How banks can harness the power of GenAI Switzerland

However, alongside these benefits come substantial cybersecurity risks that must be managed to protect sensitive financial information and maintain trust in banking institutions. Model governance across financial crime models should be prioritised, with technology providers aiding in resource allocation and quality control. Providers also play a key role in supporting clients by offering detection tuning and effectiveness testing, enabling organisations to respond quickly to changes in risk and keep pace with regulatory expectations. This partnership ensures that top management gains insight into risk management, enhancing the organisation’s ability to mitigate financial crime effectively. Technology providers are no longer mere suppliers; they are now strategic partners helping businesses prevent financial crime. These providers enable companies to enhance their risk management processes, act as data-driven backbones, and offer state-of-the-art solutions without the burden of major in-house development projects.

Another critical aspect of responsible AI implementation in finance is data privacy and protection. As custom AI systems trained to work for a particular company would rely heavily on the sensitive financial data used by the model, ensuring the confidentiality and security of this information is paramount. This involves not only stringent cybersecurity measures but also clear data governance policies that outline how data is collected, stored, and used by AI algorithms.

The strategic investment Bangor Savings Bank has made in this initiative is indicative of how aware it is of the tides of change. Using Gen AI doesn’t have to be a harbinger for job cuts – instead, it can be the cause for banks to adapt to a new role in the community as a place for learning skills that improve its people’s employability. However, to harness the full potential of AI, businesses need to understand its capabilities and explore how they can be integrated into their specific operations. If you are interested in learning more about the Generative AI for Finance use cases showcased during the event, or if you would like to organize a Gen AI session for your own Finance team, we invite you to reach out to us. Whether you are considering developing Gen AI for Finance use cases at your own office or at the Garage, or if you want to have a broader conversation about the Future of Finance, our team is here to assist you. Together, we can explore this exciting frontier and delve into the Future of Finance, powered by Gen AI.

The integration of generative AI, particularly LLMs, offers transformative potential to automate compliance processes, detect anomalies, and provide comprehensive insights into regulatory requirements. A. Generative AI offers numerous applications in finance, ranging from customer engagement to risk management. It can be utilized to analyze customer sentiment, generate personalized financial advice, and automate investment strategies. The FinTech industry thrives on innovation, constantly seeking new ways to enhance its approach and drive profitability. Generative AI models play a pivotal role in this quest for advancement, offering a range of valuable tools and techniques that finance businesses leverage to achieve their goals.

Morgan Stanley is pointing the way to how new large language models now enable these interactions to become a rich vein of insight that every organization will want to take advantage of,” Murray said. Available at Shinhan Bank branches across South Korea, the AI bank tellers can be found at digital desks and smart kiosks. They are capable of handling 64 different consultation tasks often performed at ATMs, including deposits, credit loan applications, and deposit-backed loan executions.

GenAI: Balancing innovation, security, and customer-centricity

The business case for such deals should be based on a careful assessment of capabilities and with results from initial use cases. Jonathan Murray, the chief strategy officer at marketing firm Mod Op, agreed that the Morgan Stanley AI move has significant analytics potential. “The interactions most companies have with their clients are typically an untapped source of insight.

Generative AI will fundamentally change the way we interact with analytical software, and we aim to provide it as a service to finance teams. In the increasingly complex world of corporate reporting and consolidation, speed must not come at the expense of accuracy in the day-to-day work of specialist departments. Addressing issues such as algorithmic bias, data privacy, and the appropriate level of human oversight is crucial to maintaining trust and transparency.

Murray’s colleague at Mod Op, executive-in-residence Monica Richter, added that the potential value will be found in how deeply Morgan Stanley executives probe their newfound data. “Will they use AI to aggregate what is heard across clients to generate ideas for research or buy/sell orders? If a client spoke about starting to set up a retirement account, will AI assist in not just noting that request but automatically responding and emailing out key articles on IRAs, Roths, etc that match the client’s portfolio needs? The real benefit will come from every Morgan Stanley employee and contractor using the exact same package for those summaries, which means that the data will all be in the same format and can therefore be analyzed comprehensively. Whether you’re looking for structured finance expertise or macroeconomic data, our proven, integrated capabilities—covering credit, climate, ESG risk, and more—help you proactively mitigate risk, embrace innovation, and stay agile.

Generative AI will significantly improve risk identification and enhance enterprise risk management through identification of anomalies and improved risk assessment timelines. Generative AI also has potential use cases that create process and cost efficiencies, with areas of deployment across all functions. Large language models (LLMs), for example, can assist in driving a better customer experience, building a robust supply chain, improving operations, and supporting the human capital of the enterprise. While the impact and implications of generative AI are still being considered, the technology is emerging and evolving. Finance professionals need to understand both the business potential and the finance and accounting applications of generative AI.

Innovate or stagnate: Creating value from technology in asset management

Finally, AI systems can be used to monitor and detect fraud, as well as to comply with regulatory and ethical requirements, such as the AI Act. This can enhance the security and trustworthiness of lending, while minimizing the legal and reputational risks. In recent years, and even more rapidly since the launch of ChatGPT in 2022, AI and GenAI have emerged as a game-changers in various industries, and lending is no exception. Key lending activities involve assessing borrowers’ creditworthiness, loan origination, and managing repayment and default risks. In 2028, the market is expected to reach approximately €185 billion in Europe alone, with B2B accounting for around 40% of the volume, twice the share observed in 2022. Currently, B2B financing methods in both online and offline businesses are quite similar.

Looking ahead, AI is set to become an increasingly integral part of the BFSI landscape. “Given AI’s potential to add complementary value, it will become mainstream in the BFSI industry,” Unnikrishnan predicts. “Going forward, BFSI firms will increasingly leverage composite AI technologies – both predictive and Gen AI – for disruptive transformation.” But despite the enormous potential of AI in finance, its adoption is not without challenges. 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.

Implement real-time performance tracking, data analysis, and iterative enhancements to maintain the models’ effectiveness and relevance. Generative AI automates tax compliance processes by analyzing tax laws, regulations, and financial data to optimize tax planning and reporting. It helps businesses minimize tax liabilities while ensuring compliance with tax regulations. Generative AI has potential to streamline the process of generating financial reports by synthesizing data from multiple sources and presenting it in a structured format. This enables businesses to produce timely and accurate reports for stakeholders, regulatory authorities, and investors. Generative artificial intelligence in finance simplifies the process of searching and synthesizing financial documents by automatically extracting relevant information from diverse sources.

In the finance sector, Generative AI has become a tool that financial institutions cannot afford to overlook. It transforms operations and decision-making processes with unmatched capabilities. GenAI is revolutionising the banking industry by enhancing operational efficiency and customer satisfaction. As the market moves toward cashless banking, GenAI introduces a unique opportunity for banks to explore untapped possibilities and overcome existing limitations. Financial services CEOs in the region have acknowledged the necessity to evolve their business models to ensure sustainable outcomes for stakeholders and society, especially in the face of challenges, such as climate change and the rise of GenAI.

Strong use cases will include “high-touch” activities historically owned by people, which leverage large datasets or require a generative response logic. Authorities will likely expect firms to deploy advanced GenAI systems in areas like financial crime. 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.

Of course, as with any transformative technology, the actual integration of GenAI into how we work comes with its challenges. Two major concerns often raised are data privacy and the potential for AI to generate incorrect or nonsensical information, known as hallucinations. You can foun additiona information about ai customer service and artificial intelligence and NLP. Today, analysts, creatives, and other professionals can leverage these powerful tools to streamline their workflows. Tools like ChatGPT have played a pivotal role in this shift, making AI accessible without the need for deep technical know-how.

Our recent global research survey gives insights into key strategies and applications for GenAI in banking, and we look forward to sharing the results at Sibos 2024, the annual conference and exhibition organized by Swift. Specific training around data literacy, AI ethics, and ChatGPT human collaboration with AI systems is essential. The ability to adapt to evolving roles and responsibilities becomes a valuable skill in the context of generative  AI deployment. Finance should partner with HR to create capabilities to cope with the level and pace of change.

A few notable first-tier banks have integrated their mobile banking app with various third-party services, offering for example mobility and energy solutions. Building the foundations for a strong GenAI capability through the collection and curation of reliable proprietary data, is key in enabling tailored GenAI use cases that provide CFOs with a competitive advantage. By investing in uplifting their organisations data landscape CFOs can build the technology infrastructure required to store, process, analyse, and report on data with the help of generative AI. CFOs should shift from occasionally cleaning up data to an ongoing approach that creates, cleanses, and maintains data in pace with the business. This helps to define a clear data governance structure where data stewards, accountable for specific data sets, engage cross-functionally and with IT to understand requirements and are empowered to make data related decisions. Cognitive assistants are intelligent systems designed to enhance the capabilities of financial professionals.

This capability saves time for financial analysts and improves decision-making by providing comprehensive insights. Its integration into financial institutions profoundly improves efficiency, decision-making, and customer engagement. By automating repetitive tasks and optimizing workflows, Generative AI streamlines operations, reduces errors, and cuts costs, ultimately enhancing businesses’ bottom lines. Ultimately, the goal is to harness the power of GenAI responsibly, ensuring that innovation does not come at the cost of security and customer trust.

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. EY is a global leader in assurance, consulting, strategy and transactions, and tax services. The insights and quality services we deliver help build trust and confidence in the capital markets and in economies the world over.

To address transparency, financial institutions must implement explainable AI techniques that provide insights into how AI models arrive at their decisions. This involves using interpretable models, documenting decision-making processes, and providing clear explanations to stakeholders. In addition, references should be provided to the material that was used for producing outputs. LLMs like Granite from IBM, GPT-4 from OpenAI, are designed to intake and generate human-like text based on large datasets. They are employed in various applications, from generating content to making informed decisions, thanks to their ability to detect context and produce coherent responses. Embrace continuous monitoring and improvement post-deployment to adapt to evolving finance trends.

Furthermore, according to a report by BCG, finance functions within global companies are embracing the transformative potential of AI tools like ChatGPT and Google Bard. These tools are expected to reshape the future of work within the finance function, revolutionizing processes, enhancing efficiency, and driving innovation, requiring CFOs to gain a nuanced understanding of their impact. This paper presents recent evolutions in AI in finance and potential risks and discusses whether policy makers may need to reinforce policies and strengthen protection against these risks. Larger banks further along in their AI experimentation should establish a control tower function to not only provide direction and vision, but also document a high-level roadmap to achieving the firm’s GenAI goals.

Similarly, many banks have been pursuing industry verticalization and deposit retention strategies, as well as seeking new and diversified revenue streams. Build confidence, drive value and deliver positive human impact with EY.ai – a unifying platform for AI-enabled business transformation. Listed on the FTSE 100 Index, Experian is committed to empowering people worldwide to achieve their financial goals. The opinions expressed in this blog are those of Evan Schuman and do not necessarily represent those of IDG Communications, Inc., its parent, subsidiary or affiliated companies. This AI effort “will give time back to that support professional, time that they can spend in a higher-value way,” Picariello said in an interview with CIO.com.

Addressing these challenges and ethical considerations requires a proactive and collaborative approach. Finance professionals must engage in ongoing dialogue with regulators, industry peers, and academic experts to stay informed about best practices and emerging standards in AI governance. It is important to remember that the AI we’re using today is the worst we will see from this point forward.

With bank technology leaders suggest they are inundated with requests from the business for genAI support. 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. In the short time since generative AI first exploded onto the scene, executive leaders have found the prime benefit in terms of savings has not so much been money but time, finance leaders on a Controllers Council panel said Thursday. By following these principles, and embracing the possibilities of GenAI, financial organizations will be well positioned to meet the demands and challenges of tomorrow.

This marks a significant advance in the way financial insights are gleaned and utilised for strategic decision-making. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited (“DTTL”), its global network of member firms, and their related entities (collectively, the “Deloitte organization”). DTTL (also referred to as “Deloitte Global”) and each of its member firms and related entities are legally separate and independent entities, which cannot obligate or bind each other in respect of third parties.

• Financial institutions are encouraged to embrace AI technologies to stay ahead of regulatory demands and enhance their operational capabilities.
• Financial institutions must stay informed about changes in data privacy regulations and adapt their AI strategies accordingly to ensure compliance.
• The European Union has the AI Act, which establishes a common regulatory and legal framework for AI in the EU.
• Even if it works precisely as planned, some question whether this analysis could have a downside for Morgan Stanley.
• There might also be a time when it becomes accepted for students to use ChatGPT to aid with schoolwork.
• We’ll delve deep into these challenges, unveiling innovative solutions poised to overcome these obstacles and pave the way for transformative advancements in the finance industry.

Organisations now seek providers who can guide roadmap planning, execution, and final strategy design. Since ChatGPT 3.0 debuted in early 2023, generative artificial intelligence has been making waves in all industries, including financial services. As AI becomes more prevalent, companies need finance professionals who are well-versed ChatGPT App in these technologies. With a deep bench of AI talent, companies are better positioned to make data-driven decisions, identify new opportunities, and optimize their financial strategies. This strategic advantage can translate into improved business outcomes, such as increased efficiency, cost savings, and better risk management.

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. 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. Although regulators try to keep pace with technological development by issuing nonbinding guidelines, the territory lacks GenAI rules and regulations. In June, the Office of the Privacy Commissioner for Personal Data, Hong Kong’s privacy regulator, issued its first personal-data protection guidelines for firms using GenAI services.

From refining risk management frameworks to enhancing trading strategies and elevating customer service experiences, Generative AI plays a multifaceted role within JPMorgan’s ecosystem. Let’s delve into how top industry players are harnessing the power of Generative AI in banking and finance to revolutionize their approach, enhance customer experiences, and drive profitability. The innovative technology holds the potential to elevate businesses significantly. According to a Deloitte report, advancements in generative AI for finance could boost business productivity growth by 1.5 percentage points. Thus, finance businesses can see substantial gains in productivity and revenue by integrating generative AI into their processes.

Such a roadmap requires a rethink of the value chain and business model, a full assessment of technology architectures and data sets and evaluation of innovation investments. A control tower approach both provides GenAI leadership and coordinates ongoing execution and deployments. It’s critical that the right controls and metrics be put in place, with adjustments being made over time as business outcomes are tracked and needs change.

We see its potential, but for many, it is unclear how we can leverage it to improve our work right now. When looking at the emerging AI tools and their various generative applications, the opportunities they present to finance and accounting are tremendous. New gen ai in finance ways of working and processes that are affected will require organisational and structural changes. The deployment of generative AI across business functions will increase the rate of change, impact culture, and require strong change management capability.

Certain services may not be available to attest clients under the rules and regulations of public accounting. Artificial intelligence (AI) has been in use for several years in chatbots, virtual assistants, predictive analytics, and many other applications. But it’s generative AI specifically that has seen exponential growth within the past year or so. Generative AI refers to systems or models that can generate new content, such as images, text, or other types of data, based on the patterns and information they have learned from a training data set. IBM, a global leader in technology and consulting services, have shared their insights on generative AI with FinTech Magazine, discussing strategic priorities, regulatory compliance and talent acquisition challenges facing the industry today.

This collaboration between AI and human expertise results in more accurate reports produced in less time, without sacrificing quality. Embedded lending (EL), a subset of embedded finance, extends loans or credit through non-financial platforms such as retail, e-commerce, or travel services. This approach allows customers to access financing precisely when needed, bypassing traditional financial institutions.

For us at Moody’s, we think it’s crucial users understand that while GenAI is incredibly powerful, it’s not a replacement for human expertise. For example, an analyst might use GenAI to generate an initial draft of a financial report. The analyst then reviews and refines the report, adding insights and interpretations that only a human can provide.

The privacy regulator urged companies to establish internal AI governance committees that directly report to their boards. In time, use-cases could expand to include robo-advisers and customer-facing chatbots in private banking, wealth management and insurance, HKMA said. Rather, the technology augments an existing workforce by increasing processing capacity and quality, while freeing people to focus on relationships and customer facing roles, where human emotional intelligence matters. This capability has proved to be a game changer for meeting the challenges today’s banks and capital markets are facing. Nearly 100 per cent of leading financial services companies in the U.S. and Canada are implementing AI technology into their operations in some fashion.

Strategies for secure GenAI implementation in fintech becomes crucial in such scenarios. Companies must invest in blockchain-enhanced data governance, federated learning for model security, real-time AI audit trails, along with ethical AI framework for financial services, and cybersecurity stress testing for AI systems. As GenAI reshapes the fintech landscape, robust cybersecurity measures tailored to the unique needs of financial services become vital. By embracing the synergy between GenAI and cybersecurity, fintech companies can build more secure, efficient, and innovative financial systems that maintain the trust of both customers and regulators in an increasingly digital financial world. GenAI empowers banks to offer bespoke financial advice tailored to individual customer profiles, revolutionizing the way financial services are delivered. Traditional financial advisory services often rely on a one-size-fits-all approach, which can fail to address the unique circumstances and goals of each customer.