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The Economic Impact of Generative AI on the Banking Industry


Generative AI is poised to bring about a significant transformation across various industry sectors. Among these, banking and high tech are expected to experience the most substantial impact in terms of revenue. In the banking industry alone, the full implementation of generative AI use cases could generate an additional annual value ranging from $200 billion to $340 billion according to McKinsey research.


Automation Revolution: Liberating Time for Innovation


Current generative AI technologies, along with other advancements, have the potential to automate a substantial portion of work activities that currently consume 60 to 70 percent of employees' time. This is a notable increase compared to previous estimates, which suggested that technology could automate only half of employees' work time. 


The adoption of generative AI could lead to a remarkable growth in labor productivity, ranging from 0.1 to 0.6 percent annually until 2040. When combined with other technologies, the overall automation of work could contribute 0.2 to 3.3 percentage points annually to productivity growth. However, to fully harness these benefits, workers will require support in acquiring new skills and adapting to changing occupational landscapes.


Embracing Generative AI: A Path to Economic Growth


Generative AI has the potential to significantly contribute to economic growth, fostering a more sustainable and inclusive world. By integrating generative AI applications, the banking industry can unlock value equivalent to 2.8 to 4.7 percent of its annual revenues, amounting to an additional $200 billion to $340 billion. Moreover, the adoption of generative AI tools can lead to enhanced customer satisfaction, improved decision-making processes, enriched employee experiences, and reduced risks through better fraud and risk monitoring.


The Banking Industry: A Fertile Ground for Generative AI Integration


The banking industry, with its deep knowledge and technology-driven nature, has already reaped the benefits of artificial intelligence applications in areas such as marketing and customer operations. Generative AI, with its proficiency in handling text-based modalities, can further amplify the industry's advantages, especially in regulatory compliance and programming languages. Furthermore, the industry's customer-centric focus, serving both B2C and small-business customers, positions it favorably for leveraging generative AI applications.


Several key characteristics make the banking industry a prime candidate for the integration of generative AI:


Sustained digitization efforts along with legacy IT systems


Decades of technology investments have resulted in significant technical debt and complex IT architectures within banks.


Large customer-facing workforces


Banks heavily rely on a substantial number of service representatives, including call-center agents and wealth management financial advisers.


A stringent regulatory environment


Banking, being a heavily regulated industry, necessitates extensive risk, compliance, and legal frameworks.


White-collar industry


Generative AI's impact can extend across the organization, assisting employees in various tasks such as writing emails and creating business presentations.


The Power of Generative AI in Banking


The banking industry is gradually recognizing the immense potential of generative AI in its frontline operations and software activities. Early adopters are already harnessing solutions like ChatGPT and industry-specific tools for software and knowledge applications. Here are three key use cases that highlight the value of generative AI in the industry:


Virtual expert for augmenting employee performance: By training a generative AI bot on proprietary knowledge, policies, research, and customer interactions, banks can provide always-on technical support to their frontline staff. This approach improves customer experience and enables real-time monitoring of industries and clients.


Documentation: Simplifying Compliance and Paperwork


In the banking sector, meticulous documentation and compliance regulations are paramount. AI and ChatGPT have emerged as invaluable assets in streamlining the documentation process, boosting efficiency, and ensuring adherence to regulatory requirements.


By harnessing the power of AI-driven natural language processing (NLP), ChatGPT can analyze complex banking documents, including loan agreements, mortgage contracts, and insurance policies. This advanced technology significantly reduces the time and effort involved in reviewing and extracting pertinent information from these documents. Through automated document classification and data extraction, ChatGPT accurately identifies crucial data points, eliminating manual data entry and minimizing errors.


Automation: Driving Operational Efficiency


Automation lies at the heart of digital transformation in the banking industry. AI and ChatGPT enable banks to automate repetitive and time-consuming tasks, empowering employees to focus on higher-value activities and enhancing operational efficiency.


Moreover, AI and ChatGPT facilitate the automation of back-office processes, including data and stock data entry and follow-up emails. By analyzing vast volumes of financial data and identifying patterns, AI algorithms can proactively detect information mentioned during a calland pre-fill a number of documents to later send the data to existing CRM. Additionally, AI-based automation streamlines internal workflows, enhances compliance monitoring, and expedites decision-making processes, ultimately reducing costs and optimizing operational efficiency.


Data Extraction from Internal Sources: Unleashing Actionable Insights


Banks generate massive amounts of data, encompassing customer transactions, market trends, and more. Extracting actionable insights from this data is paramount for strategic decision-making and maintaining a competitive edge. AI and ChatGPT play a pivotal role in unlocking the untapped potential of internal data sources in the banking sector.


By leveraging advanced machine learning algorithms, ChatGPT can analyze unstructured data such as customer feedback, emails, recorded client conversations and support tickets. This analysis provides banks with a deeper understanding of customer sentiments, preferences, and needs. These insights, in turn, inform product development, drive effective marketing strategies, and enable personalized customer experiences.


Furthermore, AI algorithms can process and analyze historical customer data, unraveling patterns and trends. This empowers banks to predict customer behavior, assess creditworthiness, and offer tailored financial solutions. Such information empowers banks to provide personalized recommendations, execute targeted marketing campaigns, and proactively detect fraud, resulting in heightened customer satisfaction and reduced risks.

The advancements in artificial intelligence have garnered substantial interest and investment. Management teams of publicly-traded corporations are increasingly recognizing the significance of AI, resulting in a surge in capital investment at the company level. According to Global Economics Analyst, by 2021, private investment in AI reached $53 billion in the US and $94 billion globally, reflecting a fivefold increase in real terms over five years. If this investment continues at a similar pace, the US alone could witness AI investment approaching 1% of its GDP by 2030.


Overcoming Challenges for AI Adoption in Banking


While the possibilities of AI in banking are vast, the adoption rate remains low. Recent research conducted by Accenture highlights three typical challenges hindering widespread AI implementation:


Technology-driven approach: Many banks apply AI based on technology rather than business intent, resulting in limited improvements in overall processes and customer experience.


Fragmented technology infrastructure: Banks often rely on a collection of fintech solutions and lack integrated data platforms, impeding the effectiveness of AI. Knowledge and algorithms are locked in individual silos, leading to inefficiencies and rework.


Impact on the operating model: AI is transforming the nature of work, requiring business leaders to actively engage in creating their future by assembling multidisciplinary teams, providing infrastructure and tools, and promoting a culture of AI collaboration.

Embracing the Future with Generative AI


To fully realize the potential of generative AI, the banking industry must overcome the challenges and seize the opportunities it presents. By embracing generative AI applications, banks can not only drive economic growth but also improve customer experiences, streamline operations, and empower their workforce. With careful planning, skill development, and effective integration, generative AI has the power to revolutionize the banking industry and pave the way for a future of innovation and success.

Written by

Hanna Karbowski