Banking Reinvented: How Advanced Generative AI Models Are Shaping the Industry

Banks must also recognize GenAI as just one piece of an overall innovation agenda. 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. By automating repetitive tasks, bank workers are freed from mundane responsibilities and are able to focus on complex problem-solving and strategic gen ai in banking initiatives. AI-driven support tools provide real-time data analysis and insights, enhancing the quality and speed of decision-making. Furthermore, Generative AI tailors training modules to individual learning styles, accelerating employee development and skill acquisition. This synergy between human expertise and technological capabilities unlocks a new level of productivity and innovation within organizations.

The technology is already changing work every day for most employees at most banks. We concluded that 73% of the time spent by US bank employees has a high potential to be impacted by generative AI—39% by automation and 34% by augmentation. Its potential reaches virtually every part of a bank, from the C-suite to the front lines of service and in every part of the value chain. Market insights and forward-looking perspectives for financial services leaders and professionals. For now, most applications of generative AI and large language models (LLMs) that you may have seen in banks have been limited to lower-risk internal purposes. When it comes to using gen AI in highly regulated sectors like banking, the onus is on us in the industry to shape the conversation in a constructive way.

For example, Deutsche Bank is testing Google Cloud’s gen AI and LLMs at scale to provide new insights to financial analysts, driving operational efficiencies and execution velocity. There is an opportunity to significantly reduce the time it takes to perform banking operations and financial analysts’ tasks, empowering employees by increasing their productivity. The first wave heavily focuses on human-in-the-loop reviews to ensure the accuracy of model responses. Using gen AI to check itself, such as through source citations and risk scores, can make human reviews more efficient. By moving gen AI guardrails to real time and doing away with human-in-the-loop reviews, some companies are already putting gen AI directly in front of their customers. To make this move, risk and compliance professionals can work with development team members to set the guardrails and create controls from the start.

Organizations with advanced data platforms will be the most effective at harnessing gen AI capabilities. Banks shouldn’t underestimate the data and tech demands related to a gen AI system, which requires enormous amounts of both. For one, the process of context embedding is crucial to ensure the accuracy and relevance of results.

KPMG people combine deep industry experience with extensive technology capabilities to help you achieve your organization’s goals. Moreover, this technology significantly enhances customer experiences by ensuring services are closely tailored to individual needs and preferences. Let’s explore more details and specific use cases of Generative AI in banking and financial services. While generative AI holds big promise for the banking industry, most of the current deployments are limited to just a few banking areas or don’t go beyond the experimental phase.

Define priority areas and set goals

Your business can then evolve with it to start with Generative AI step by step. Of course, working with Generative AI in the banking sector has its challenges and limitations. For example, a customer may need help understanding how much of a mortgage they can afford.

Risks related to data privacy, security, accuracy and reliability are banks’ top concerns for GenAI implementations. That’s understandable given that large language models (LLMs) can be subject to hallucination and bias. The prevalence of sensitive and confidential data in banking raises concerns about accidental data breaches and erroneous transactions.

Customers can effortlessly track spending patterns, monitor subscriptions, and manage payments. Additionally, AI-driven algorithms generate detailed financial models and forecasts, providing bankers with a clearer picture of likely consequences. This blend of efficiency, accuracy, and insight is reshaping the landscape, ultimately leading to better outcomes for both investors and clients. When ChatGPT launched to the public in late 2022, many wondered if generative AI was a fad or a genuinely transformative phenomenon.

It can slow execution of the gen AI team’s use of the technology because input and sign-off from the business units is required before going ahead. This article was edited by Barr Seitz, an editorial director in the New York office. This article will appear in the first themed issue, on the Future of Technology, which will launch in October. Sign up for the McKinsey Quarterly alert list to be notified as soon as other new Quarterly articles are published. We anticipate that changes in the tech skills landscape will accelerate, requiring HR and tech teams to become much more responsive in defining (and redefining) how skills are bundled into roles. If every company needs to be a software company, do you have a software organization that can deliver?

While we’re still in the early stages of the Generative AI revolution powered by machine learning models, there’s undeniable potential for vast changes in banking. Verticals within financial services predicted to undergo significant transformation include retail banking, SMB banking, commercial banking, wealth management, investment banking, and capital markets. The high interest in gen AI solutions in the banking industry highlights its transformative potential and practical applications.

It will access a wider range of secure information sources, providing answers on products, services, and even career opportunities within the NatWest Group. Cora+ aims to be a safe, reliable digital partner, helping clients navigate complex queries with ease and improving accessibility to data. Instead, they turned to Gen AI, a powerful tool that swiftly parsed the dense regulatory document, distilling it into key takeaways.

Acquisitions and joint venture opportunities can help banks build new or enhance existing GenAI-focused ecosystems and deliver new products and solutions more quickly. The business case for such deals should be based on a careful assessment of capabilities and with results from initial use cases. At MOCG, we’re not just a Generative AI development company; we’re your strategic partner in capitalizing on AI to optimize your banking operations. Our team of seasoned experts is well-versed in a wide range of models, including GPT, DALL-E, PaLM2, Cohere, LLaMa 2, and other LLMs. Cora, NatWest’s virtual assistant, is getting a Generative AI upgrade with the help of IBM and their Watsonx platform. This enriched version, Cora+, will offer customers a more conversational and personalized experience.

These gains in operational performance will flow from broad application of traditional and leading-edge AI technologies, such as machine learning and facial recognition, to analyze large and complex reserves of customer data in (near) real time. May 29, 2024In the year or so since generative AI burst on the scene, it has galvanized the financial services sector and pushed it into action in profound ways. The conversations we have been having with banking clients about gen AI have shifted from early exploration of use cases and experimentation to a focus on scaling up usage.

Increasingly, customers expect their bank to be present in their end-use journeys, know their context and needs no matter where they interact with the bank, and to enable a frictionless experience. Numerous banking activities (e.g., payments, certain types of lending) are becoming invisible, as journeys often begin and end on interfaces beyond the bank’s proprietary platforms. For the bank to be ubiquitous in customers’ lives, solving latent and emerging needs while delivering intuitive omnichannel experiences, banks will need to reimagine how they engage with customers and undertake several key shifts. A recent survey from Insurify found that 22% of Gen Z rely on TikTok for financial advice.

Risk management has also greatly benefited from AI’s predictive analytics and risk modeling tools, allowing for better decision-making and risk mitigation strategies. Banks’ traditional operating models further impede their efforts to meet the need for continuous innovation. Most traditional banks are organized around distinct business lines, with centralized technology and analytics teams structured as cost centers.

According to data compiled by Pew Research Center in 2023, TikTok stood out for its user growth, as 33% of American adults admitted to using the platform, which was an increase of 12 percentage points from 2021. As social media platforms become more ingrained in our daily lives, it’s clear that we rely on them for more than just entertainment. GOBankingRates’ editorial team is committed to bringing you unbiased reviews and information. We use data-driven methodologies to evaluate financial products and services – our reviews and ratings are not influenced by advertisers.

Some financial institutions like mortgage brokers or investment companies provide financial advice to their customers using gen AI technology. This can be one of the best Generative AI use cases for financial service companies. Such financial advisors and businesses can combine human expertise with the power of AI to give consumers more comprehensive and customized financial plans. Using conversational AI in the banking sector has become increasingly prevalent in recent years. Major financial institutions such as Bank of America and Wells Fargo have integrated this technology as the backbone of their AI virtual assistants.

They can then translate these insights into a transformation roadmap that spans business, technology, and analytics teams. Few would disagree that we’re now in the AI-powered digital age, facilitated by falling costs for data storage and processing, increasing access and connectivity for all, and rapid advances in AI technologies. These technologies can lead to higher automation and, when deployed after controlling for risks, can often improve upon human decision making in terms of both speed and accuracy. The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually (Exhibit 1). The integration of Gen AI in banking has the potential to transform the sector, yet it is not without its challenges.

A checklist of essential decisions to consider

It can automatically generate syntheses of counterparty transition plans and compare them against actual emissions to evaluate progress toward goals. To further demystify the new technology, two or three high-profile, high-impact value-generating lighthouses within priority domains can build consensus regarding the value of gen AI. They can also explain to employees in practical terms how gen AI will enhance their jobs. This article was edited by Jana Zabkova, a senior editor in the New York office. Business units that do their own thing on gen AI run the risk of lacking the knowledge and best practices that can come from a more centralized approach. They can also have difficulty going deep enough on a single gen AI project to achieve a significant breakthrough.

Let’s explore the seven use cases of Generative AI in modern banking in the USA, Canada, and India. Gen AI has the potential to revolutionize the way that banks manage risks over the next three to five years. It could allow functions to move away from task-oriented activities toward partnering with business lines on strategic risk prevention and having controls at the outset in new customer journeys, often referred to as a “shift left” approach. That, in turn, would free https://chat.openai.com/ up risk professionals to advise businesses on new product development and strategic business decisions, explore emerging risk trends and scenarios, strengthen resilience, and improve risk and control processes proactively. While smartphones took many years to move banking to a more digital destination—consider that mobile banking only recently overtook the web as the primary customer engagement channel in the United States6Based on Finalta by McKinsey analysis, 2023.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Financial institutions are already actively employing Gen AI in their operations, and the technology’s potential for transforming the industry is vast. Understanding and determining customer needs in order to recommend solutions specific to those necessities while exercising discretion in confidential matters is key to building perfect client relationships and loyalty. Generative AI in banking can make savings advice for certain accounts based on previous user activity. For example, if you add $XX more to your retirement plan (RRSP), you could receive a higher return of $$. Few technologies have moved from theoretical potential to game-changing impact as quickly as generative AI.

Now, the race is on to do so again with an even more transformative technology. Leveraging gen AI to reinvent talent and ways of working, the top banking technology trends for the year ahead and the mobile payments blind spot that could cost banks billions. 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. The aged, heavily-customized technology architectures in place at many banks today, with all their workarounds and poor data flows, are a barrier to AI implementation. Recognizing these constraints, a significant proportion of survey respondents said they did not believe their institution had the correct technological infrastructure and capabilities to implement GenAI.

That flexibility pertains to not only high-level organizational aspects of the operating model but also specific components such as funding. The AI-first bank of the future will need a new operating model for the organization, so it can achieve the requisite agility and speed and unleash value across the other layers. Third, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction. This involves allowing customers to move across multiple modes (e.g., web, mobile app, branch, call center, smart devices) seamlessly within a single journey and retaining and continuously updating the latest context of interaction. Leading consumer internet companies with offline-to-online business models have reshaped customer expectations on this dimension.

This goes beyond generic advice, ensuring that tips align with individual needs and preferences, ultimately enhancing the customer’s journey. Additionally, the technology relies on market trends and economic forecasts to provide up-to-date investment insights. In our analysis of US banks, we discovered that occupations representing 41% of banking employees are engaged in tasks with higher potential for automation. Roles such as tellers, whose jobs primarily involve collecting and processing data, would benefit greatly from automation—60% of their routine tasks could be supported by generative AI.

This article explores the various applications of AI in banking, the benefits it offers, and the challenges it presents. First and foremost, as with any new technology, banks need to have a clear goal that aligns their efforts to business impact. This poses a significant barrier to the large-scale adoption of Gen AI in the banking industry. Gen AI also provides a new tool that fraudsters could use increase the sophistication and scale of their scams.

By integrating business and technology in jointly owned platforms run by cross-functional teams, banks can break up organizational silos, increasing agility and speed and improving the alignment of goals and priorities across the enterprise. The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies. It will innovate rapidly, launching new features in days or weeks instead of months.

To seize the GenAI opportunity, banks should reimagine their future business models based on the new capabilities GenAI enables and then work backward to prioritize near-term use cases. New AI-enabled capabilities across the business can create new opportunities to monetize data, expand product and service offerings, and strengthen client engagement. While the technology Chat GPT is enhancing customer-facing services, it’s also making significant strides in the realm of investment banking and capital markets. It empowers analysts to rapidly sift through mountains of data, revealing hidden patterns and potential opportunities that might otherwise go unnoticed. Complex risk assessments become more streamlined, allowing for informed decision-making.

Apply genAI across the process and you can start to run the various steps in parallel. And these kinds of applications could deliver productivity gains of, say, 75 percent. As a bank, you don’t just want to gain new customers; you also want to retain existing ones, and gen AI tools can help you achieve this.

Generate Financial Advice for Customers Based on Proprietary Data

Generative AI, powered by advanced machine learning models, including gen AI models, is revolutionizing the banking and financial sectors. This technology is reshaping the landscape of AI and automation in banking by introducing efficient solutions to automate previously time-consuming tasks. The dynamic landscape of gen AI in banking demands a strategic approach to operating models. Banks and other financial institutions should balance speed and innovation with risk, adapting their structures to harness the technology’s full potential. As financial-services companies navigate this journey, the strategies outlined in this article can serve as a guide to aligning their gen AI initiatives with strategic goals for maximum impact. Scaling isn’t easy, and institutions should make a push to bring gen AI solutions to market with the appropriate operating model before they can reap the nascent technology’s full benefits.

Additionally, banks will need to augment homegrown AI models, with fast-evolving capabilities (e.g., natural-language processing, computer-vision techniques, AI agents and bots, augmented or virtual reality) in their core business processes. Many of these leading-edge capabilities have the potential to bring a paradigm shift in customer experience and/or operational efficiency. Generative AI is poised to revolutionize the banking and financial sectors, offering innovative solutions to enhance operational efficiency and customer experiences. This advanced technology, capable of processing and interpreting vast amounts of data, enables banks to automate complex tasks, provide personalized services, and detect fraudulent activities with greater accuracy. The future of AI in banking includes transformative applications that enhance operational efficiency and customer experiences.

The second factor is that scaling gen AI complicates an operating dynamic that had been nearly resolved for most financial institutions. While analytics at banks have been relatively focused, and often governed centrally, gen AI has revealed that data and analytics will need to enable every step in the value chain to a much greater extent. Business leaders will have to interact more deeply with analytics colleagues and synchronize often-differing priorities. In our experience, this transition is a work in progress for most banks, and operating models are still evolving. AI has significantly impacted customer service, enabling banks to provide personalized, efficient, and seamless experiences through chatbots, virtual assistants, and natural language processing. Additionally, AI has bolstered fraud detection and prevention measures by employing machine learning algorithms and pattern recognition techniques.

Generative AI is a game-changer when it comes to enhancing the customer experience in banking. With the ability to analyze and learn from vast amounts of customer data, AI-driven systems can create highly personalized experiences tailored to individual preferences and needs. This level of personalization extends to product recommendations, targeted marketing campaigns, and customized financial advice. Generative AI refers to algorithms that can create new data samples by learning patterns from existing data. At its core, generative AI involves the development of algorithms that can create or generate new content, such as text, images, code, and even music, based on the patterns and structures identified from a vast array of input data. This type of AI has become increasingly important in the banking industry due to its potential to improve efficiency and accuracy in various applications.

• Convolutional natural network is a multilayered neural network with an architecture designed to extract increasingly complex features of the data at each layer to determine output; see “An executive’s guide to AI,” QuantumBlack, AI by McKinsey, 2020.
• Thinking through how GenAI can transform front-office functions and the overall business model is essential to maximizing technology’s return on investment.
• By integrating business and technology in jointly owned platforms run by cross-functional teams, banks can break up organizational silos, increasing agility and speed and improving the alignment of goals and priorities across the enterprise.
• In the United States, NIST has published an AI Risk Management Framework, and the National Security Commission on AI and National AI Advisory Council have issued reports.

To be clear, banks have every reason to be cautious when it comes to AI — generative AI in particular. Large language models and generative AI systems are trained on massive amounts of data, leaving significant room for bias to creep in. Some chatbots have been deployed to manage employee queries about product terms and conditions, for example, or to provide details on employee benefits programs. KPMG professionals have helped banks pilot genAI as information extractors to find anomalies within contracts or flag potentially fraudulent transactions. GenAI has also been used to quickly create bits of code that allow legacy systems to interact with new technologies. Of course, no one should take gen AI’s explanations as gospel, especially when it comes to something as critical as banking.

Now, they see genAI emerging and are asking themselves (and the rest of the business) how this new and disruptive technology might change their world for the better. In conjunction with proper data governance practices, privacy design principles, architectures with privacy safeguards, currently existing tools can help anonymize, mask, or obfuscate sensitive data, feeding into those systems and models. In enterprise gen AI implementations, banks maintain control over where their data is stored and how or if it is used. When fine tuning the data, the banks’ data remains in their own instance, whereas the LLM is “frozen.” The learning and finetuning of the model with the bank’s data is stored in the adaptive layer in its instance. Here at Aisera, we offer Generative AI tools tailored to different industries, including the financial services and banking industries. However, the deployment of generative AI in banking comes with its challenges, including data privacy concerns and the need for regulatory compliance.

The analyst uploads all the relevant documents and then queries the chatbot to ensure it has the material it needs. Then, the analyst can instruct the tool to produce many of the slides that are typically needed and many others that reflect the specifics of the proposed investment. The tool saves analysts about 30 percent of the time they used to spend creating pitchbooks. In this article, we look at the areas where gen AI has the most potential for corporate and investment banks, and the risks that banks need to watch for. We conclude with an outline of the capabilities that banks will need if they are to thrive in the era of gen AI.

AI-powered virtual assistants are available around the clock to answer inquiries and offer guidance tailored to each individual’s goals. Meanwhile, behind the scenes, Gen AI optimizes back-office processes, reducing operational costs and minimizing human errors. Crucially, generative solutions play a vital role in providing a safer financial space for all. The combination of enhanced customer service and internal efficiency positions the technology as a cornerstone of modern retail banking. Where it gets amazing is when it starts to fundamentally change ‘the possible’.

The banking industry has long been familiar with technological upheavals, and generative AI in Banking stands as the most recent influential development. This advanced machine learning technology, adept at sifting through vast data volumes, can generate distinct insights and content. Implementing gen AI initiatives involves strategic road mapping, talent acquisition, and upskilling, as well as managing new risks and ensuring effective change management.

The same holds for generative artificial intelligence (Gen AI), the deep-learning technology that can generate human-like text, images, videos, and audio, and even synthesize data for training other AI models. Formerly limited to physical establishments, banking has morphed into a completely digital realm, due in no small part to generative AI. This model ensures critical decisions on funding, new technology, cloud providers and partnerships are made efficiently. It also simplifies risk management and regulatory compliance, providing a unified strategy for legal and security challenges. In the financial services industry, new regulations emerge every year globally while existing rules change frequently, requiring a vast amount of manual or repetitive work to interpret new requirements and ensure compliance. Developers need to quickly understand the underlying regulatory or business change that will require them to change code, assist in automating and cross-checking coding changes against a code repository, and provide documentation.