The reality of the banking industry’s struggles to find and attract quality tech talent remains unchanged, despite the prevalent media hype of mass layoffs and a VC funding winter for the technology industry.
Even for newly laid-off developers and engineers, working in the BFSI industry remains a less appealing option, trailing the allure of working for established technology firms, such as those in consulting or services.
Banks, on the other hand, are still struggling to realize their digital ambitions due to a lack of qualified personnel. The last few years have been especially difficult. Banks were forced to apply maximum thrust to their IT projects with Covid-19 protocols and restrictions in place, even as their customers overwhelmingly switched to digital banking.
Furthermore, the rise of the consumer-focused fintech industry, as well as the entry of Big Tech companies such as Apple and Google into banking, has opened up entirely new competitive frontiers. Improving the customer experience is now the most important strategic priority. Cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) algorithms are increasingly dictating who wins this race.
If technology is shaping the next generation of banking, AI is the primary technological tool that banks must master. However, many banks and other financial services firms have struggled when it comes to deploying deep-tech solutions, often due to a scarcity of developers with the necessary skills.
Furthermore, banking is not the only industry investing heavily in AI. The phenomenon is almost universal, and as a result, we have a problem with a large number of companies from various industries competing for the same pool of skilled workers. For obvious reasons, this scarcity is far more acute for AI/ML specialists.
Aside from the scarcity of AI/ML talent, banks or financial services providers are also having difficulty hiring developers or engineers for similar reasons. They frequently discover that they simply cannot compete with the developer salaries offered by large technology companies. It is unclear whether the current round of layoffs will soften compensation levels, but the early signs are not promising.
Even if such talent can be found and hired at reasonable salaries, banks will still find it difficult to develop customized AI applications because the skills required to master the banking industry’s data needs will take time to develop. In other words, if banks plan and execute an in-house AI application development strategy, they will face significantly longer development and rollout cycles. Delays of this magnitude would put banks at a significant disadvantage in comparison to their more agile competitors.
The brand-new business model
Banks are no longer just in the banking business; they are also in the technology business. While this is a significant shift that requires traditional banks to redefine and reorient their strategic view of technology, they do not necessarily need to build large technology teams with organizational structures similar to those of large technology companies.
Today, there is a strong alternative in the form of no-code or low-code platforms. Banks’ only real challenge now is to transform their traditional workflows and processes before automating them.
No-code AI is one of the most significant innovations in recent years, allowing banks to quickly develop, test, and launch AI applications in-house without the need to hire any developers or testers. The no-code AI does exactly what the name implies; it does not require any coding or programming. On the contrary, the platform is intended for use by business executives and typically includes a simple drag-and-drop interface that allows even non-technical users to create a cutting-edge AI application.
Using a no-code platform, banks can quickly build AI capabilities and integrate them into redesigned workflows without having to go through a six to twelve-month coding and development cycle. They can use a No-code workflow platform to directly integrate AI capabilities and launch a fully functional AI application in a matter of weeks or even days.
Even non-technical finance professionals can quickly build apps to work with large amounts of data, as well as analyze and derive insights that are important to financial organizations. All of this can be accomplished without the need for software engineers or the establishment of an expensive in-house IT infrastructure. These platforms, in particular, reduce the need for banks to outsource application development to third-party vendors, which naturally necessitates the sharing of banks’ customers’ private data with third parties.
The true significance of no-code or low-code platforms is that they can be used as a Swiss army knife by businesses regardless of the industry or sector in which they operate. Furthermore, despite the fact that several no-code platforms are mature and in use, the market for such solutions is still nascent and expanding. This can mean a significant head start for banks that are early adopters.
