Unlocking the future of banking: the transformative power of generative AI MENA

Its conversational powers could also guide users through sometimes complicated programmes. Generative design helps with ideation, generating all computationally possible solutions to a problem within a given set of parameters — even when the design is completely novel and a radical change from anything that has come before. AI will eventually perform many of the tasks paralegals and legal assistants typically handle, according to one study by authors from Princeton University, New York University and the University of Pennsylvania. A March 2023 study from Goldman Sachs said AI could perform 44% of the tasks that U.S. and European legal assistants typically handle.

Generative AI in banking refers to the use of advanced artificial intelligence (AI) to automate tasks, enhance customer service, detect fraud, provide personalized financial advice and improve overall efficiency and security. Success in GenAI requires future-back planning to set the vision and a programmatic approach to use-case prioritization, risk management and governance. Banks will need to challenge their current understanding of AI primarily as a technology for back-office automation and cost reduction. Thinking through how GenAI can transform front-office functions and the overall business model is essential to maximizing technology’s return on investment. Using GenAI along with a balanced set of measured actions supported by a longer-term strategy will allow banks to create value for customers and shareholders while building the bank of the future.

How artificial intelligence is reshaping the financial services industry

Currently, there is a growing need among Indian banks to utilize Gen AI-powered virtual agents to handle customer inquiries. Adding Gen AI to existing processes helps banks convert customer call to data, search knowledge repositories, integrate with pricing engine for quotations, generate prompt engineering, and provide real-time audio response to customers. This, in turn, improves user experience as it minimizes the wait time for the customer, reduces redundant and repetitive questions, and improves interaction with the bank. Across industries, staffing shortages force companies to “do more with less,” leveraging their limited resources for maximum efficiency. Financial institutions are certainly not excluded from this struggle, and resource constraints may be even more pressing as some of the largest banks strive to process millions of transactions each day.

• The researchers studied three million conversations between customers and 5,179 customer support agents at a large software company.
• Here are five areas where AI technologies are transforming financial operations and processes.
• Notably, it is the first European bank to forge an alliance with OpenAI, which will share its knowledge and unlock the full potential of the new tool at the bank.
• Spin up thousands of different models across the enterprise and the costs rapidly multiply (as do carbon emissions).
• Globally, institutions foresee a 5 to 10 year timeline for full automation harnessing, strategically investing in areas with immediate benefits, such as customer service and cost reduction.

Insurance can be complicated, and customers naturally want things to be as simple as possible when they interact with providers. Generali Poland, which offers comprehensive insurance services, recognized that its customer consultants were spending most of their time repeatedly fielding basic queries and managing straightforward claims and policy changes. After the COVID-19 pandemic sent the adoption of virtual agent technology soaring, companies are now discovering how adding generative AI into the mix can pay dividends. Forward-thinking organizations can remove friction from customer self-service experiences across any device or channel, driving up employee productivity and enabling adoption at scale. A vast majority of bank organizations are either in production or have gone live with generative AI use cases, often focused on client engagement, risk and compliance, information technology, and other support functions.

BBVA plans to hire 2,700 technology professionals in 2024

Therefore, financial institutions worldwide are typically exploring only 7-10 crucial use cases on average. Our survey confirms this pattern, as 45% of participants have emphasized that identifying use cases and inadequate focus on Gen AI initiatives are among the primary obstacles when implementing Gen AI. More broadly, gen AI could transform compliance ChatGPT App and security measures, enabling firms to meet regulatory requirements more efficiently while reducing the cost and effort involved in combating financial fraud and managing risk. Hyper-automation aims to achieve end-to-end automation across various treasury functions, from cash management and liquidity forecasting to compliance and reporting.

European banks have beaten many of their US counterparts on profitability during the past two years, riding the higher interest-rate wave. But when it comes to adopting artificial intelligence (AI) – the megatrend emerging over the same period since the release of ChatGPT in 2022 – European banks fall behind once again. Travel companies can also use AI to analyze the deluge of data that customers in their industry generate constantly. For example, travel companies can use AI to help aggregate and interpret customer feedback, reviews and polls to evaluate the company’s performance and develop strategies for improvement. KPMG combines our multi-disciplinary approach with deep, practical industry knowledge to help clients meet challenges and respond to opportunities.

Building on technology leadership

Embedded finance can help banks serve clients whenever and wherever a financial need may arise. Asteria plans to help its SME clients improve profitability, increase financial stability, and enhance financial acumen through broader implementation of its virtual advisor. Also based on action.bot from TUATARA and IBM watsonx Assistant, Piotr is a virtual assistant that’s fully integrated with the bank’s knowledge base.

These include skills such as prompt engineering, management of vector databases, and command of a toolbox dedicated to AI and ML operations. You can foun additiona information about ai customer service and artificial intelligence and NLP. A recent industry study found that current hiring trends suggest more than 30% of job ads by prominent European banks, including Barclays, ING, and NatWest, now encompass AI-related roles. An effective operating model should enable a bank to capitalize on potential synergies through, for generative ai use cases in banking example, the joint development of reusable components or the consolidation of learnings across the organization. Ideally, the model promotes operational efficiency while fostering innovation and adaptability. To capitalize on the most promising opportunities from adaptive banking, banks will need several key building blocks to leverage the natural language orchestration and product manufacturing capabilities of Gen AI.

BANKING EXCHANGE FLY IN CONFERENCE

As large language models (LLMs) continue to advance, GenAI is emerging as a key tool in helping bank compliance professionals stay more current on the regulatory landscape, and ultimately optimize their risk and compliance programs. This capability stems from GenAI’s power to generate ChatGPT profound insights from new information and even recommend next steps based on historical actions. Today, more than 50% of tech leaders within the financial services industry are interested in exploring AI applications, signaling a trend of increased adoption of this technology.

“What we find with generative AI is that you rarely ever make the best better, but you make the low-end and middle better, and therefore you shift the whole curve,” he said. We must be patient and go step-by-step with a roadmap in mind – things never advance as fast as we expect. Nevertheless, whatever our level of exposure to, and interest in, AI solutions, this technology is going nowhere but upwards. While it is clear that treasurers will benefit from AI, usage is still in its infancy. In the 2023 EACT survey, we saw that digitalisation and AI are important but not a top priority for corporate treasurers.2 It seems that there are many other issues to be fixed before thinking about AI. Unlike its predecessors, generative AI’s applications transcend conventional boundaries, promising unforeseen possibilities and reshaping our understanding of creativity and interaction between machines and humans.

Use our hybrid cloud and AI capabilities to transition to embrace automation and digitalization and achieve continued profitability in a new era of commercial and retail banking. As banks monitor initial use cases and partnerships, they should continually evaluate use cases for scaling up or winding down, as well as assessing which partnerships to consolidate. Banks will also need to decide how the control tower will interact with the different lines of business, and how ownership of use cases, budget, success and governance should be spread or centralized. Banks can use GenAI to generate new insights from the data they

collect on buying habits, trade patterns and internal tax

compliance and to createadditional revenue streams. The many banks that need to update their technology could take the opportunity to leapfrog current architectural constraints by adopting GenAI.

As AI technology rapidly advances, it will automate complex cognitive tasks and decision-making at an unprecedented rate. We are now at the beginning of the fourth wave of AI – characterised by the intersection of AI with other emerging technologies such as the internet of things (IoT), cloud computing and augmented reality. AI will have a major impact, but exactly how is not yet clearly defined – we are still trying to figure it out.

Major banks, especially those in North America, have been pioneers in this journey, making substantial investments in AI to spearhead innovation, talent development and operational transparency. Their investment strategies encompass a wide range of applications, including enhancement of fraud detection mechanisms and customer service chatbots. Their focus is on acquiring critical hardware, such as NVIDIA chips for AI processes, and making strategic investments in human and technological resources. The aim of refining existing processes is driving this strategic shift, combined with an ambition to explore and capitalize on high-impact AI use cases, balance potential benefits against risks, and scale innovative prototypes into robust solutions. Financial institutions are prioritizing the integration of AI to address pressing challenges and enhance their competitive edge.

Feedback and best practices will be collected from users across different countries to refine and enhance AI applications within the bank. In addition to providing licenses, OpenAI will offer training and the latest updates for its large language models (LLMs), which underpin ChatGPT. By closely collaborating with OpenAI, BBVA aims to identify and implement the most effective AI use cases within its business processes.

Key use cases include automating regulatory reporting, improving fraud detection, personalizing customer service, and optimizing internal processes. By leveraging LLMs, institutions can automate the analysis of complex datasets, generate insights for decision-making, and enhance the accuracy and speed of compliance-related tasks. These use cases demonstrate the potential of AI to transform financial services, driving efficiency and innovation across the sector. Banks investing in Gen AI are poised to perform strongly in the future as this technology continues to drive change in the industry. The success stories of Bank of America’s Erica and NatWest’s Cora demonstrate the significant impact that Gen AI can have on customer engagement and operational efficiency.

Similarly, GFC encompasses a broad set of regulations aimed at ensuring financial institutions operate within the legal standards set by regulatory bodies. Compliance with these regulations is crucial to avoid hefty fines and maintain the trust of stakeholders. 1 Why most digital banking transformations fail—and how to flip the odds (link resides outside ibm.com), McKinsey, 11 April 2023. In recent years, AI has revolutionized various aspects of our world, including the banking industry. In this video, Jordan Worm delves into five key areas where AI is making groundbreaking impacts on banking. Institutions, on their part, must integrate ethical considerations into the design and architecture by developing a responsible design framework for ethical AI usage.

Moreover, the use of AI in fraud prevention, as seen with Mastercard and Revolut, showcases the potential for enhanced security and cost savings for financial institutions. As more people gain confidence in Gen AI, we can expect to see continued investment and innovation in AI technologies within the banking sector, ultimately leading to a more seamless and personalised banking experience for customers. Gen AI is poised to revolutionize banking by dynamically creating responsive services, potentially adding US$200b to US$400b value by 2030.