Tuesday, August 09, 2022

BFS firms spend to benefit Indian IT companies

IANS | November 16, 2021 03:17 PM

NEW DELHI:  Banking and financial services firms in the US and Europe look to maintain or accelerate IT spending intensity as they pursue technology-first or digital-first business and operating models. Rising online penetrating, competition from fintechs, big tech, and a regulatory push are catalysts for compressed transformation. Investment is across leaders and laggards. The journey is not a short one and provides plenty of opportunities for Indian IT. Both Tier 1 and mid-tier will benefit, Kotak Institutional Equities said in a report.

Commentary by BFS firms on IT spends in the Sep-21 quarter is similar to that in the previous quarters. A similar outlook has been shared by a newer set of players indicating that the trend is broad based. Accelerated investments in cloud and digital will continue in the near term and beyond making 2022E a strong year of IT spending.

Offshore players will benefit from the accelerated investments even if companies intend to maintain or reduce the level of technology spending in 2022.

Drivers for outsourcing to Indian IT remain quite favourable both from a demand (shortage of skilled talent, legacy headcount, time to market pressure and cost pressures) and supply perspective (availability of skilled talent at scale, cost advantage, leadership in digital competencies, proven execution capabilities and global delivery model). We expect higher outsourcing to be a driver of growth for Indian IT in the next 2-3 years, the report said.

BFS firms are increasingly competing with fintechs and even with big tech. Fintechs are rising in prominence and are receiving huge investments fueling strong market adoption. They both compete and collaborate with BFS firms. At the same time big tech wants to own the customer experience and is vying to be at the front end for customers. These trends together with higher digital penetration post Covid are putting pressure on BFS firms to undergo transformation in a compressed time frame.

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