How gen AI is ‘raising the floor’ for explainability and access in financial services

This is shifting the paradigm in FS from a reactive service to one that is truly intuitive and responsive. It now handles two-thirds of customer service interactions and has led to a decrease in marketing spend by 25%. Rather than reactively engaging when customers have a request or issue, it could eventually anticipate and proactively reach out to customers before they even know something is wrong. Financial institutions are encouraged to embrace AI technologies gen ai in finance to stay ahead of regulatory demands and enhance their operational capabilities. By integrating advanced AI solutions like LLMs, banks can ensure robust compliance, improve customer satisfaction, and drive operational efficiencies. 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.

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. Giving an entire workforce the confidence and ability to query an enterprise’s data and pull insights can have a huge impact on how the enterprise operates and innovates.

The future of AI in finance is not about replacing human expertise but augmenting it. Keeping humans in the loop ensures that critical thinking and nuanced judgment continue to guide the finance function. This integrated collaboration between humans and technology could lead to a seismic shift in work culture, maximising productivity and granting the invaluable gift of time. AI may be adopted faster by digitally native, cloud-based firms, such as FinTechs and BigTechs, with agile incumbent banks following fast. Many incumbents, weighed down by tech and culture debt, could lag in AI adoption, losing market share. “The most effective way that financial institutions can cultivate data intelligence whilst complying with external regulations is by leveraging a data intelligence platform”, Russ explains.

Embedded finance allows customers to access financial products and services in a seamless and personalized way, without having to leave their preferred digital interface. It is enabled by the collaboration of banks, technology providers, and distributors of financial products via non-financial platforms. This is gaining traction, as more customers demand faster, easier, and more tailored financial solutions.

By implementing mitigation strategies, financial organisations can balance leveraging the benefits of GenAI and maintaining robust cybersecurity measures. This approach will help safeguard customer data, maintain trust, and drive sustainable innovation in the digital banking landscape. Generative AI (gen AI) has opened up new possibilities for financial crime detection, and its adoption in recent years marks a pivotal shift for the industry.

You can foun additiona information about ai customer service and artificial intelligence and NLP. These companies are able to gain insights beyond those using traditional dashboards and reporting. Each year, Sibos brings together over 9,000 thought leaders and decision-makers from around the world. This year’s event, themed “Connecting the future of finance,” will take place from October 21 to 24 in Beijing, China. To stay ahead of market trends in GenAI and banking, be sure to attend NTT DATA’s public stage sessions and presentations at stand G31. The finance team should be aware of adoption challenges and obstacles and help enable the deployment of generative AI.

considerations for finance teams about gen AI

The ability of LLMs to model sequences and make probabilistic decisions enables their application in complex analytical tasks. They can generate comprehensive reports by synthesizing information from multiple sources, summarize lengthy regulatory documents, and identify patterns indicative of compliance risks. These capabilities enhance the efficiency and accuracy of compliance processes, allowing financial institutions to respond proactively to regulatory requirements and potential risks. Additionally, LLMs can assist in training and onboarding by generating educational materials and interactive simulations for employees. For example, AI could analyze blockchain data to enhance security and transparency, automate smart contracts, and offer personalized financial services. Similarly, IoT data could be leveraged by AI for real-time financial forecasting, risk management, and ESG reporting.

Interpreting complex regulatory requirements helps businesses stay compliant and mitigate regulatory risks effectively. It will significantly help make the overall financial services process more secure, efficient, and customer-friendly. As banks continue on this journey, they can look forward to a more innovative and resilient future, with GenAI as a core component of their digital strategy. This ongoing commitment to innovation will be crucial for staying ahead of the competition and meeting the evolving needs of clients in a digital-first world. One of the most significant innovations in AI for financial crime prevention is transfer learning, it said. This technique allows models to apply knowledge from one task to related activities, enhancing detection capabilities and reducing the need for extensive data resources.

Banks increasingly adopt genAI to improve operations, from spend categorization and transaction monitoring to enhancing risk decisions and predictive customer service. This process involves enhancing raw transaction data with contextual information, including merchant identification, transaction location, payment processor details, and spending categories. Enriched data allows banks to create a comprehensive picture of customer behavior, enabling personalized services and accurate risk assessments. Beyond customer service, generative AI in banking is also transforming fraud detection and risk management. By analyzing vast amounts of transaction data, AI models can identify unusual patterns that might indicate fraudulent activities. This proactive approach enables banks to mitigate risks more effectively, safeguarding customer assets.

Innovate or stagnate: Creating value from technology in asset management

One example of a generative AI-powered marketing campaign was the #NotJustACadburyAd campaign, which used the digital likeness of Bollywood star Shah Rukh Khan to create thousands of hyper-personalized ads for small local businesses. The campaign used a microsite that enabled small-business owners to create their own version of the ad featuring the Bollywood star. 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. AI is already replacing jobs, responsible for nearly 4,000 cuts made in May 2023, according to data from Challenger, Gray & Christmas Inc. OpenAI — the company that created ChatGPT — estimated 80% of the U.S. workforce would have at least 10% of their jobs affected by large language models (LLMs).

“We believe the responsible use of AI can help create new opportunities for consumers looking to improve their financial literacy and overall financial health,” Experian representative Christina Roman said in the report. Dublin-based Experian determined that 67 per cent of Gen Z Americans are using the technology to assist with financial planning while 62 per cent of millennials do so. The analytics company surveyed 2,011 U.S. citizens in the two age groups for its findings. Millennial and Gen Z citizens in the United States have been taking advantage of AI tools like ChatGPT to help balance their finances, according to a new survey from an Irish data and tech company.

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It reports that a whopping 96% of users who leverage AI tools for financial management have positive experiences, with 77% using these tools at least weekly to support their financial goals. At Moody’s, we are at the forefront of this integration, setting a benchmark for how AI can revolutionize the industry. Our commitment is to continue exploring and implementing AI solutions that drive value for our clients and stakeholders. We believe that by leveraging the full potential of AI, we can transform financial analysis, making it faster, more accurate, and more insightful, ultimately leading to better outcomes for our clients, our organizations, and the industry as a whole. To address data privacy, we partnered with Microsoft to create a secure environment for our AI tools.

Translation requires a certain level of nuance, as translators need to be able interpret body language and emotions of the speaker or in the text they are translating. Our analysis also identified several roles and functional areas in finance that are currently experiencing low impact from generative AI. In cross-functional areas, we found that Executive Leadership, Ethics and Corporate Governance, Strategic Partnerships and Complex Problem Solving remain largely unaffected.

• Ultimately, that digital agent could customize pricing in real-time, delivering competitive offers to target customers, such as preferential lending rates, based on an enhanced measurement of their credit risk.
• Cognitive assistants can transform how banks and financial institutions interact with their clients.
• Regulatory bodies emphasize the need for financial institutions to demonstrate how AI models make decisions, particularly in high-stakes areas like AML and BSA compliance.
• In shaping their GenAI strategies and plans, banking leaders must recognize GenAI’s position alongside Web3, blockchain, quantum computing and other disruptive technologies.
• However, SymphonyAI believes that regulators should focus on understanding the risk of AI, rather than being involved in approving every AI model.

Horn noted that finance can be a very complex topic, that can be particularly difficult for smaller businesses that might not have the resources to have a dedicated Chief Financial Officer (CFO). With gen AI, he said that complex topics can be translated into an easier-to-understand natural language that can potentially enable a digital CFO capability for an organization. Moreover, GenAI is reshaping how organisations understand internal and external policies, market data, and how they generate insightful content in response.

Our goal is finding efficiencies for our employees and getting information to them to better serve customers. The assessment allows the Accelerating Insights initiative to take a more role-based approach, with some roles receiving more technical training than others, according to Bangor’s Director of Strategic Initiatives, Sandra Klausmeyer. 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. With experience in startups and as a bank CDO, Shameek Kundu explains how genAI is impacting fintech. Not to mention, enterprises cannot overlook the need for an airtight data governance strategy to help them adhere to stringent legislation.

KPMG Trusted AI, is our strategic approach and framework to designing, building, deploying and using AI solution in a responsible and ethical manner so we can accelerate value with confidence. This is about helping the business address big problems — speed to market with new products, for example, or risk processes. Understand the business outcome you want to achieve and then consider how you can use genAI to help solve those problems.

While the adoption of AI in financial analysis and decision-making processes offers numerous benefits, it also presents new challenges for finance professionals. To fully capitalize on the potential of AI, individuals and teams must develop the necessary skills and knowledge to use these tools effectively. Artificial intelligence (AI) technologies are rapidly transforming today’s business models, and the emerging Generative AI and advanced applications are presenting new opportunities and possibilities for AI in finance and accounting. From Generative AI to machine learning and other foundation model solutions, we look at the new era of AI innovations, the tools they may offer accounting and finance, and considerations for incorporating an AI framework for success. The Bud platform processes vast amounts of real-time data, providing actionable insights that improve customer engagement and operational efficiency.

AI-powered supply chain management tools can track supplies as they make their way through the various links and partners in the supply chain. AI in supply chain management has the potential to improve demand forecasting, inventory evaluation, customer communication, operational performance and even sustainability. GenAI could be used to monitor transactions and give detailed financial advice on how to save and spend efficiently. Travel companies can also use AI to analyze the deluge of data that customers in their industry generate constantly.

However, it is worth taking a step back from the hype to really understand what genAI is, what it can do, and the risks and opportunities involved. Gauging the more specific, financial impacts of AI remains a relatively elusive exercise, the leaders said. Often, marketing offers come under regulatory scrutiny for matters such as mis-selling and misinformation. For multinational organizations, cultural differences across regional markets can lead to product misunderstandings, which can create additional regulatory challenges. The partnership between OpenText and TCS brings together unique strengths that set it apart from other industry players.

“The large, general-purpose models are trained on a much larger dataset, often composed of data scraped from the web. This includes all manner of information,  including irrelevant or poor-quality data, which has a huge impact on the model’s output”, Russ points out. But for an LLM to be tailored to a specific need, it must first be trained and reasoned on an enterprise’s proprietary data. “Customised models are actually more cost-effective to run due to their smaller size. Not to mention, the smaller and higher quality datasets will result in the model producing more relevant and accurate results”, Russ notes. With all the hype around artificial intelligence (AI), it can be difficult to separate fact from fiction when it comes to what capabilities are available today vs. what might be available soon.

With transfer learning, financial institutions can refine risk management processes, meeting business growth demands without excessive financial crime control investments. The integration of GenAI into finance teams presents a unique opportunity to redefine the role of finance professionals. No longer confined to the traditional tasks of number crunching and data entry, they are now poised to become the architects of the financial future. GenAI empowers them to shift their focus from routine tasks to exploring ‘what-ifs’ and driving business innovation. This transformation is not merely a change in daily activities but a leap towards a more impactful and strategic role within organisations. The Financial Services sector has undergone substantial digital transformation in the past two decades, enhancing convenience, efficiency, and security.

Ensuring the integrity and security of financial data is crucial when deploying AI tools. In budgeting and variance analysis, AI tools can identify patterns and anomalies, improving accuracy and providing explanations for variances. Moreover, AI is enhancing forecasting techniques and predictive ChatGPT analytics to better forecast future performance, allowing finance professionals to develop sophisticated forecast models that can adapt to changing market conditions. AI improves the capability of translation services, enabling automated, real-time translation in multiple languages.

The integration of Generative AI into finance operations is expected to follow an S-curve trajectory, indicating significant growth potential. Have you ever considered the astonishing precision and growth of the finance industry? It’s a realm where errors are minimal, accuracy is paramount, and progress is perpetual.

Through a comprehensive understanding of systemic methodologies and partnering with a reliable development firm, businesses can effectively leverage Generative AI’s transformative potential to drive innovation and achieve their goals. Our latest 27th Annual CEO Survey indicated that leaders expect technology including GenAI and Machine Learning (ML) to be the centre of optimising costs, creating new revenue streams and improving the customer experience within their organisations. Middle East CEOs are also optimistic about the financial impact of GenAI, with 63% expecting the adoption of it in their organisation to increase revenue, while 62% said it would increase profitability.

By prioritizing data privacy, financial institutions can build trust with customers and regulators, demonstrating their commitment to ethical data practices. Global financial institutions must navigate a complex landscape of data privacy regulations, ensuring that their AI systems comply with varying requirements across jurisdictions. This involves implementing robust data governance frameworks, ensuring data anonymization and encryption, and maintaining transparency in data processing practices.

It aids in developing predictive models, automating financial reports, identifying anomalies, and refining trading strategies. By simulating different scenarios, generative AI improves decision-making, enhances risk management, and bolsters fraud detection, providing financial institutions with a robust tool for innovation and efficiency. Morgan Stanley, a stalwart in wealth management and financial services, is at the forefront of exploring AI-driven innovations to enhance its competitive edge. With a keen focus on leveraging Generative AI, Morgan Stanley aims to bolster its fraud detection capabilities, optimize portfolio management processes, and provide personalized financial advice to its clients. After all, a significant amount of financial service organizations’ marketing, onboarding, customer service, and regulatory reporting involves repetitive content creation. GenAI, on the other hand, can process repetitive content faster, and with fewer inaccuracies, while also checking things like localized marketing content in different languages for regulatory matters within each jurisdiction.

Combining Wipro’s consulting experience with Microsoft’s technology creates seamless AI solutions. Generative AI gets better at giving accurate and personalized responses as it learns over time. “The power of large language models at a scale that we now can access anybody can access… is the unlock”, Bill explains. “Good AI is always underpinned by good data and a solid understanding across the board. If the financial services sector wants to maximise the value of generative AI, then enterprises need to establish a strong data culture and build data intelligence as part of their overall data and AI strategies”.

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.

This involves conducting a meticulous needs assessment to precisely identify and define the challenges and objectives at hand. GANs consist of two neural networks, a generator and a discriminator, that are trained together competitively. Taking advantage of the transformational power of GenAI requires a combination of new thinking about a longstanding challenge for banks — how to innovate while keeping the lights on.

One of the effective applications of generative AI in finance is fraud detection and data security. Generative AI algorithms can detect anomalies and patterns indicative of fraudulent ChatGPT App activities in financial transactions. Additionally, it ensures data privacy by implementing robust encryption techniques and monitoring access to sensitive financial information.

New AI-enabled capabilities across the business can create new opportunities to monetize data, expand product and service offerings, and strengthen client engagement. In shaping their GenAI strategies and plans, banking leaders must recognize GenAI’s position alongside Web3, blockchain, quantum computing and other disruptive technologies. Long-term roadmaps must reflect how these technologies, when deployed in the right combinations, can transform core business functions (e.g., operations, finance, risk management, product development and sales). More importantly, they can also open new revenue streams and create entirely new value propositions. Experian, the global data and technology company, conducted the study to gauge how generative AI is impacting their approach to personal finance.