Self-service that delights customers: How the IBM Partner Ecosystem is harnessing generative AI assistants in the banking and financial sectors

A data scientist at a midsize hedge fund told BI that generative AI models are a “superpower for coders.” One of his biggest use cases is solving coding problems in different coding languages. “It enables executives to get information in a comprehensive way faster, which allows you to make your decisions faster and quickly move toward execution,” Donahue said. AlixPartners, which advises private-equity firms, uses a proprietary, AI-powered diagnostics tool that draws on decades of the firm’s consulting work to help buyout shops evaluate potential acquisition targets.

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. Fintechs remain at the forefront of harnessing gen AI and many of their use cases and solutions are impacting financial services. For example, Synthesia utilizes an AI platform to create high-quality video and voiceover content tailored for financial services, while Deriskly provides AI software aimed at optimizing compliance in financial promotions and communications. Banks, nonbank financial services players, and FinTech or Big Tech are at different stages of the journey to harnessing Gen AI capabilities. Established financial institutions are experimenting with Gen AI use cases, initially in marketing and sales, customer support, risk and compliance.

Overlay data and analytics capabilities

What they did do, however, was allow people to focus on the more value-adding parts of their jobs. The first is the implementation costs — building out new apps, training them, integrating them into existing systems, testing them, putting them into ChatGPT production and so on. That all takes massive amounts of computing power, loads of data and access to highly skilled people. Centers of excellence may help balance that cost in the initial phases but will likely slow adoption in the long run.

• SaaS and cloud banking provider Temenos launches Responsible Generative AI for banking, promising enhanced data management, productivity, and profitability with secure, explainable AI solutions.
• As we navigate the transformative era of AI in financial services, it is evident that AI is not merely a technological upgrade but a catalyst for profound disruption across products, processes and operations in the sector.
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• Strong use cases will include “high-touch” activities historically owned by people, which leverage large datasets or require a generative response logic.
• 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).

But, at the same time, they worry that the enterprise adoption of a new technology might create new attack vectors. While centralization streamlines important tasks, it also provides flexibility by enabling some strategic decisions to be made at different levels. This approach balances central control with the adaptability needed for the bank’s needs and culture and helps keep it competitive in fintech.

Current industry applications of LLMs: Overview of LLM use cases in financial services

While the human brain is excellent at reacting to immediate information and making decisions, GenAI can take a bird’s-eye view of an entire information landscape to surface insights hidden to the naked eye. This capability is useful for pairing customer caches with historical trend data to inform risk assessments or flag anomalous transactions indicative of potential fraud. BBVA has ChatGPT App already begun deploying 3,000 ChatGPT Enterprise licenses among Group employees in a bid to increase productivity and process efficiency, while stimulating innovation across the Group. The enterprise version of ChatGPT delivers the utmost security and privacy, combined with its unique ability to generate content or answer complex business questions, among numerous other features.

Among the use cases for gen AI at Bank of America outlined by Bajwa is improving developer efficiency and productivity within the bank’s large engineering organization of more than 10,000 developers. He also noted that it can help knowledge workers more efficiently ingest and process information by enabling knowledge discovery and summarization. Future potential use cases in customer-facing recommendations and automating customer service, generative ai use cases in banking though the bank is still in the early exploration phase for those types of applications. Burris at Bank of America leads a team of more than 30 data scientists, data engineers and reporting analysts who develop AI models that can decide, in real time, which transactions are highly suspicious and therefore should be blocked. Burris estimates this protects the bank and its customers from about $100 million worth of fraud every year.

To address these challenges, banks are also investing in robust AI governance frameworks, continuous monitoring and auditing, stakeholder engagement, and adherence to ethical guidelines and regulatory standards, she said. Additionally, board oversight can be complicated by a lack of clear regulatory direction, according to EY data. Regulators have expressed concern about embedded bias in algorithms used to make credit decisions and chatbots sharing inaccurate information, the firm said. On the flip side, GenAI’s ability to generate highly plausible, human-like communications is also making it easier and cheaper for criminals to defraud banks. GenAI could enable fraud losses to reach $40 billion in the U.S. by 2027, up from $12.3 billion in 2023, according to Deloitte’s Center for Financial Services’ “FSI Predictions 2024” report.

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More from Artificial intelligence

The financial services industry has had several similar moments in the past decade, yet not all of them have resulted in true transformation. For example, blockchain in 2017 led many to believe it would completely revolutionize banking, but nearly a decade later, it remains a theoretical interest. “Banks are naturally cautious in embracing generative AI to the full and want to ensure they do so responsibly. A proven track record in delivering responsible AI will be vital for financial institutions to confidently experiment and deploy generative AI models for critical business functions across the enterprise. In Financial Crime Mitigation, the AI assists business users in extracting intelligence from data, enabling quick cataloging of information such as financial crime alerts. This can identify themes or root causes that would take humans much longer to analyse manually.

Deliver consistent and intelligent customer care with a conversational AI-powered banking chatbot. Sustainable energy consumption, costs and regulations are also challenges when it comes to generative AI. 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. Leon now handles more than 97% of customer conversations without requiring redirection to human agents. As a result, Generali Poland is saving approximately 120 person-hours monthly and has shortened customer consultants’ working time by one hour per day.

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. A shift to a bot-powered world also raises questions around data security, regulation, compliance, ethics and competition. Since AI models are known to hallucinate and create information that does not exist, organizations run the risk of AI chatbots going fully autonomous and negatively affecting the business financially or its reputation. As the first European bank to form an alliance with OpenAI, BBVA is leading the way in integrating disruptive technologies within the financial sector. This collaboration will enable OpenAI to share its expertise and help BBVA unlock the full potential of generative AI, setting a benchmark for other financial institutions. Identifying a use case necessitates substantial effort in prioritization, cost-benefit analysis, and strategic considerations regarding technology and data architecture.

• The first billion interactions took four years, but the second billion were achieved in just 18 months, indicating a significant increase in client engagement.
• Ensuring that their AI systems do not violate privacy, prevent bias from creeping in, and remain secure keeps enterprise CXOs awake at night.
• Currently, there is a growing need among Indian banks to utilize Gen AI-powered virtual agents to handle customer inquiries.
• 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.

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. BBVA’s dedication to data and technology is evident through its decade-long establishment of AI Factories in Spain, Mexico, and Türkiye. As we navigate the transformative era of AI in financial services, it is evident that AI is not merely a technological upgrade but a catalyst for profound disruption across products, processes and operations in the sector.

Understanding the cost

By extracting valuable insights, detecting patterns, and recognizing correlations, AI algorithms can help identify potential risks and market disruptions that may impact financial institutions’ operations and investments. This enables institutions to make informed decisions, take proactive measures, and manage their risks effectively in response to changing market conditions. Today, banks of all sizes have access to a considerable amount of customer data that’s processed and stored on a daily basis, from credit history to buying activity. The integration of AI into the cybersecurity framework of the banking sector encapsulates the technology’s dual nature as both a potential risk factor and a critical defensive tool.

Gen AI is now catalyzing a significant shift, with 78% of surveyed financial institutions implementing or planning Gen AI integration. You can foun additiona information about ai customer service and artificial intelligence and NLP. Around 61% anticipate a profound impact on the value chain, enhancing efficiency and responsiveness. Globally, institutions foresee a 5 to 10 year timeline for full automation harnessing, strategically investing in areas with immediate benefits, such as customer service and cost reduction.

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. Banks are increasingly adopting generative AI to elevate customer service, streamline workflows and improve operational efficiency. 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. New AI-enabled capabilities across the business can create new opportunities to monetize data, expand product and service offerings, and strengthen client engagement. We have also set up a responsible AI taskforce comprising senior leaders from multiple disciplines to assess and address these risks prior to any use case being deployed in production. Our existing responsible data use framework for AI continues to provide us with guardrails as we look at new use cases.

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. AI engines can help with detecting frauds in large datasets by looking for correlations and trends. This can help financial institutions prevent security detect fraudulent activity before it becomes a major problem.

Retail chatbot examples: Three successful recipes to unlock sales

By recording these interactions, businesses can identify patterns, refine their offerings, and streamline their customer support processes. This data-driven approach empowers companies to make informed decisions and ensure that their website remains responsive to evolving customer needs. By employing eCommerce bots, retailers can access a variety of valuable functionalities aimed to transform Customer Experience (CX). These bots can seamlessly guide customers through the intricate journey of purchasing, providing step-by-step assistance and clarifications on product details. The potential to offer tailored product recommendations based on individual preferences empowers retailers to deliver a more personalized shopping encounter. By keeping customers informed about ongoing sales, promotions, and exclusive offers, eCommerce bots become indispensable allies in marketing efforts.

This will help you welcome new visitors, guide their buying journey, offer shopping assistance before, during, and after a purchase, and prevent cart abandonment. Imagine having to “immediately” respond to a hundred queries across your website and social media channels—it’s not possible to keep up. Chatbots will continue to leverage AI and NLP technologies to enhance their conversational abilities. Especially, integration in retail will become proficient in understanding intent, sentiment, and complex queries to offer natural and seamless user interactions. Improved AI capabilities will enable chatbots to provide more accurate and tailored responses, enhancing customer satisfaction.

How Do I Build a Chatbot?

It works as a human agent that offers personalized support via a live chat. Configuring your live chat with Lyro is straightforward, as the tool walks you through the process after you sign up. You can set up customer or sales oriented messages, based on your goals. The instant gratification of @-mentions, DMs and chatbots has influenced the trajectory of social messaging and customer care.

Built for the Google Assistant, eBay’s chatbot can be used with Google Home or on the phone. The bot will answer customer queries about products and drive the sales process. For example, it can answer users queries around the lowest price options or the best new products – across all eBay’s worldwide sites. Chatbots offer ways to instantly communicate with customers on multiple platforms or online eCommerce stores. They use AI to infer customer preferences and offer visitors personalised experiences. Chatbot.com is a platform for building conversational retail chatbots without coding and connecting them to live chat.

Offer easy order tracking and updates

Chatbots have also showm to improve customer satisfaction and increase sales by keeping visitors meaningfully engaged. Simply put, an ecommerce bot simplifies a customer’s buying journey with a brand by bringing conversations into the digital world. While most ecommerce businesses have automated order status alerts set up, a lot of consumers choose to take things into their own hands. A simple chatbot will ask you for the order number and provide you with an order status update or a tracking URL based on the option you choose. In this case, the chatbot does not draw up any context or inference from previous conversations or interactions.

• They can be used for a variety of tasks, from answering basic customer service questions to offering health advice.
• This is simple for bots to do and provides faster service for your customer compared to calling in and waiting on hold to speak to a person.
• Imagine a scenario where the interaction between bot and human feels like an actual conversation.
• A WhatsApp eCommerce Chatbot allows online retailers to connect with their customers where they actually are, i.e.
• Advanced retail AI chatbots equipped with natural language processing capabilities go a step further.

Sephora became one of the first brands to integrate chatbots when they began using them in 2017 via Kik. This is arguably one of the best chatbot marketing examples for highlighting how a bot can take something done via mobile and make it just as good (if not better) on social. Although digital ordering is nothing new, ordering through a chatbot requires no native downloads or sign-ups on an app. The bot allows customers to place orders and customize their pizzas all within the chat, making it a cinch to buy your favorite pie. Dom has the ability to save and repeat orders and find the closest store to you.

Microsoft CEO Nadella: ‘Bots Are the New Apps

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Customer Service Solutions for Digital Customer Experience

For example, while one company may be content with simple chat software, others may need a complex solution that supports multichannel communication with customer. A customer service software is software that streamlines customer communication across different channels. Each software is different in the way it handles incoming customer messages.

Beyond Slack, you can use Salesforce Service Cloud to provide support via email, live chat, and self-service channels. The platform also offers add-ons like field service and AI tools and can integrate easily with Saleforce’s CRM for added customer insights. Helpshift is a leader in in-app support, specifically focusing on providing in-app support for mobile applications. It allows customers to receive help when and where they need it most via both chat and self-service channels. Agents are able to manage incoming customer messages from a unified agent desktop that lets them see customer data and interaction history to aid in providing contextual support. All their plans include phone support essentials like IVR, the ability to set custom business hours, and call queuing.

Customer service scenarios with tips & examples

These scenarios can pertain to a range of customer concerns, such as product usage, billing information and more. Handling difficult customers is challenging for any customer service professional. The most important thing you can do is show them respect, patience, and care. Customers don’t always want to speak to someone, especially if their inquiry is straightforward. Our report revealed that 83 percent of customers will spend more with companies that allow them to find answers online without contacting anyone. Try as you may to prepare for everything, inevitably a customer will have a specific need or question that you haven’t heard before.

A registered agent is an individual or entity appointed to receive official mail and legal documents on behalf of a business, such as a corporation or LLC. Rocket Lawyer offers professional legal advice from qualified experts, providing valuable guidance and support. This is especially beneficial for those who require ongoing legal assistance.

Helping customers help themselves

Having a dedicated knowledge repository that outlines basic processes, policies, or how-to’s, can significantly decrease ticket volumes and take some pressure off your employees. Self-service resources can be easily implemented using a comprehensive knowledge base software. It’s important to note that not every business needs to have a presence on every platform. However, most companies, organizations, and educational institutions to have an active Facebook, Instagram, and Twitter page that they can turn to for customer support.

With automations and customizable workflows, HappyFox helps reduce tedious tasks, making agents’ day-to-day work easier. It can help you provide excellent customer service and, in turn, boost sales. A large, dispersed company means managing multiple agents and their unique skill sets.

Considering Desku, it has a rich editor that will help to format and insert the rich media for better understanding and curating the information for your customers. Customer support solutions can serve you across various channels such as phone, email, social media, chat, and more. Knowing how to serve the right customer service has a different impact on the business’s success. Zendesk’s adaptable Agent Workspace is the modern solution to handling classic customer service issues like high ticket volume and complex queries. If your marketing team manages your social media accounts, make sure they connect with the customer service team for help with any incoming support requests. Remember, everyone is responsible for good customer service, so agents need to have strong collaboration skills.

• This mindset also allows agents to offer proactive service by focusing on the customer and their needs, which can change the customer’s perception of events from bad to good.
• Buffer is a social media management platform that promises to improve social engagement for small businesses.
• Part of this includes customer service features that help support agents respond to customers who ask questions or provide feedback through social media channels.
• For those looking to switch their current registered agent to LegalZoom, the company takes care of the necessary paperwork and covers the $5 filing fee required by the state of New York.
• Jira Service Management empowers IT teams with a modern service desk that works right out of the box, including ITIL-certified processes.

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