Data's Untapped Potential in Banking

In the realm of finance, the significance of robust data governance and management cannot be overstatedWith growing pressures for digital transformation, financial institutions are increasingly recognizing the need to harness both internal and external data resourcesThis has prompted a shift towards a truly unified management approach that not only consolidates data but also facilitates its sharing across various domainsAs digital financial systems evolve, banks are particularly focused on how they can innovate and improve efficiency by better leveraging data assets.

Recent discussions around this transformation have highlighted the pivotal opportunity that arises from the development of public data resourcesAccording to Ren Tune-nan, a senior researcher at Xinyi Research for Financial Regulation, banks in China are poised to benefit greatly from unlocking public dataHe emphasizes that the year 2025 may mark a significant turning point for the banking sector, where the emphasis will be on utilizing developed public data resources to propel their services forward

This is part of a larger vision to align financial systems closely with the evolving digital economy by the conclusion of 2027.

The quality of data serves as the cornerstone for artificial intelligence (AI) applicationsMajor financial institutions, such as Su Shang Bank’s financial technology lab, are injecting new energy into the sector by embedding advanced digital solutions that enhance market responsiveness and enrich customer serviceSuch digital transformations are positioning banks not only to modernize their offerings but also to ignite innovation within the industryFurthermore, the implementation of data-driven intelligent risk management frameworks is expected to refine the precision and timeliness of risk assessments, solidifying operational stability in an ever-changing market landscape.

Insights shared by Ren Tune-nan also point to a concentrated policy drive aimed at eliminating barriers in the development of public data resources between September and October 2024. This strategic push is anticipated to lay the groundwork for the robust emergence of public data resources, enhancing how financial institutions operate

• Developing Multi-Tiered Capital Markets
• U.S. Inflation Decline Set to Slow
• Dollar Wealth Management Assets Double Amidst Interest Rate Uncertainty
• RMB Stays Steady Against a Strong Dollar
• India's Q3 GDP Growth Hits Low, Economy Faces Headwinds

The three levels of digital finance development—application of data elements, utilization of financial technology, and the digital transformation of banking operations—underscore the transformative potential that lies ahead, particularly through effective data resource utilization.

The banking industry, characterized by its data-intensive nature, stands to gain tremendously from the availability of high-quality dataA forecast by Palo Alto Networks, a leader in global cybersecurity, predicts that by 2025, large enterprises already owning extensive customer bases and data repositories will dominate the cybersecurity landscape, particularly when compared to newly founded AI startupsThis ability to harness and optimize data will enable these larger entities to greatly enhance the performance of AI models, yielding competitive advantages in various sectors.

Interestingly, a survey found that 81% of CEOs identify generative AI as their top investment priority, with many anticipating substantial returns in the next three to five years

They show considerable enthusiasm about the latent capabilities of AI technologies in improving fraud detection, bolstering cybersecurity measures, enhancing data analytics, streamlining operational efficiencies, and personalizing customer service experiences.

As commercial banks navigate the complexities of the digital economy, it has become apparent that they must adapt data governance efforts to align with the demands of a data-centric and intelligent futureSpecific attention is being directed towards strengthening data security measures alongside enhancing data protection capabilities to mitigate associated risks.

The technological revolution and the rise of generative AI further compound the urgency for banks to develop robust frameworks for data quality controlInstitutions like Su Shang Bank advocate for a comprehensive approach encompassing a full integration of both internal and external data resources, allowing for seamless data management and sharing

This vision includes utilizing advanced technologies—such as big data analytics and privacy computing—to optimize risk management models and the design of financial products, ultimately ensuring cost efficiency in data acquisition.

However, alongside the growing value derived from data, challenges in data security and governance have also surfacedAs Martin Creighan, the Vice President of Commvault for the Asia-Pacific region, notes, the cloud computing era has birthed a complex landscape of private, public, and multi-cloud environments, exacerbated by a surge in data volume—especially with the wide adoption of AIThis evolution has intensified the problem of data silosOrganizations are tasked with addressing data governance holistically while navigating these silosIt is essential that firms implement unified platforms able to oversee and manage their data environments comprehensively, thus enhancing their ability to secure data integrity in unpredictable situations.

Accordingly, efforts such as those by the Bank of Communications reflect a need for multi-domain data integration to maximize the breadth and depth of financial service offerings

By addressing communication costs and trust issues, they can successfully navigate the challenges presented by existing market regulations governing data elements.

Practical measures adopted by the Bank of Communications illustrate a well-coordinated endeavor across three levels: data, models, and knowledgeIn the data category, they are constructing comprehensive data asset catalogs that not only encompass structured data but also extend into unstructured data terrain—ranging from text to multimedia formatsThis effort is instrumental in refining artificial intelligence applications for precision and alignment with market needs.

The model aspect encompasses the extraction and management of indicators, labels, and model information which is essential for maintaining cohesion from both large and small model frameworksEstablishing an enterprise-level model feature repository can facilitate the efficient reutilization of such models across various projects, thereby increasing overall development productivity.

Finally, the knowledge dimension emphasizes the establishment of a robust operating mechanism for knowledge captured within the enterprise