need to prioritize what parts of the data architecture need to be updated first and why. Compliance and business value are particularly important in this respect. Banks must define the data and ensure that it can be reused in different platforms.
Last but not least, banks need to ensure that ethics are embedded in their new data architecture and that adequate governance is in place. AI and ML models work with estimations, and their outcomes need to be “good enough.” Although they can make decisions at scale, it doesn’t mean that the results are flawless. Banks make impactful decisions, such as who gets access to loans and on what terms, and they need to be able to explain and defend their decisions.
In conclusion, the machine data revolution is necessary to support AI and ML models. Banks need to reorganize their data architecture end-to-end to implement automated, machine-based decisions in their primary processes. This will require a clear strategy, planning, and management, and new technologies and approaches to support the process. Finally, banks must ensure that ethics are embedded in their new data architecture and that adequate governance is in place.