How AI is Transforming Customer Service, Security, and Financial Management in Banks

To increase the effectiveness of communication, it is important that proposals and suggestions for improving the customer’s financial health are tailored to the customer’s situation and interests. BBVA account and card transactions are classified to one category or another based on certain attributes. The name of the business, its business activity code, the details of the receipt, type of transaction, etc., allow identification of whether it is a payroll entry or an expense for food, fuel, transportation or clothing, for example. Triodos Bank believes AI systems must have human dignity at their core, and be humanity-centred, upholding fundamental rights and benefitting broader societal wellbeing. People should always be in control; any decision on ethical issues that could affect the rights and dignity of groups and individuals should never be fully outsourced to machines.

It is very good at finding patterns in data and reacting quickly, cheaply, and usually reliably. As the private sector adopts AI, it speeds up its reactions and helps it find loopholes in the regulations. As we noted in Danielsson and Uthemann (2024a), the authorities will have to keep up if they wish to remain relevant.

Now, many mature banks and financial institutions are moving to the next level with ML, natural language processing (NLP), and GenAI. Understanding how to build trust between humans and AI will be key to shaping the future of finance. Big banks and investment firms are using artificial intelligence (AI) to help make financial predictions and give advice to clients. Using AI, valuation models can consider more robust scenarios and sensitivities that impact valuation and merger consequences. Additionally, due diligence can potentially be automated, using natural language processing to analyze contracts or lengthy financial documents like credit agreements. Proactive governance can drive responsible, ethical and transparent AI usage, which is critical as financial institutions handle vast amounts of sensitive data.

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This proactive approach enables banks to mitigate risks more effectively, safeguarding customer assets. While using AI applications, data privacy and compliance with regulatory requirements are crucial for maintaining customer trust and meeting industry standards. Financial institutions are prioritizing the integration of AI to address pressing challenges and enhance their competitive edge. Key use cases include automating regulatory ChatGPT App reporting, improving fraud detection, personalizing customer service, and optimizing internal processes. By leveraging LLMs, institutions can automate the analysis of complex datasets, generate insights for decision-making, and enhance the accuracy and speed of compliance-related tasks. These use cases demonstrate the potential of AI to transform financial services, driving efficiency and innovation across the sector.

Generative AI will play an important role in corporate transformation by improving key processes and efficiency and providing individualized client engagement, tailored offerings, and effective data exploitation. 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. In order to stay competitive in a data-driven and dynamic business environment, embracing AI financial modeling is becoming less of an option and more of a necessity. Those who successfully integrate AI into their financial processes stand to gain significant advantages in terms of financial insights, risk management, and decision-making. AI’s predictive analytics can help companies detect anomalies early, allowing risk management teams to design comprehensive plans to mitigate potential risks.

Learn how to transform your essential finance processes with trusted data, AI insights and automation. Between growing consumer demand for digital offerings, and the threat of tech-savvy startups, FIs are rapidly adopting digital services—by 2021, global banks’ IT budgets will surge to $297 billion. As we have explored, navigating the complexities of AI integration necessitates a comprehensive approach that fosters responsible development and implementation. In this regard, EY has demonstrated its commitment to responsible AI development with its platform, EY.ai, launched in September 2023 with an investment of US$1.4 billion. This platform aims to be a comprehensive solution for businesses seeking to leverage AI for transformative outcomes. Meanwhile, collaborations with FinTechs and Web 3.0 innovations are forging new paradigms in financial services.

Key take-aways

In a competitive landscape, banks are constantly seeking to reduce costs, pioneer new products and services that gain customer support, and advance their market share. 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. The generative AI market in finance is poised for significant growth, with projections indicating a surge from 1.09 billion U.S. dollars in 2023 to over 12 billion U.S. dollars by 2033.

While AI is powerful on its own, combining it with automation unlocks even more potential. AI-powered automation takes the intelligence of AI with the repeatability of automation. For example, AI can enhance robotic process automation (RPA) to better parse data analytics and take actions based on what the AI decides is best.

As economic volatility continues to rise, CFOs face increasing pressure to ensure operational efficiency while also spearheading digital transformation. The challenge lies in adopting new technologies to stay ahead of the competition, while managing the complexities of today’s financial landscape. The answer to this challenge might lie in harnessing the power of artificial intelligence (AI).

This not only for the EU-sake but also to position Europe as a global leader in this space other jurisdictions will follow when considering their own approaches towards the regulation of the AI. Therefore, it is recommended that financial institutions start to consider how to incorporate the Guidelines into their AI governance model. For financial institutions operating in multiple jurisdictions, it is further recommended to check if there are potential conflicting obligations between the Guidelines and regulations in other jurisdictions to ensure compliance globally.

She holds a PhD from the Media Lab at MIT and an Honorary Doctorate from the University Miguel Hernández. She is an IEEE Fellow, and ACM Fellow, and EurAI Fellow and elected permanent member of the Royal Academy of Engineering of Spain. She is well known for her work in computational models of human behavior, human computer-interaction, mobile computing and big data for social good. It will start with two keynote talks, from the perspectives on either side of the bridge topic of human modeling in AI. This will be followed by a poster session where authors of accepted papers will be invited to present their work.

The Guidelines also acknowledges that there could be ways to achieve the goal of properly managing AI risks, and financial institutions can adopt more cost-effective methods to achieve the same goal. If industry associations are looking to establish self-regulatory rules for the use of AI, the Guidelines may serve as a reference. Before the establishment of self-regulatory rules, it is recommended that financial institutions follow the Guidelines for the application of AI. The Guidelines specially mention that branches of international groups in Taiwan may follow existing rules of the group if the AI systems are provided by the group.

The finance sector could lead the way in using artificial intelligence to transform business during a period of investment in the technology across many sectors. Recommendations are then delivered in “an interactive, conversational format with lower incremental client servicing costs than human advisers.” AI is more accurate than manual fraud detection methods or rules-based anti-fraud software, improving fraud detection processes, Sindhu said. In 2024, 58% of banking CIOs surveyed reported they had already deployed or are planning to deploy AI initiatives this year, according to Jasleen Kaur Sindhu, a financial services analyst at Gartner.

B8: Exploring the use of Federated Learning for Data-Sensitive applications

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. In the GCC, enthusiasm is even higher with two thirds expecting revenue increases and a similar number expecting profitability increases. While these statistics cover various industries, the banking sector specifically has been heavily reliant on technology since its inception. In a dynamic banking environment, banks are seeking to differentiate themselves and gain a competitive advantage. Generative Artificial Intelligence (GenAI) is transforming the banking sector, providing innovative solutions that optimise efficiency, enhance security, and increase customer satisfaction.

Anne Goujon from BGL BNP Paribas emphasized the effectiveness of their AI-anti-fraud tool, which has reduced false alerts by 75% and increased detection rates to over 90%. If your organization is ready to explore the possibilities of IBM watsonx Assistant and related technologies, try watsonx Assistant for free or embed watsonx in your solutions. This 2024 IBM IBV CEO Study revealed that product and service innovation is CEOs’ top priority for the next 3 years, with generative AI opening the door to a new universe of opportunity. Get stock recommendations, portfolio guidance, and more from The Motley Fool’s premium services.

In crises, this homogenising effect of AI use can reduce strategic uncertainty and facilitate coordination on run equilibria. The key to understanding financial crises lies in how financial institutions optimise – they aim to maximise profits given the acceptable risk. When translating that into how they behave operationally, Roy’s (1952) criterion is useful – stated succinctly, maximising profits subject to not going bankrupt. That means financial institutions optimise for profits most of the time, perhaps 999 days out of 1,000.

Generally, artificial intelligence is the ability of computers and machines to perform tasks that normally require human intelligence, such as identifying a type of plant with just a picture of it. With ChatGPT setting off a new revolution in AI, we could just be seeing the start of AI in the financial industry as these companies find new ways to use this breakthrough technology. Embedded Lending and AI stand out as the vanguards of this transformation, propelling the sector into a new era of efficiency and customer-centricity.

Generative AI-driven tools can also evaluate historical data, market trends and financial indicators in real time. This ability enables accurate risk assessments, aiding banks in making more informed decisions regarding loan applications, investments and other financial operations. These AI capabilities help banks optimize their financial strategies and protect themselves and their clients. ThetaRay, which employs its own proprietary machine learning algorithms, takes a risk-based approach to targeting financial crime. Using a large swath of data points, the firm’s AI learns the normal behavior of banking customers in what’s known as “unsupervised learning,” a type of machine learning that learns from data without human oversight. This allows the technology to spot anomalies based on behavioral patterns, rather than human instruction.

The Motley Fool reaches millions of people every month through our premium investing solutions, free guidance and market analysis on Fool.com, top-rated podcasts, and non-profit The Motley Fool Foundation. AI can help improve customer experience by evaluating a borrower’s past spending behavior and credit history, to provide customized offers that are best suited to the client’s personal needs via for example digital assistants. Customers demand a seamless, end-to-end, consistent lending experience that delivers fast decisions and immediate availability of funds. AI can increase customer satisfaction and retention, as well as attract new customers and segments  by for example proactively identifying cross- or up-sell opportunities in the client portfolio. Arka Daw is a Distinguished Staff Fellow (DSF) at Oak Ridge National Lab (ORNL), where he is a member of the Center for Artificial Intelligence Security Research (CAISER). He is also affiliated to the Emerging Cyber Systems Group in the Cyber Resilience and Intelligence Division of the National Security Sciences Directorate.

At BBVA, we want to further promote our role as pioneers when it comes to innovating in financial services and we are therefore firmly committed to exploring the potential of this technology. We believe that generative AI, when used safely and responsibly, is a game-changer in how we support our customers in their decisions and offer personalized services. It also happens to stimulate creativity among our employees,” explains Ricardo Martín Manjón, Global Head of Data at BBVA. The call to action emphasizes the need for financial institutions to adopt AI technologies proactively, leveraging their potential to enhance compliance and operational efficiency.

Development

Informed by extensive user feedback obtained through a design thinking approach, this tool assists development practitioners who work on digital projects by saving time in data searches for policy dialogues and project design and implementation. Both the private and the public financial sectors are expanding their use of artificial intelligence (AI). Because AI processes information much faster than humans, it may help cause more frequent and more intense financial crises than those we have seen so far. BBVA uses advanced analytics to identify groups of customers with similar needs in order to tailor the financial health plan to each individual case.

As banks continue to refine AI applications and address these challenges, they are poised to achieve greater efficiency and security. This integration not only enhances efficiency but also sets a new standard for financial management in the banking industry. By leveraging AI, banks can offer more accurate financial insights and streamline operations, enabling businesses to make informed decisions quickly.

• AI will also be useful in ordinary economic analysis and forecasting, achievable with general-purpose foundation models augmented via transfer learning using public and private data, established economic theory, and previous policy analysis.
• Additionally, variance analysis can be automated to quickly identify deviations from the budget or forecast.
• Addressing the “black box” issue involves implementing explainable AI techniques that provide insights into model behavior and decision-making processes.
• As the banking sector embraces the transformative potential of AI, including the innovative development of GenAI, it is encountering a complex landscape of challenges and opportunities.

The better these tools get, even if we’re talking about human-in-the-loop, there is the risk that people start to shut their brain off because it does seem so good at what it does. There is the autonomous interaction with the customer, which is the highest risk element of what we do. We have to be able to explain very clearly through our policies and our procedures what those models are going to do, and they are going to do them consistently in a way that’s fair to the customer. I generally take a very selective approach when it comes to making those reorganization changes.

Discover how EY insights and services are helping to reframe the future of your industry. His research specializes in lifelong machine learning for computer vision and natural language processing. He is anticipated to receive his PhD in Machine Learning in November 2023 from the School of Interactive Computing at the Georgia Institute of Technology, advised by Dr. Zsolt Kira. Additionally, he serves as a Board Member for the non-profit research organization, ContinualAI. She is co-founder and vice-president of ELLIS.During the COVID-19 pandemic, she was Commissioner to the President of the Valencian Government on AI and Data Science against COVID-19. Previously, she was Director of Data Science Research at Vodafone, Scientific Director at Telefónica and researcher at Microsoft Research.

The integration of artificial intelligence (AI) into various banking operations is accelerating. From enhancing customer service to improving security measures, AI is revolutionizing how banks operate. 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. The cost-saving potential of artificial intelligence only adds to its appeal to banks and other financial companies. If you’re looking for an investment opportunity, consider some of the stocks above, as well as other AI stocks or AI ETFs if you’re looking for a broad-based approach to the sector.

How Artificial Intelligence is Going to Make Your Analytics Better Than Ever

The evolution of AI in banking has been nothing short of revolutionary, moving from foundational concepts to the creation of sophisticated, innovative applications. Finance professionals and team leaders should assess their own or their team’s current skill levels and identify the specific areas where AI training would be most beneficial. The Machine ChatGPT Readable Transcripts dataset aggregates data from earnings calls delivered in a machine-readable format for Natural Language Processing (NLP) applications with metadata tagging. Alfaro also remarks that while ChatGPT Enterprise is certainly a major strategic commitment, it will not be the only solution to be used within the organization.

It promises considerable cost savings and efficiency improvements, and in a highly competitive financial system, it seems inevitable that AI adoption will grow rapidly. There is high momentum for using AI technology, including GenAI tools, for fraud detection and regulatory compliance. Machine learning can be used to analyze data in real time to look for unusual patterns and flag new fraud tactics. GenAI is used to model normal banking behavior and identify activities that deviate from the norm, enabling banks to spot emerging threats.

Automating middle-office tasks with AI has the potential to save North American banks $70 billion by 2025. Further, the aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total. In wealth management, AI is unlocking personalized advice and risk assessment opportunities. These advancements represent a new frontier where AI intersects with core financial operations, propelling the sector into an era of unprecedented innovation and efficiency. AI uses customer behavior, transaction patterns, and preferences, hence recognizing their needs.

The power of these models lies in their versatility acquired through the large set of data sources used for training, making them exceptionally flexible. This means that each foundation model can be reused in countless downstream applications, whether for use of artificial intelligence in finance specific-intended-purpose or general-purpose AI systems. For this reason, the Parliament imposes stringent requirements for the foundation models, including an obligation to disclose when the AI system is trained with data protected under copyright laws.

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. GenAI  offers tremendous potential for enhancing efficiency, personalisation, and customer engagement in the banking sector. To mitigate these risks, banks need to implement additional security measures, particularly in securing data, ensuring its accuracy and completeness, and maintaining service availability. Nazanin Mehrasa is a Senior Machine Learning Researcher at Borealis AI, focusing on AI for financial services.

The disruptive power of GenAI extends beyond banking to wealth management, insurance and payments, transforming customer engagement, transaction processing and fraud detection. Addressing issues such as algorithmic bias, data privacy, and the appropriate level of human oversight is crucial to maintaining trust and transparency. You can foun additiona information about ai customer service and artificial intelligence and NLP. By tackling these challenges head-on and ensuring that AI is implemented responsibly, finance leaders can position their teams to thrive in an AI-powered world.

BBVA is continuing to evaluate other tools that may prove viable for the more than 100 use cases to be rolled out over the course of 2024. Developments in AI have accelerated tremendously in the last few years, and FP&A professionals might not even know what is possible. It’s time to expand our thinking and consider how we could maximize the potential uses of AI.

The future of financial services lies in the effective integration of AI, and institutions must act now to harness its benefits and stay competitive in a rapidly evolving regulatory landscape. Generative AI supports IT development by automating coding tasks, generating code snippets, and assisting in quality assurance processes. Additionally, AI plays a crucial role in modernizing legacy systems, enabling them to support advanced applications and meet evolving business needs.