Digital Transformation In Retail Banking

Digital Transformation In Retail Banking

Banking no longer means standing in long queues and engaging in a lot of paperwork, thanks to the digital transformation in retail banking.

Examples of digital transformation in retail banking include the rise of neo banks, artificial intelligence for AML and KYC compliance, Artificial intelligence for personalized recommendations, cloud computing, and open banking.

According to a Deloitte report, the future of digital banking will put clients at the heart of every digital strategy, and institutions will need to start implementing these strategies now to be ready to smoothly integrate by 2030.

In this ebook, we have discussed why digital transformation is necessary for retail banking, and how digital transformation can change the concept of retail banking.

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  • Table of Contents:

    • Why is digital transformation in retail banking necessary?
    • How can digital transformation change retail banking?
    • I. Neo banks
    • II. Artificial intelligence for AML & KYC compliance
    • III. Artificial intelligence for personalized recommendations
    • IV. Cloud computing
    • V. Open banking
  • Summary:

    The banking industry’s digital transformation has improved its operations and the way of serving customers. And, as we’ve seen, it will continue to evolve and become more personalized over time.

    That’s why it’s important to know the reasons that make digital transformation an integral part of retail banking. Also, banks should know how to implement digital transformation in retail banking by adopting new technologies and services.

  • Key Highlights:

    • 200% jump in new mobile banking registrations in early April of 2020, while mobile banking traffic rose 85%.
    • 46.5% growth in the neo bank market is expected to accelerate at a CAGR of around between 2019 and 2026, generating around $394.6 billion by 2026.
    • $23.5 billion a year is the estimated cost of anti money laundering compliance in the US.
    • $447 billion is predicted to be the overall potential cost savings for banks through AI applications by 2023.

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