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Using No-code AI can help BFSIs bridge the Tech Talent gap - ETCIO

By Ankit Ratan

Amidst the prevailing media hype of mass layoffs and VC funding winter for the technology industry, the reality of the banking industry’s struggles to find and attract quality tech talent remains unchanged. Even for the newly laid off developers and engineers, working in the BFSI industry continues to remain a less attractive option, far behind the allure of working for established technology companies including those in consulting or services.

Banks on the other hand are still finding it hard to realize their digital ambitions in the absence of the right talent. The last couple of years have been particularly painful. With Covid-19 protocols and restrictions in place, banks were forced to apply maximum thrust to their IT projects, even as their customers switched overwhelmingly to digital banking.

Additionally, the rise of the consumer-focused fintech industry and the entry of Big Tech like Apple and Google into banking has opened whole new competitive frontiers. Enhancing the customer experience is now the biggest strategic priority. Increasingly, it is cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) algorithms that are dictating who leads this race.

If the next generation of banking is getting shaped by technology, AI is the primary tech tool that banks must master. However, a lot of banks and other financial services companies have found themselves struggling when deploying deep-tech solutions, often because there has been a veritable scarcity of developers with the required capabilities.

Also, banking is not the only industry betting big on AI. The phenomenon is almost universal and as a result, we have had the problem of a large number of companies across sectors vying for the same pool of skilled workforce. This scarcity is far more acute for AI/ML specialists, for obvious reasons.

Going beyond dealing with the scarcity of AI/ML talent, banks or financial services providers are also finding it challenging to hire developers or engineers for similar reasons. They often find that they simply cannot match developer salaries being offered by large technology companies. Whether or not the current spate of layoffs will soften the compensation levels is a moot question; but the early signs are far from encouraging.

Even if such talent is found and hired at reasonable salaries, banks still find it challenging to develop customised AI applications simply because the skills required to master the banking industry’s data needs will take time to build. In other words, banks will have to contend with significantly longer development and rollout cycles if they were to plan and execute an in-house AI application development strategy. Such delays would put banks at a significant disadvantage when compared to their more agile rivals.

The new business model

Banks are today no longer in the business of banking alone, they are also in the business of technology. While this is a radical shift and makes it incumbent upon traditional banks to redefine and reorient their strategic view of technology, they still do not necessarily need to build large technology teams that mimic the organisational structures of large technology companies. There is a powerful alternative available today - in the form of no-code or low code platforms. The only real challenge left for banks is to transform their conventional workflows and processes before they can be automated.

No-code AI is in fact one of the most significant innovations in recent years with the capability to enable banks to quickly develop, test and launch AI applications in-house, but without needing to hire any developers or testers. The no-code AI does exactly as its name indicates; it does not require actual coding or programming. On the contrary, the platform is meant to be used by business executives and typically features a simple drag-and-drop interface, using which even a non-technical person can develop a cutting-edge AI application.

Using a no-code platform thus allows banks to quickly build AI capabilities and integrate them into their redesigned workflows without having to go through a six to twelve month coding and development cycle. They can directly integrate AI capabilities using a No-code workflow platform and rollout a fully functional AI application in a matter of weeks or even days.

Even non-technical finance professionals can build apps quickly to work with large amounts of data; and analyse and derive insights that may matter most to financial organisations. All this can be done without needing software engineers and building a costly in-house IT infrastructure. For banks in particular, these platforms also mitigate the need to outsource application development to external vendors, which naturally necessitates sharing of banks’ customers’ private data with third parties.

The real significance of no-code or low code platforms is that it can be put to work like a swiss army knife by businesses irrespective of the industry or sector they operate in. Further, even though several no-code platforms are mature and already in use, the market for such solutions is still nascent and growing. For banks that are early adopters, this can mean significant head start.

The author is the cofounder of Signzy, a leading banking infrastructure enabler

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