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Summary

Embark on banking’s technological odyssey, from the reluctant adoption of cloud computing to pandemic-propelled digital shifts. Discover the transformative force of Gen AI poised to unlock a $200-$340 billion value in reshaping financial services.

In the ever-evolving symphony of technological progress, the banking industry stands as a testament to transformation. Over time, banking technology has undergone an incredible journey. From the early days of cloud computing to today’s rise of Gen AI, the path of banking technology has been a testament to constant change, adaptation, and meaningful influence.

Think of it as a journey. Banking technology has evolved through disruptive shifts. It all started with the excitement and buzz around something new, akin to the early hype of cloud computing. Then came the phase of learning and adjustment, where digital innovations were integrated into banking technology. Finally, it settled into a steady rhythm, becoming an integral part of how we bank and operate, much like the steady implementation of Gen AI today.
Cloud Computing: Embracing Flexibility and Security

Amazon Web Services pioneered cloud computing1. Its advantages, including seamless hardware scalability and cost efficiency through the pay-as-you-go model, were specifically tailored for industries like banking and financial services, historically reliant on substantial computing power. However, the banks initially responded tepidly.

The reason for this was the stringent compliance standards and security concerns that governed the banks.

This spurred Amazon and other cloud providers to weave robust security protocols, encryption measures, and compliance certifications tailored specifically to meet the rigorous standards of the financial sector, into their cloud offerings.

This transformed the perception of cloud computing within highly regulated banks. Banks began adopting cloud computing and today most global banks operate on hybrid cloud models, leveraging multiple hyperscalers like Amazon, Google, and Microsoft.

Digital Transformation: The Pivot Prompted by a Pandemic
Digital Transformation had been about the transformational leap to a ‘bank of the future’. It encompassed a myriad of mobile-centric, metaverse, and chatbot-driven initiatives, envisioning the future of banking. The onset of the pandemic accelerated the urgency to eliminate in-person dependencies. Although the world has largely reverted to in-person modes, the technological innovations from that period have become deeply ingrained. Zoom and team calls have reshaped interactions, while mobile enablement has revolutionized work, ingraining digital transformation into the core operations of banks.
AI-powered transformations
In the banking landscape, AI had initially found its footing in Robotic Process Automation (RPA) for mundane back-office tasks and in aiding fraud detection. Gen AI and Chat GPT technologies mark the beginning of a transformative era. They empower banks to offer a more personalized and efficient customer experience while drastically improving operational efficiency and decision-making capabilities.
  1. Front Office Revolution: Enhanced Customer Interaction
    Gen AI-powered chatbots2 capable of engaging customers in natural, human-like conversations used by financial institutions to recommend products, assist with queries, and personalize interactions based on individual customer preferences will become the norm.
  2. Mid and Back Office Transformations: Efficiency at Scale
    Risk and Compliance teams will leverage AI-powered systems to analyze vast amounts of data, documents, and regulatory information, automating tasks like report generation and anomaly detection, reducing not just human error but opening the doors to quicker and more accurate decision-making. This will also strengthen security measures, protecting both institutions and customers from fraudulent activities.
“In the business world, the rear view mirror is always clearer than the windshield.” – Warren Buffet

Technology adoptions seen in hindsight, as previously, clearly point to certain set patterns of adoption. Though the outcomes of new technologies remain uncertain, we can glean insights from past industry adoptions. The lifecycle of these transformative technologies follows a familiar trajectory:

  1. Hype and Confusion: This phase is marked by initial excitement and discussions around the nascent technology. A lot of ideas and possibilities are being passed around. Actual implementations are just a handful.
  2. Intense Learning Phase: A phase where companies engage in learning programs around the facets of the new technology. Enterprise teams confront challenges, learn hard lessons and also watch a lot of project cancellations. A few continue to be dormant at the proof-of-concept stage.
  3. Steady Implementation: Enterprises advance armed with confidence in established strategies and insights into potential pitfalls. Companies implement refined strategies, significantly increasing the success rate of projects.

Gen AI is currently transitioning to the second stage. Here are some use cases that should double up as quick wins.

  1. Front Office – AI leveraged to serve as co-pilots to bank staff to guide and recommend the appropriate products for customers. For example, predictive use cases that help relationship managers to provide robust product recommendations, and “next best actions” based on the customer’s response.
  2. Mid Office – Risk and Compliance teams would experience huge increases in efficiency and accuracy with AI use cases to help read through documents and data points, and to automatically generate reports that require minimal or zero human involvement. Another use case could be around AI’s ability to automate repetitive tasks, such as anti-money laundering (AML) checks, which could significantly reduce workloads.
  3. Back Office – Operations and fulfilment teams will experience huge productivity increases with AI use cases that can generate responses to queries, predict errors or ‘missing data’ and prompt the human operator to correct them. AI’s machine learning capabilities enable it to identify patterns and anomalies in large datasets, leading to more timely and accurate risk identification.

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The ongoing evolution in banking technology underscores the enduring truth in Roy Amara’s words—”We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” As banks traverse these technological frontiers, the convergence of AI and banking envisages a future that transcends human limitations, redefining the very essence of financial services. $200-$340 billion is the estimated productivity value that is waiting to be unlocked through Gen AI technology in the banking sector3. Now, isn’t that something to look forward to?

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies

References:
  1. https://techcrunch.com/2016/07/02/andy-jassys-brief-history-of-the-genesis-of-aws/
  2. https://www.cbinsights.com/research/generative-ai-chatbots-banking-market/
  3. https://www.mckinsey.com/industries/financial-services/our-insights/been-there-doing-that-how-corporate-and-investment-banks-are-tackling-gen-ai

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