Fraud, productivity are top of mind for AI thought leaders in banks

Generative AI can also automate time-consuming tasks such as regulatory reporting, credit approval and loan underwriting. For example, AI can quickly process and summarize large volumes of financial data, generating draft reports and credit memos that would traditionally require significant manual effort. In the near term, banks should focus on driving forward the highest value potential opportunities while factoring in the level of risk exposure.

Several states — including California, Illinois, Texas and Colorado — have introduced or passed laws focused on protecting consumers from harms caused by AI. AI chatbots could also be used internally to help employees access their benefits and perform other self-service tasks. AI assistants and chatbots let users book flights, rent vehicles and find accommodations online and offer a personalized booking experience.

They are employed in various applications, from generating content to making informed decisions, thanks to their ability to detect context and produce coherent responses. Customers demand automated experiences with self-service capabilities, but they also want interactions to feel personalized and uniquely human. They are more likely to stay with banks that use cutting-edge AI technology generative ai use cases in banking to help them better manage their money. The IBM Partner Ecosystem is helping banking and financial institutions bring their generative AI dreams to life through IBM watsonx™ Assistant, a next-gen conversational AI solution. Temenos, a leader in banking software, has launched its Responsible Generative AI solutions, marking a significant advancement in AI-infused banking platforms.

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Generative Artificial Intelligence (GenAI) is transforming the banking sector, providing innovative solutions that optimise efficiency, enhance security, and increase customer satisfaction. To be clear, banks have every reason to be cautious when it comes to AI — generative AI in particular. Large language models and generative AI systems are trained on massive amounts of data, leaving significant room for bias to creep in. Another significant challenge is the integration of AI technologies within existing banking systems. Many banks operate with legacy systems that might not be compatible with new AI frameworks, which can create costly and time-consuming issues. The efficiency of generative AI in summarizing regulatory reports, preparing drafts of pitch books and software development significantly speeds up traditionally time-consuming tasks.

“This democratization of nefarious software is making a number of current anti-fraud tools less effective.” “What it says to me is the importance of AI, not just in terms of what it can do, but how fundamental it is [becoming] in terms of how a bank operates and how it creates value for its customers,” Sindhu said. Discover how AI revolutionizes consumer experiences and boosts business efficiency in India. “What I hear is that the very experienced coders get a little frustrated with it,” she said. “They’re like, it’s easier if I just do it myself.” And among very inexperienced developers, “it doesn’t really help because they can’t spot the mistakes” the generative AI model makes, she said. “These results are consistent with the idea that generative AI tools may function by exposing lower-skill workers to the best practices of higher-skill workers,” the report states.

The portfolio of AI investments should accelerate broader bank strategic objectives while capitalizing on near-term quick wins that offer clear value with minimal risk. Internally oriented use cases for generating content and automating workflows (e.g., knowledge management) are typical­­­­ly good starting points. While such front-office use cases can yield high-profile wins, they can also create new risks. Appropriate controls should inform initial planning and ­­help minimize the risk of damage to service quality, customer satisfaction and the bank’s brand and reputation. Banks must also recognize that regulators will pay particular attention to customer-facing use cases and those where AI enables automated decisioning. Enabled by data and technology, our services and solutions provide trust through assurance and help clients transform, grow and operate.

LLMs in comparison with traditional ML models

This framework, called Pure, ensures that our use of data remains purposeful, unsurprising, respectful and explainable for customers. In addition, we are building a technology infrastructure to enable the adoption of large language models in a secure manner. Financial services have made considerable progress adopting gen AI in the last two years.

• While the efficiency of existing models is rising and the cost of deploying LLMs is dropping, the market continues to see newer, larger and more capable models being deployed.
• “These results are consistent with the idea that generative AI tools may function by exposing lower-skill workers to the best practices of higher-skill workers,” the report states.
• For example, Erste Bank in Austria launched Financial Health Prototype, a customer-facing tool that lets banking customers ask questions about their financial life, such as how can they manage financial debt or plan for a vacation.
• This framework, called Pure, ensures that our use of data remains purposeful, unsurprising, respectful and explainable for customers.

In the future, banks will advertise their use of AI and how they can deploy advancements faster than competitors. AI will help banks transition to new operating models, embrace digitization and smart automation, and achieve continued profitability in a new era of commercial and retail banking. The interesting dichotomy with AI ChatGPT App is that it can automate hacking, sidestepping traditional security – yet it can also bolster security through anomaly detection, threat prediction, and real-time monitoring. Institutions must continuously adapt to stay ahead of risks, which can shape the industry’s future by addressing data privacy, integrity, and fairness.

These algorithms leverage advanced data processing techniques to handle large volumes of market data, such as economic indicators, financial reports, and news articles. As treasury has entered the era of “everything in real time,” the fragmentation and multitude of IT systems complicates treasurers’ lives. Therefore, treasury first needs to focus on the next level of process automation to improve efficiency, get a better grip on the data and strengthen internal controls. Mastercard has launched Decision Intelligence Pro (DI Pro), a Gen AI consumer protection tool that determines transaction risk by assessing entity relationships. The platform will reportedly now harness an unparalleled volume of 1 trillion data points to accurately predict the likelihood of transaction authenticity or falsity in real time.

The scalability of AI solutions and their integration with existing legacy systems are vital considerations for banks aiming to future-proof their services. This includes developing talent, managing AI capabilities, and ensuring AI-driven decisions are transparent and justifiable. The banking sector’s commitment to the continuous learning and updating of AI models is crucial in adapting to new data and evolving market conditions. GenAI models such as GPT, ChatGPT with its transformer architecture, mark a quantum leap from the AI of yesteryear, which primarily focused on understanding and processing information. Today, these models are the architects of text, images, code and more, initiating an era of unparalleled innovation in banking. The call to action emphasizes the need for financial institutions to adopt AI technologies proactively, leveraging their potential to enhance compliance and operational efficiency.

In conclusion, while AI presents a formidable opportunity for growth and innovation in the banking sector, a spectrum of challenges requires careful navigation. By prioritizing data privacy, engaging proactively with regulators, mitigating risks related to bias and accuracy, and addressing cultural and strategic hurdles, banks can leverage AI’s potential to the full. This comprehensive approach ensures that the adoption of AI in banking is not only technologically innovative but also ethically responsible and aligned with the long-term interests of customers and the broader financial ecosystem. After years at the forefront of artificial intelligence (AI)-based research and projects, BBVA has taken another big step forward in the use of generative AI in its main markets. The aim is to explore, in a safe and responsible way, 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. Generative AI has the potential to transform AML and BSA programs by automating complex tasks, improving detection capabilities, and enhancing regulatory compliance.

And while there is still a lot to learn, there are three key themes that continue to resonate. As much processing power, computing and energy as it takes to create a model, it takes multiples of that to maintain it. Spin up thousands of different models across the enterprise and the costs rapidly multiply (as do carbon emissions).

In wealth management, AI is unlocking personalized advice and risk assessment opportunities. The three largest U.S. players, JPMorgan, Capital One and Wells Fargo, employ 17.5% of banking’s AI talent pool, the analysis found. The AI workforce, which Mousavizadeh said encompasses roughly 240 roles and titles, grew 17% year over year across all 50 banks in the study. All four of the leading banks, JPMorgan, Capital One, Royal Bank of Canada and Wells Fargo, have dedicated AI research teams, according to Mousavizadeh.

Large language models have given way to the emergence of focused and specific narrow transformers, making energy and costs sustainable. The right talent is the bedrock for building resilient, compliant, and secure AI systems. Yet, the Infosys Bank Tech Index found that AI and cybersecurity talent are the most difficult skills for enterprises to recruit. Generative AI assistants are an ideal entry point for organizations in the financial and banking sectors looking to gain a foothold in this exciting new world. With help from the IBM Partner Ecosystem, these institutions can effortlessly build assistants that wow customers while boosting the bottom line.

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He added that some of the governance and security measures that would be required by a highly regulated bank are also part and parcel of the ecosystem. In the eerie glow of the digital age, financial institutions face a new breed of bad actors and how they are using technology against us to perform their crimes. 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.

For example, in the mortgage or credit underwriting process, regulators require an audit trail and information must be logged on why the decision was made, what parameters were considered, and were decisions made without any bias. With the right systems in place, AI can make better, faster, and less erroneous decisions than a human. AI can eliminate inherent human bias, making decisions in an ethical and responsible manner. 3 min read – Businesses with truly data-driven organizational mindsets must integrate data intelligence solutions that go beyond conventional analytics.

Generative AI can meanwhile help banks to stay compliant by continuously monitoring changes in regulations and swiftly adapting internal processes to ensure that they comply with new regulatory requirements. In this age of digital disruption, banks must move fast to keep up with evolving industry demands. Generative AI is quickly emerging as a strategic tool to carve out a competitive niche. With unique insight into a bank’s most resource-heavy functions, risk and compliance professionals have a valuable role in identifying the best areas for GenAI automation. How over-hyped is the promise of artificial intelligence – and particularly generative AI – in financial services?

There are a huge amount of lessons learned, which we share across the HR organization, but also share across our enterprise. We are making sure we don’t get bad actors, in terms of accessing the data and asking questions if we don’t necessarily want to be questioned. And as we head early into next year we are going to scale, probably in two key ways. The Group has announced plans in recent years to invest £3 billion in technology, with the aim of providing a “modern digital workplace” for all Lloyds employees. Recent announcements include the use of Microsoft Azure, Microsoft Managed Desktop and Microsoft Teams, as well as a partnership with Google Cloud to modernize its customer experience.

“A lot of checks and balances, a lot of validating, a lot of evidence-based artifacts need to be provided and committees needing to review and approve,” they said of the AI approval process, adding that “it will just beat you up.” His employer, one of the world’s largest brokerages, has developed an internal AI product that analyzes client data and generates reports. One of his interns showed him how to use ChatGPT; other team members have used it to summarize hundred-plus-page private-equity offerings.

Generative AI, particularly LLMs, enables the development of sophisticated chatbots and virtual assistants that deliver personalized and efficient customer service. 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. The versatility of LLMs enables their application in diverse areas such as automated report generation, customer service chatbots, and compliance document analysis. Their ability to process natural language and generate contextually relevant outputs makes them ideal for successfully performing tasks that require subjectivity and producing human-like text.

• More darkly, the MIT/Stanford study also found that training models on the work of experienced agents and feeding the outcomes to novices takes advantage of the skilled workers.
• That’s understandable given that large language models (LLMs) can be subject to hallucination and bias.
• And that I think is going to be a slightly bigger, longer term challenge that we are going to have to acknowledge and think about.
• Their investment strategies encompass a wide range of applications, including enhancement of fraud detection mechanisms and customer service chatbots.
• Now, they see genAI emerging and are asking themselves (and the rest of the business) how this new and disruptive technology might change their world for the better.

To be sure, generative AI will pose new challenges around managing requests, compute capacity, and token charges (which are based on the number of words). Banks will also have to build a service layer acting as an interface between large language models (LLMs) and generative AI-based applications. These will involve a significant investment, so it is vital to allocate funds strategically, which will require meticulous decision-making around whether to build, partner, or buy individual technology solutions. The global market value for generative AI technologies in banking and financial services institutions could increase from $20 billion in 2022 to more than $100 billion by 2032, according to Global Market Insights. As use of the technology has spread, many banks have realized they need a more comprehensive posture than the current siloed proofs of concept.

Additionally, GenAI is proving invaluable in the field of tax compliance within banking by automating the preparation of tax returns and enhancing fraud detection. You can foun additiona information about ai customer service and artificial intelligence and NLP. Similarly, in legal departments, AI-driven document review and analysis are streamlining workflows, while AI tools assist in contract reviews and negotiations, reducing risk and improving efficiency. This integration of AI fosters a collaborative ecosystem that elevates the precision and effectiveness of financial and legal services, positioning the sector at the forefront of technological innovation.