How GenAI is evolving customer journeys in retail banking

When it comes to GenAI specifically, banks should not limit their vision to automation, process improvement and cost control, though these make sense as priorities for initial deployments. GenAI can impact customer-facing and revenue operations in ways current AI implementations often do not. For example, GenAI has the potential to support the hyper-personalization of offerings, which helps drive customer satisfaction and retention, and higher levels of confidence. The insights and services we provide help to create long-term value for clients, people and society, and to build trust in the capital markets. Marketing emerged as the most common area for banks to use GenAI, cited by 47% of banking leaders polled. A related SAS study, based on a separate survey of marketing professionals, found that banking marketers most frequently use GenAI for customer interactions (44%) and generating written copy (33%).

This was pretty influential when COVID hit, allowing Cora to become our superhero and support hundreds of thousands of customers to access payment holidays on loans, credit cards and mortgages. For a deeper exploration of these valuable insights, we invite you to join the two public stage sessions hosted by NTT DATA at Sibos. These sessions will provide a comprehensive overview of our findings from the survey and offer insights from NTT DATA’s experts.

Global deal activity fell to $350 billion last year, from $530 billion in 2022. Retrieval-Augmented Generation (RAG) techniques, which enhance LLMs by integrating external knowledge sources, add another layer of complexity. Effective governance frameworks must be established to manage these sophisticated AI systems. Unlike gen ai in banking traditional machine learning models, which often require extensive feature engineering and domain-specific adjustments, LLMs can generalize from vast datasets without the need for such tailored configurations. Well, we’re at a moment in time where the pace of AI development is rapid but still a little uneven.

Use Cases of AI in Accounts Payable Operations

The largest players are aggressively investing in developing their AI infrastructure and scaling use cases to capture more value. Daniel Pinto, JPMC’s President and COO, recently estimated that gen AI use cases at the bank could deliver up to $2 billion in value. “Temenos Explainable AI offers transparent, auditable insights while our Generative AI infused platform delivers these insights instantly in an intelligent and personalised way.

Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders. Build AI applications in a fraction of the time with a fraction of the data. Adopting GenAI will help banks realise these objectives through various use cases. In this report we touch base on these scenarios, their benefits and primary risks.

Generative artificial intelligence is about to change the banking experience for clients

And by January it was estimated to have reached 100 million monthly active users.1 Bankers poured back into the office with dreams of massive productivity improvements and — perhaps — a bit more free time. Learn how Brazilian bank Bradesco is giving personal attention to each of its 65 million customers with IBM Watson. Dubbed “Project Agora,” the initiative will build on a unified ledger concept proposed by BIS that bridges tokenised commercial bank deposits and tokenised wholesale central bank money. The launch of DBS-GPT, our employee-facing version of ChatGPT, is helping employees with content generation and writing tasks in a secure environment.

BIS members include the central banks of prominent economies such as Australia, China, France, Belgium, Japan, South Korea, Italy, Switzerland, the United Kingdom, and India, among others. The BIS recently teamed up with seven central banks to explore asset tokenisation within the monetary system alongside private financial institutions. Moreover, as AI-generated content becomes even more conversational and widespread, the importance of early disclosure of how GenAI may influence their products and services is paramount. Risk and compliance professionals should consult their company’s legal team to ensure these disclosures are made at the earliest possible stage. No technological integration is worth exposing a bank’s sensitive information to potential hackers or leaving data open to compromise, and GenAI integration is no exception. However, by employing the latest guidance, risk and compliance professionals can support a secure rollout.

This capability stems from GenAI’s power to generate profound insights from new information and even recommend next steps based on historical actions. It certainly has the ability to streamline operations – including client service, marketing, compliance and other shared service/cost centres. We believe Gen AI has the potential to help unlock value trapped in siloed systems and data – value that can accrue to banks, their customers and their investors. Generative AI, particularly LLMs, enables the development of sophisticated chatbots and virtual assistants that deliver personalized and efficient customer service.

Artefact, an IBM Business Partner headquartered in Paris with 1,500 employees globally, used IBM watsonx.ai AI studio to help a large French bank gain insights into consumer habits. Asteria Smart Finance Advisor gives Asteria’s small and medium enterprise (SME) clients immediate insight into the financial health of their businesses. The virtual advisor can also answer financial questions and advise them on which products are most relevant to their specific business and financial situation. For financial institutions using third-party AI systems, the paper emphasises the importance of maintaining oversight while leveraging external expertise. This includes establishing clear lines of responsibility and maintaining appropriate levels of internal expertise to effectively manage these relationships. Real time payments are transforming the payment industry but this shift from cash to digital transactions presents security challenges that need to be addressed.

These use cases demonstrate the potential of AI to transform financial services, driving efficiency and innovation across the sector. FinTech Magazine connects the leading FinTech, Finserv, and Banking executives of the world’s largest and fastest growing brands. Our platform serves as a digital hub for connecting industry leaders, covering a wide range of services including media and advertising, events, research reports, demand generation, information, and data services. With our comprehensive approach, we strive to provide timely and valuable insights into best practices, fostering innovation and collaboration within the FinTech community. David Parker is Accenture’s global financial services industry practices chair who covers the impact of technology and fintech on the banking, capital markets and insurance industries.

The research also reveals that AI is already impacting headcount, with 38% of banks believing AI can reduce the number of business analysts needed for these projects. An additional 27% anticipate this reduction will occur within the next 1-2 years, and 28% foresee it happening within 3-4 years. Every bank in Europe is currently readying its infrastructure to meet the SEPA Instant Payment Regulations deadlines, which many think are unrealistic. Meanwhile, banks in the US are trying to meet the growing momentum for instant payments, while Canadian banks are preparing for the launch of their instant payments scheme as early as 2026. This was combined with insight drawn from employees of varying levels of seniority, said Liz Kohler, Managing Director of Strategy, Operations and Growth at The Roux Institute at Northeastern University.

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Evolving regulations create uncertainty about compliance requirements and the liability risks banks could face. From a resiliency perspective, banks need to be prepared for hackers, fraudsters and other bad actors taking advantage of the power of GenAI. Because regulation is catching up, firms will need to think about how they build and enable systems that anticipate developments in regulation, rather than building processes that might be overtaken by restrictions. Similarly, banks looking to deploy must bear in mind regulators’ claims that existing rules will apply to GenAI. “Banks should resist legacy thinking when identifying opportunities with GenAI. Existential risks posed by disrupters and new market forces demand that banks go beyond automation to reimagine banking business models,” says EY-Parthenon Financial Services Leader Aaron Byrne.

• Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.
• However, GenAI can help mitigate these regulatory risks, by creating marketing materials across geographies that contain the appropriate tone, language, and cultural references, while also supporting consumer understanding of each product, in each locale.
• If data feeds are incomplete or the training, prompting and monitoring aren’t up to scratch, the technology can slip into bias, hallucinations (false answers) or toxicity (harmful language).
• Model benchmarking provides a standardized approach to evaluating AI performance, ensuring that models meet regulatory and operational standards.
• 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.

That all takes massive amounts of computing power, loads of data and access to highly skilled people. Centers of excellence may help balance that cost in the initial phases but will likely slow adoption in the long run. 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.

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For example, Deloitte’s Trustworthy AI™ framework includes a series of guiding principles to ensure GenAI trustworthiness and reliability. Many traditional banks’ initial indecisiveness in rolling out AI prompted many analysts to predict that more dynamism of challenger or neobanks could end their dominance. And challenger banks have doubtless upped the stakes, especially in customer service and with product innovations such as Buy Now, Pay Later (BNPL). But the premise that they are displacing traditional banks in the US and Europe is unproven. Generative AI’s potential to rescript the business of banking implies almost limitless applications. However, having poured millions if not billions into digital banking, GCC banks may hesitate over another round of technology investment expenditure.

As such, for Harmon, while this new technology “could be really exciting, gain efficiencies and insights into customer behaviour, it must be implemented correctly”. GenAI is going to be hugely transformative – in the way that powerful new technologies tend to be. When we launched the very first mobile banking app back in 2009 there was a similar level of optimism, excitement and concern about what it might mean.

The paper aims to help the financial services industry better understand how Gen AI can  be leveraged for efficiency and innovation. It proposes key recommendations for industry  adoption and integration, including the need for industry-level accepted test data sets, air  usage policies, AI sandboxes and streamlined regulations. It also offers a view of a  strategic “composable Gen AI” roadmap for companies beginning their AI journey. Increased efficiency, and reducing operating costs, is perhaps GenAI’s most well-known benefit.

Many times, manual contract processing becomes a bottleneck for efficiency and risk mitigation due to the sheer volume of agreements, varying complexities and the ever-evolving landscape of compliance. Fortunately, the advent of new technologies like generative artificial intelligence (GenAI) offers a transformative solution to streamline and enhance contract management processes. Data, however, is a core capability gap for most MENA banks, despite years of spend on data lakes; challenges range from incomplete and inconsistent data on customers, products and transactions, as well as disparate data sources and technologies. Focused effort is required to produce robust, augmented and synthetic data sets for customer needs profiling, product profitability analyses, risk and regulatory compliance model training. Finally, access to data remains a challenge for over 65% of financial institutions, with fragmented data ownership and governance limiting the ability to rapidly adopt GenAI and machine learning (ML) technologies at scale. Forward-thinking technology teams in large financial institutions are also applying Gen AI solutions to harmonize legacy enterprise technologies, thereby reducing technical debt and freeing up operating costs for innovation.

These methods are closer to pattern-matching systems – for example, payment authorisation systems that tag a particular transaction as potentially fraudulent by comparing them with verified instances of actual fraud. Market insights and forward-looking ChatGPT perspectives for financial services leaders and professionals. The first is the implementation costs — building out new apps, training them, integrating them into existing systems, testing them, putting them into production and so on.

With a lower interest rate environment in view, the KPMG team anticipates supporting tailwinds for banks’ investment banking business, as equities are expected to be more attractive. Local banks enjoyed strong margin performance and moderate growth in their overall balance sheets during last year, according to Paul McSheaffrey, senior banking partner, Hong Kong, at KPMG ChatGPT App China. Cybersecurity measures are so important for banks today, with institutions like JPMorgan facing up to 45 billion hacking attempts a day, employing over 60,000 technologists for the primary aim of combatting cyber attacks. With natural language processing, the bank was able to quickly digitise paper-generated information, quickly analysing and processing it.

TUATARA also helped leading cooperative bank BS Brodnica continue to challenge the status quo in customer service. The organization, which was one of the first cooperative banks in Poland to offer digital banking services, looked to harness AI automation to give its customers access to instant, high-quality support. Thanks to the transformative benefits promised by generative artificial intelligence (AI), the banking and financial sectors are at a turning point. You can foun additiona information about ai customer service and artificial intelligence and NLP. From redefining a bank’s competitive edge in customer relationships to streamlining core banking operations and strengthening cyber-resiliency, AI technologies can unlock numerous new capabilities. Acquisitions and joint venture opportunities can help banks build new or enhance existing GenAI-focused ecosystems and deliver new products and solutions more quickly.

Forging the right balance between capability, responsibility and value creation can help build confidence in your GenAI strategy and enable your organisation to move out in front. A key part of this shift in mindset is a readiness to experiment even if this can end up in failure. Accepting this up front as part of a ‘fail fast’ approach would help you to quickly identify what went wrong, address it and learn from the experience. So a good starting point for identification and prioritisation is to gauge the value of a process such as onboarding, the number of hours you currently spend on it and the data available to support it. You can then look at how GenAI can help you to not only do this in less time and at lower cost, but also better.

• Major Gulf banks, including Al Rajhi Bank of Saudi Arabia, Qatar National Bank, and National Bank of Kuwait are already using AI to varying degrees.
• Cut through the noise to determine how your particular business can benefit and how this would support your overall strategy – tackling specific paint points or enhancing customer experience, for example.
• Determine how to build fluency with GenAI across your business, with training, talent acquisition, and partnerships.
• Traditional AI, which excels at analysis and automation, has been in use for some time now.
• Fintechs remain at the forefront of harnessing gen AI and many of their use cases and solutions are impacting financial services.

These AI systems can interpret and respond to diverse customer queries, provide real-time assistance, and offer tailored financial advice. By enhancing client engagement, AI-powered solutions improve customer satisfaction, reduce response times, and free up human resources for more complex tasks. The integration of AI in client engagement represents a significant advancement in delivering personalized and efficient financial services. “GenAI is obviously a major trend across sectors right now, but maybe most significantly in financial services,” said Alex Kwiatkowski, Director of Global Financial Services at SAS. The Financial Services sector has undergone substantial digital transformation in the past two decades, enhancing convenience, efficiency, and security. Gen AI is now catalyzing a significant shift, with 78% of surveyed financial institutions implementing or planning Gen AI integration.