A future towards enlightenment
January 23, 2021
Muskan Surana

“It’s all too easy for a successful organization to make next year’s strategy simply an incremental improvement on the previous year’s. But in this age of digital disruption, companies need to be regularly repositioning themselves for a radically different environment”

- Robert Alexander, Capital One Financial.

In old days, slow & steady won the race. These days, one has to be fast & lean which requires working on the Agile & Development Ops Principle.

Post the Global Financial Crisis (GFC), Financial Services industries were imposed with enormous rules & regulations. After taking the hit from the crisis and all the rules & regulations, the Financial Services industry slowly increased its pace towards advancement, only to get hit again by the pandemic (Covid-19).

During the period of pandemic & worldwide lockdown caused by it, the world experienced the rise & fall of the digital world & traditional methods.

The rising number of start-ups in the FinTech industry & the advancements within the internet giants have posed an enormous challenge to the existing traditional & legacy application in the FS industry.

The digital world is changing how a customer handles his/her transactions and finances. With the lack of advancements in technology in the existing banks, customers are directing themselves towards a more customer- friendly, FinTech industry.

Factors that are holding banks behind in implementing AI & ML technology are data silos & outdated IT infrastructure, tighter regulatory scrutiny, hype-driven scattershot goal setting, slow to react to the changing demands & environment, etc.

Banks must turn the tide around to survive in the competitive market by understanding the changing demography and customer base, investing in the R&D department which can lead to improved margins & generate additional revenue streams and the most crucial part is, if they won’t do it, someone else will.

There are several uses of AI & ML technology in the FS industry, for example; personal finance management (PFM), sentiment analysis, Robo advisors, behavioral biometrics, natural language generation, churn prediction, query routing, customer segmentation, targeted marketing and value proposition, job function identification, cost optimization and several other very advanced applications.

Contacts
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