Disruptive technologies remaking corporate banking

March 04, 2020 17:03
Artificial intelligence, cloud and other disruptive technologies enable banks to build and retain trust in a dynamic and highly competitive business environment. Photo: Bloomberg

Technological advancement and customer expectations are the twin forces driving corporate banking into a new era. An increasingly tech-savvy and demanding clientele is prompting banks to adopt new digital channels and analytics software as they strive to build and retain trust in a dynamic and highly competitive business environment.

Corporate banking clients today expect the same speed of transaction that they experience as retail consumers, if not faster. There is a hefty demand for immediate, real-time access to funds and instantaneous responsiveness.

Banks are continually innovating to stay relevant. This will eventually result in the development of tools that produce practical and meaningful change, which ultimately benefits banks and their clients alike.

Banking innovations are transforming businesses

Among the many disruptive technologies remaking the world of corporate banking, one stands out from the rest: Artificial Intelligence (AI). AI will power a bulk of the major transformative innovations and a particularly promising example is the development of predictive financial models.

This capability fills a key need among corporate clients, with forecasting being the number one issue enterprises want technology to address. This is valuable insight for banks strategizing to win over (or retain) corporate customers but are undecided on how to further enhance their technology repertoire.

Because, while companies have invested substantially in honing their predictive capabilities, more sophisticated and automated analytics tools can significantly improve accuracy rates and create more robust models by crunching data more effectively. A better handle on data enables companies to price their products better while making it easier for customers to buy them. Further, in the case of insurance companies, for example, AI tools can process available data to help quickly settle legitimate claims while weeding out fraudulent ones.

Yet another area where AI is driving rapid progress is supply chain finance. This is especially critical in an increasingly globalized world characterized by the remarkable growth of small and medium enterprises (SMEs). As local SMEs aspire to go regional, supply chain finance becomes one of the biggest needs of the hour and banks are stepping in to fill the lending gap.

And this is not an insignificant gap. As SMEs expand – they already contribute nearly 40 percent of the GDP of emerging markets – their financing needs have ballooned to trillions of dollars a year.

Banks are taking note and turning to technology to help address the shortfall. Some have started using machine learning (ML) and analytics to better understand customer behavior and digitalize its processes accordingly, including streamlining the loan application process for SME clients. There are other banks that are developing AI-based real-time screening capabilities and faster payments infrastructure.

Along with helping SMEs scale up, AI-driven banking solutions are also optimizing companies’ cash flows to ensure SME suppliers are paid on time – an ideal outcome that reduces risk for both buyer and seller.

Immediate challenges to consider

Even as banks race to adopt the latest technological tools, there are a number of issues to consider and obstacles to clear.

Among the most important of these is the siloed nature of banks’ technological infrastructure which often results in the fragmentation of data and, in turn, hinders and complicates the creation of effective predictive models.

Ineffective legacy infrastructure also leads to inconsistent and delayed delivery of services, and high operational costs, all of which can potentially affect a bank’s reputation, its ability to retain or take on new clients, and efforts to expand operations.

Yet another major challenge lies in creating a secure payments infrastructure that is compliant with anti-money laundering and counter-financing of terrorism regulations. Cost, too, is an issue as banks struggle to bring down the processing fees of various transactions.

Undoubtedly AI and ML-based tools can be harnessed to sharpen an organization’s predictive capabilities, as well as create smart, efficient financial crime and compliance management systems and effective digital tools that reduce operational costs.

But the most important thing to remember is that tackling these challenges requires an enterprise-wide approach to adopting standardized technology that can help centralize capabilities.

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RT/CG

Head Business Development Corporate Banking, Oracle Financial Services