Top Finance Execs and Junior Bankers Tell Us How They Are Using AI

AI is affecting retail checkout and cashier positions as well, reducing the need for human employees. These systems can handle transactions independently, manage inventory and even collect data on customer behavior — such as purchase frequency and average basket weight. Adobe Photoshop’s new Generative Fill feature is one example of the way generative AI can augment the graphic design profession.

Most banks are using a centralized model to kick-start generative AI, promote transparency, align on roles and priorities, and extract the most value from scarce resources. Later, they can shift to a hub-and-spoke model in which individual business units maintain ownership of specific use cases. In mid-August, HKMA launched a GenAI sandbox with the government-funded incubator tech hub Cyberport. The aim is to let financial institutions pilot use-cases within a risk-managed framework and with technical assistance. Gen AI could help with drafting project specifications; writing and debugging code; creating synthetic data with which to stress new solutions’ fraud and risk systems; code refactoring; and more.

• The bank took a top-down approach to adoption, adding Chief Data and Analytics Officer Teresa Heitsenrether to its technology leadership team in June 2023, and putting an AI assistant called LLM Suite in the hands of 140,000 employees last month.
• Risk and compliance professionals should consult their company’s legal team to ensure these disclosures are made at the earliest possible stage.
• It’s a “lack of available use cases rather than a deliberate decision not to,” a fundamental analyst at one of the world’s biggest hedge funds told BI.
• While proofs of concept might work initially, the widespread application of use cases requires enhancements consistent with a larger scale, echoing DevOps principles.

The feature lets people with no photo editing experience make photorealistic edits using a text prompt. Other tools — such as Dall-E and Midjourney — also create realistic looking images and detailed artistic renderings from a text prompt. The trucking industry uses AI for driver assistance and accident prevention systems, route planning, predictive maintenance and more advanced driver training systems. AI will help people improve their work experience by automating rote, repetitive tasks. The technology will maximize the “goods” of work while minimizing the “bads.” This may contribute to a surge in AI jobs and increased demand for AI skills.

Key features of LLMs and their applications

Besides answering questions, the prototype also compares various products the bank offers that will be relevant for a specific customer. AI could drive productivity gains for banks by automating routine tasks, streamlining operations, and freeing up employees to focus on higher value activities. The goal is to explore, in a safe and responsible manner, how generative AI can expedite processes, improve productivity, and foster innovation thanks to its abilities to create text and images and process information, among other features. One place banks are starting to see time savings from the use of generative AI is in software development (like Citi’s use of Github Copilot).

Furthermore, neobank Revolut has announced the launch of an AI-based advanced scam detection feature to help protect its customers from malicious card scams. In case of potential scams, customers are guided through an intervention process within the Revolut app. EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, ChatGPT each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. By leveraging AI, financial institutions can enhance the efficiency and effectiveness of their IT development processes, ensuring that their technology infrastructure remains robust and capable of supporting innovative AI solutions.

The bank took a top-down approach to adoption, adding Chief Data and Analytics Officer Teresa Heitsenrether to its technology leadership team in June 2023, and putting an AI assistant called LLM Suite in the hands of 140,000 employees last month. Benchmarking AI models involves rigorous testing against standard datasets to evaluate their performance. Continuous documentation and updating of AI models ensure they remain compliant with regulatory standards and perform consistently over time. Financial institutions must document and justify AI-driven decisions to regulators, ensuring that the processes are understandable and auditable. Predictability in AI outputs is equally important to maintain trust and reliability in AI systems. The advent of AI technologies has made digital transformation even more important, as it has the potential to remake the industry and determine which companies thrive.

The virtual advisor can also answer financial questions and advise them on which products are most relevant to their specific business and financial situation. The insurer teamed up with IBM Business Partner® TUATARA to reimagine its customer service experience. In one month, Generali Poland rolled out Leon, a virtual assistant built with action.bot from TUATARA, based on IBM watsonx Assistant. “Security pervades our business, from protecting the systems themselves to new capabilities that we’re bringing out for our customers,” says Ed McLaughlin, chief technology officer at Mastercard. Mastercard also has invested deeply in fraud prevention, spending $7 billion on cybersecurity over the past five years, including the acquisition of new technologies and developing AI tools that make it easier to identify fraud. Mastercard has also invested in around 20 different startups to get a first look at emerging security tools that the company may want to use to support future readiness in combating fraud.

Bringing customer data to life

Temenos Generative AI also provides tailored productivity insights, such as where tuning can improve Straight Through Processing (STP). Next up, Vivian Yeung, Executive Vice President, Chief Digital & Technology Officer at Fremont Bank, examined what AI in action looks like. Yeung offered examples on how AI is being used to improve the customer experience across different industries and how financial services are being used to personalize the customer experience. She also takes a look into the future of the customer experience and considers the ethical implications of AI implementation. The expanding use of AI has raised demand for specialized abilities that extend beyond the standard repertoire of most data scientists and engineers.

More than 40% of respondents seek to harness “data as a product” as the architectural paradigm. This stance stems from a desire to meet regulatory requirements related to data handling, and to expedite progress toward advanced analytics. Given how broadly generative AI may change both internal operations and customer-facing interactions, banks will need to develop a broad view of the underlying systems and data that enable the technology to make a difference. Most banks will not be laying the bricks and mortar of their AI foundations from the ground up, but rather will make incremental advances and upgrades that align with their ambitions. Deploying solutions at scale requires consideration of the supporting capabilities in such areas as user interfaces, data integrations, process design, and training. Selecting the sequence of use cases will depend not only on data-driven scenario planning around industry trends but also on customers’ priorities and behaviors.

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 continue to prioritize AI investment to stay ahead of the competition and offer customers increasingly sophisticated tools to manage their money and investments. Customers continue to prioritize banks that can offer personalized AI applications that help them gain visibility on their financial ChatGPT App opportunities. Regulatory compliance is paramount for financial institutions looking to employ AI and go AI-first. While this is worrying, work is being done to respond to concerns about bias, discrimination, and other ethical nightmares that come with improper use of the technology.

Additionally, AI can also help with predicting the market situation, providing insights that can help financial institutions make better decisions and maintain financial market stability. But the banking sector’s use of the technology, popularised by OpenAI’s chatbot ChatGPT, is still at an early stage, the HKMA said. Most firms are using “off-the-shelf” third-party solutions for business functions such as summarisation, translation, coding and internal chatbots, according to the regulator. Use cases could expand potentially to customer-facing chatbots and robo-advisers in wealth management and insurance, it added. Across the banking sector, leaders are weighing AI’s immense potential against the risks it may pose to privacy and security. We are in the midst of one of the largest business transformations in the financial services industry.

As a highly regulated industry, banking has a vested interest in AI governance issues. Many people still struggle to use mobile banking applications due to literacy challenges, language barriers, age, or physical impairments. AI can support these groups with voice-activated banking as well as personalized financial advice. You can foun additiona information about ai customer service and artificial intelligence and NLP. AI can also be used to derive the creditworthiness of the unbanked populace by analyzing alternative data for credit scoring, especially for those with limited to no credit history. A growing number of vendors have incorporated generative AI functionality into their off-the-shelf solutions. Examples include tools for project managers and software developers, such as Atlassian, and customer experience products, such as those by Genesys.

Financial institutions must stay informed about evolving regulatory requirements and adapt their AI strategies accordingly. For instance, in financial services, they can generate detailed reports, summarize regulatory documents, and predict potential compliance issues based on historical data patterns. Traditional ML models rely on predefined features and specific training data, limiting their flexibility. In contrast, LLMs are pre-trained on extensive datasets, allowing them to generalize across various tasks without extensive customization. This generalization capability reduces the need for domain-specific adjustments and enables LLMs to adapt to new use cases quickly. In financial services, this adaptability allows LLMs to handle diverse tasks such as compliance monitoring, customer service, and risk assessment with minimal reconfiguration.

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Also, while AI can automate and streamline many processes, it should not have the final say in critical decisions such as loan approvals. Instead, AI should handle data analysis and initial assessments, leaving the ultimate decision to human financial professionals. This approach ensures that AI serves as a powerful tool to enhance banking operations without overstepping its limitations.

Maufe noted that data has ended up in silos for various reasons including technology constraints and organizational preferences. He also said that the financial ecosystem contains a large amount of both structured and unstructured data. Concurrently, in Singapore, we worked with the Monetary Authority of Singapore as part of the MindForge consortium to develop a whitepaper that examines the risks and opportunities of GenAI for the financial sector. In institutional banking, we tapped GenAI to help reduce the time needed for relationship managers to fill in the ESG (environmental, social, and governance) risk questionnaire by summarising company reports and prepopulating relevant fields.

These frameworks require continuous monitoring, reporting, and updating to address evolving threats and regulatory changes. Financial institutions must implement robust systems to identify suspicious activities, conduct thorough customer due diligence, and maintain detailed records. The integration of generative AI into these systems can enhance their effectiveness by providing real-time analysis, improving detection capabilities, and streamlining compliance workflows. To stay ahead of technology trends, increase their competitive advantage, and provide valuable services and better customer experiences, financial services firms like banks have embraced digital transformation initiatives.

“A lot of the banks we talked to are not ready for scalable adoption of GenAI yet, with a lack of adequate data or infrastructure,” he said. 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 China. The bank has also been piloting generative AI, learning from those pilots and getting ready to put them into production. Swamy sees an overall shift in the industry from generative AI being cool, interesting and fun to being something that needs to have practical applicability. At Commonwealth Bank of Australia, Jermyn also leads a team that uses AI to protect the bank and its customers from fraud and scams. Krish Swamy, chief data and analytics officer at Citizens Financial Group, expects the bank’s investments in AI in 2025 and 2026 will be higher than in 2024.

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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. He’s also working on automating some of this work, so that data scientists can spend more time getting deep into the business problems, into the nuances of the data and into the solutions the clients are looking for. Burris also created a process that uses graph technology to detect and investigate organized crime rings. Only 7% of banking and capital-markets organizations are actively reskilling their workforces at scale, Smith added.

Ensuring the governance of AI through ethical frameworks, data privacy measures and protection mechanisms is paramount to sustaining trust and compliance. The regulatory environment for AI in banking is dynamic, posing challenges for both banks and regulators aiming to keep pace with technological advancements. Active engagement between banks and regulatory bodies is critical to the aim of establishing transparent and effective frameworks that guide the ethical and responsible use of AI. This effort focuses on eliminating bias in algorithms and enhancing the explainability of AI’s decision-making processes, which are essential to maintaining public trust and transparency.

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Generative AI in bankingGenerative AI is a revolutionary technology that can redefine all aspects of banking operations, providing banks with a competitive edge by delivering personalized services and boosting operational efficiency. This innovative technology allows banks to make insightful, data-driven decisions, manage risks effectively, and improve customer satisfaction. Therefore, this synthesis of the evolving landscape should not be the end, but rather a compelling call to action for banks globally. It is time to seize the moment and make strategic investments in GenAI, ensuring that these powerful technologies serve as the cornerstone for a new age of financial services that is equitable, ethical and exemplary in its efficiency and innovation. In every facet, from consumer banking to the precision required in tax compliance and legal operations, AI is a testament to our innovative spirit and commitment to progress.

3 min read – With gen AI, finance leaders can automate repetitive tasks, improve decision-making and drive efficiencies that were previously unimaginable. 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. The new products are launching in the first half of 2024, Visa says, with tools including Visa Deep Authorization, which is a new risk-scoring solution aimed to better manage payments that are made when a physical card isn’t present. “We are going through those guardrails, but for now, we don’t feel that we’re ready to go out directly to a customer,” says Ruttledge. Citizens Financial Group has explored more than 90 different use cases for generative artificial intelligence.

Likewise, upgraded plans with OpenAI’s ChatGPT and Anthropic’s Claude offer more control over the use of private data within those models. It is this more general and cognitively advanced AI that has sparked a mix of excitement, fear, obsession and paranoia. ChatGPT, Google’s Gemini, Claude and other generative AI services prompt such emotion due to their ability to inspire — and terrify — based on their humanlike reasoning, conversation and creative abilities. Moreover, concerns about AI are compounded by the ever-increasing number of data breaches and data-mishandling incidents at firms across many industries.

Low-code or no-code Gen AI platforms available in the market automatically generate documentation for the modernized codebase, as well as the API code for integration with the rest of the technology landscape. Some of the more innovative emerging Gen AI solutions also use Retrieval and Augmented Generation (RAG) to iteratively learn and adopt coding standards specific to each financial institution, based on architecture design and standards documentation. Through incremental development, the evolution of GenAI will pave the way for the most sophisticated applications in the banking sector.

Although many use cases may focus on customer experience applications, operational improvement is also an area of high value. In this environment, bank risk and compliance professionals have a unique opportunity to incorporate meaningful, measured GenAI capabilities into their workflows to help them manage risk, maintain compliance, and safely grow their business. AI-driven risk management solutions leverage LLMs to analyze vast amounts of transaction data, identify patterns indicative generative ai use cases in banking of fraudulent activities, and generate real-time alerts for potential compliance violations. These capabilities enhance the institution’s ability to detect and respond to financial crimes promptly, reducing the risk of regulatory breaches and financial losses. By integrating LLMs into risk management processes, financial institutions can improve the accuracy and efficiency of fraud detection and compliance monitoring, ensuring robust protection against financial crimes.

As we harness its capabilities, we pave the way for a financial sector that is not only more efficient and effective but also more just and responsive to the needs of a rapidly changing world. The transformative development of AI in banking — from enhancing operational efficiency and customer service to navigating regulatory changes and cybersecurity threats — demands a comprehensive and strategic approach. The potential for groundbreaking innovation and the necessity for ethical, transparent and responsible implementation are intrinsic to this process. 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. Large language model-powered (LLM- powered) code generation and debugging tools speed up such technology modernization efforts by identifying the underlying business logic constructs.