Generative AI in Finance: Use Cases & Real Examples
A bank that fails to harness AI’s potential is already at a competitive disadvantage today. Many banks use AI applications in process engineering and Six Sigma settings to generate conclusive answers based on structured data. Though they cost billions to develop, many of these cloud-based AI solutions can be accessed cheaply. The ability for any competitor to use and string together these AI tools is the real development for banks here. This high containment rate is driven by interface.ai’s combination of graph-grounded and Generative AI technologies. Built on 8+ years of domain-specific collective intelligence across every channel, the Voice Assistant has exceptional understanding, allowing it to accurately interpret and respond to a wide range of industry queries.
While this is not the most widely recognized example of GenAI in banking, it goes to show the many Generative AI use cases in banking that have unintended, but impactful, consequences. Organizations are not wondering if it will have a transformative effect, but rather where, when, and how they can capitalize on it. Such innovations significantly improve client satisfaction through curated advice and proactive assistance.
This is due to how decision-making AI models are developed, namely by humans who bring their biases and assumptions to the training of the machine learning model. These biases can be magnified when the model is deployed, sometimes with troubling results. This definition of machine learning bias explains the different types of bias that can inadvertently affect algorithms and the steps companies need to take to eliminate them. It smoothens the process of trading and detection of fraud, improves retirement planning, and adds efficiency, accuracy, and cost savings to the financial operation and customer experience. Although there are obstacles to be solved in the field of data privacy and regulatory compliance, the future of AI in finance looks very bright, and AI development companies understand that well. In a scenario of unstoppable technological progress, AI will be one of the key drivers shaping future change in the financial landscape.
“Law is extremely complex and nuanced, and most creators of work productivity tools lack a true understanding of the legal documents lawyers ultimately have to produce, which inhibits the development of accurate [AI] models,” Zhou said. “Supio has hundreds of models running at a given time with different functions to try to understand and classify documents. We then measure this against the work products that are expected and improve these results gradually.” Just like GenAI, predictive AI models are trained on historical data and use machine learning to identify patterns and establish relationships within the data using statistical analysis. These technologies are not only transforming how financial institutions operate but are also setting new standards for efficiency and customer engagement. Let’s take a closer look at the details of how exactly AI will transform the landscape of finance, from everyday applications to what is coming in the future.
For proof, look no further than the 300-plus organizations who are featured at this week’s Next event in Las Vegas. Users can explore investment opportunities or evaluate competitors, receiving precise, instantly verified answers. This development is a big step in AI for market intelligence promising more efficiency and accuracy in research. Generative AI operates on an augmented intelligence approach, emphasizing collaboration between machines and humans.
Gen AI in finance provides tailored recommendations to individuals after personalized analysis of existing data, risk-taking capacity, and user behaviour. It helps users optimize investment portfolios, plan their finances strategically, and enhance customer satisfaction. AI plays a significant role in the banking sector, particularly in loan decision-making processes.
These AI solutions for finance companies mean faster data processing, better predictive models, and invaluable insights in a fraction of the time. Besides real-time market data, trends, and prices, it also provides users with personalised investment suggestions based on their portfolios. It’s just the perfect financial buddy who solves all financial worries with a click. AI is useful in corporate finance because it can more accurately forecast and evaluate loan risks. AI innovations like machine learning may enhance loan underwriting and lower financial risk for businesses wanting to grow their value.
But with AI, or artificial intelligence, long and complicated processes can be shortened in such a way. You can foun additiona information about ai customer service and artificial intelligence and NLP. Strong data governance and privacy policies must support this digital transformation to ensure companies can use AI technologies safely and responsibly. Retailers can create applications that give shoppers a next-generation experience, creating a significant competitive advantage in an era when customers expect to have a single natural-language interface help them select products. For example, generative AI can improve the process of choosing and ordering ingredients for a meal or preparing food—imagine a chatbot that could pull up the most popular tips from the comments attached to a recipe. There is also a big opportunity to enhance customer value management by delivering personalized marketing campaigns through a chatbot. Such applications can have human-like conversations about products in ways that can increase customer satisfaction, traffic, and brand loyalty.
Reducing manual effort and minimizing errors increases efficiency and accuracy in financial record-keeping. McKinsey’s research illuminates the broad potential of GenAI, identifying 63 applications across multiple business functions. Let’s explore how this technology addresses the finance sector’s unique needs within 10 top use cases.
Legal work is incredibly labor- and time-intensive, requiring piecing together cases from vast amounts of evidence. That’s driving some firms to pilot AI to streamline certain steps; according to a 2023 survey by the American Bar Association, 35% of law firms now use AI tools in their practice. In 2022, the industry lost $112 billion to retail shrink, with ORC being a significant factor, according to the 2023 National Retail Security Survey.
Financial services teams can take several steps to prepare for the integration of this technology into their operations. Financial professionals understand the challenge of keeping up-to-date on competitors during earnings season. The task is tedious and time-consuming, yet crucial to maintaining a lead in your industry. In a perfect world, your team could reduce the amount of hours spent on taking notes distilling key insights from large sets of qualitative data, and ultimately save time in tracking, analyzing, and reporting on public company competitors. Custom Gen AI model development is rigorously tested by AI service providers for different AI use cases, ensuring they perform to the notch in the real world.
IBM Consulting’s F&A practitioners can partner with you as you roll out this technology, sharing valuable insights and best practices along the way. In 2023 alone, IBM Consulting has interacted with more than 100 clients and completed dozens of engagements infusing generative AI alongside classical machine learning AI strategies. Explore more posts in this blog series, The Future of Finance with Generative AI, to learn more about how to streamline and enhance critical F&A functions and improve your finance operation’s efficiency with generative AI.
Our expertise lies in creating advanced AI technologies and ensuring the innovations are deployed ethically and responsibly. We understand the complexities of the FinTech sector and are committed to delivering solutions that are not just technologically progressive but also socially conscious and regulation-compliant. Integrating AI into customer dialogues streamlines communication, minimizing wait times and reducing errors. Adopting the tool in support strategies marks a significant step in optimizing service delivery.
Whether it’s assessing credit risks, market risks, or operational risks, Generative AI provides a powerful tool for staying one step ahead in the complex world of finance. Traditional methods often involve manual reviews and batch processing, but Generative AI algorithms can continuously monitor transactions as they occur. This real-time scrutiny allows for the swift identification of suspicious activities, minimizing the impact of fraudulent transactions. Our research found that equipping developers with the tools they need to be their most productive also significantly improved their experience, which in turn could help companies retain their best talent. Developers using generative AI–based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow.
While we have estimated the potential direct impacts of generative AI on the R&D function, we did not attempt to estimate the technology’s potential to create entirely novel product categories. These are the types of innovations that can produce step changes not only in the performance of individual companies but in economic growth overall. For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for https://chat.openai.com/ what is known as generative design. Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials. Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics. But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others.
Our team has extensive experience in developing, designing, and deploying custom-gen AI solutions that meet the finance business-specific needs of finance projects. Chatbots play a vital role in every industry for serving customers instantly with contextual answers. The finance industry is no exception, where chatbots virtually assist customers individually by providing personalized answers to common questions. The capability to collect data and drive insights enables the chatbot to provide answers tailored to user interests, sentiments, and preferences.
When the model becomes skilled at identifying these patterns, it’s able to create similar patterns based on its intensive training. While both use machine learning, there’s a lot more to these AI models than it seems. Stick around to learn the key differences and how they’re reshaping industries worldwide.
Morgan Stanley has been a trailblazer in adopting Generative AI within its wealth management services. In March 2023, the firm partnered with OpenAI to launch the “AI @ Morgan Stanley Assistant”, a Generative AI-powered chatbot that grants financial advisors quick access to the firm’s extensive intellectual resources. The tool has seen a remarkable 98% adoption rate among advisors, underscoring its value in enhancing decision-making and client services. Consequently, not only can financial institutions explore new design concepts for groundbreaking innovations, but they can also optimize existing products based on specific criteria. Indeed, 72% of customers believe products are more valuable when tailored to their needs.
Generative AI in NLP is not only about crunching numbers; it’s also about improving communication and documentation processes. These algorithms can assist in drafting reports, summarizing financial documents, and even generating human-like text for communication purposes. Generative AI is a game-changer in the world of fraud detection and prevention, especially when it comes to real-time monitoring. This results in quicker responses to market changes, optimizing trading strategies, and ultimately enhancing overall portfolio performance. For most of the technical capabilities shown in this chart, gen AI will perform at a median level of human performance by the end of this decade.
So understanding the use cases that will deliver the most value to your industry is key
This will enable banks and financial institutions to conclude credit applications faster and with fewer errors. Generative artificial intelligence in finance can analyze vast amounts of regulatory data and provide insights to organizations on how to adapt to regulatory code changes efficiently. Interpreting complex regulatory requirements helps businesses stay compliant and mitigate regulatory risks effectively.
The time to act is now.11The research, analysis, and writing in this report was entirely done by humans. Previous generations of automation technology often had the most impact on occupations with wages falling in the middle of the income distribution. For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor. Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles.
It aims to revamp how transactions are monitored, promising a significant leap in fraud detection. TallierLTM has proven to be remarkably effective, showing up to 71% improvement in identifying fraudulent activities over existing models. Gen AI’s precise impact will depend on a variety of factors, such as the mix and importance of different business functions, as well as the scale of an industry’s revenue. Nearly all industries will see the most significant gains from deployment of the technology in their marketing and sales functions. But high tech and banking will see even more impact via gen AI’s potential to accelerate software development. We tapped into the minds of our very own F&A experts at IBM Consulting — the ones that know that how you help businesses make data-driven decisions indicates your ability to support future business.
The combination of Generative AI with blockchain technology is expected to strengthen security, transparency, and efficiency in financial transactions while also cutting costs and optimizing processes. The solution has dramatically reduced the time required for developers to create AI applications from months to weeks. Notably, Microsoft’s GitHub Copilot, a key AI tool used on the platform, has enhanced developer productivity by 20%.
- Financial firms and institutions stand in a unique position to take an early lead in the adoption of generative AI technology.
- AI will increase the interaction with the customer through personalized services and on-time support.
- As the technology advances, banks might find it beneficial to adopt a more federated approach for specific functions, allowing individual domains to identify and prioritize activities according to their needs.
- Gen AI tools can already create most types of written, image, video, audio, and coded content.
ORC drove a 15% increase in losses in 2022, compared to the year before, the report adds. Your donation to our nonprofit newsroom helps ensure everyone in Allegheny County can stay up-to-date about decisions and events that affect them. However, only about .1% of the people who read our stories contribute to our work financially. Our newsroom depends on the generosity of readers like yourself to make our high-quality local journalism possible, and the costs of the resources it takes to produce it have been rising, so each member means a lot to us.
A Reliable, Accurate GenAI Tool for Every Professional
Leading banks are using a combination of human talent and automation, intervening at multiple points in the data life cycle to ensure quality of all data. Data leaders also must consider the implications of security risks with the new technology—and be prepared to move quickly in response to regulations. As one of the leading generative AI service provider, we help businesses implement the proper gen AI use cases, allowing them to excel in finance.
Moreover, customers no longer need to run to the banks for common services such as checking bank balances, managing credit limits and cards, transferring funds, etc. With a conversational AI, the customer must enter his needs through voice or text commands. By 2035, AI solutions will be responsible for a whopping $1 trillion in cost savings in the financial domain. Implementing AI in the finance industry promises smart servicing, which improves customer experience besides driving efficiency. Based on these assessments of the technical automation potential of each detailed work activity at each point in time, we modeled potential scenarios for the adoption of work automation around the world. First, we estimated a range of time to implement a solution that could automate each specific detailed work activity, once all the capability requirements were met by the state of technology development.
This involves educating teams on the technology’s capabilities and ethical considerations. Moreover, companies should foster an environment that values continuous learning and conscientious AI application. Navigating the intricacies of conformity and confidentiality in artificial intelligence is also crucial. As regulatory frameworks evolve, AI-powered systems must adapt to adhere to stringent data protection laws.
Pharmaceuticals and medical products could see benefits across the entire value chain
While it is crucial to talk about the major benefits of AI in finance, we must not overlook the possible challenges and risks it can pose. Now, with the availability of Artificial Intelligence-driven tools, there are customized retirement calculators and planning strategies through which individuals can easily plan their future. The use of AI in finance can also be seen in clearing the fog in the unclear world of credit scoring. It enhances traditional credit scoring methods by incorporating a wider array of data points. To unlock the real power of generative AI, your organization must successfully navigate your regulatory, technical and strategic data management challenges.
The same report also predicts that by 2028, a 30% surge in productivity can be expected from banking employees. Deutsche Bank’s collaboration with Google Cloud’s generative AI exemplifies this shift, aiming to provide analysts with deeper insights and faster task execution, ultimately boosting employee productivity. With the extracted data, credit evaluation can be handled much accurately, and banks can provide faster services for lending operations. 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. The bank uses AI for fraud detection, implementing algorithms to identify fraudulent patterns in credit card transactions.
Compliance testing and regulatory reporting are fundamental yet laborious financial tasks. Through synthetic data generation and regular analysis automation, Generative AI facilitates how financial institutions handle compliance, ensuring they meet a wide range of regulatory requirements. They also simplify the financial reporting process by integrating data from multiple sources and organizing it into structured formats. This capability enables businesses to produce accurate and timely reports for stakeholders, regulatory bodies, and investors, streamlining financial operations and enhancing efficiency.
Generative AI in finance refers to implementing gen AI in finance processes and operations that enable investment strategy creation automation, personalized financial advice generation, customer sentiment analysis, risk management, and more. Since customer information is proprietary data for finance teams, it introduces some problems in terms of its use and regulation. Generative AI can be employed by financial institutions to produce synthetic data that adheres to privacy regulations such as GDPR and CCPA. By learning patterns and relationships from real financial data, generative AI models are able to create synthetic datasets that closely resemble the original data while preserving data privacy. These models can simulate different market conditions, economic environments, and events to better understand the potential impacts on portfolio performance. This allows financial professionals to develop and fine-tune their investment strategies, optimize risk-adjusted returns, and make more informed decisions about managing their portfolios.
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This technology opens up a wide array of applications, from enhancing fraud detection and risk management to advanced virtual assistants and beyond. Generative AI’s adoption rate is rapidly increasing within the financial services industry. MarketResearch.biz highlighted in its report that the Generative AI market in finance was valued at $1,085.3 million in 2023 and is projected to soar to $12,138.2 million by 2033, reflecting a compound annual growth rate (CAGR) of 28.1%. This helps financial institutions and banks identify potential defaulters based on their past records, thereby preventing potential fraud. However, unlike generative AI, these models don’t use these patterns and relationships to generate new content. The Autonomous Finance platform represents a cutting-edge financial system that continuously assimilates and learns from the dynamic transactional data within organizations’ finance and accounting departments.
What Supio does, Zhou explained, is generate demand letters — letters outlining the legal disputes to be resolved — as well as supporting documentation, while letting users search the evidence through a chatbot-type interface. Unlike generic, publicly available generative AI tools, Appriss Retail’s Incident + ORC Intelligence is purpose-built for the unique challenges of retail loss prevention and investigations. By providing tailored AI-driven insights, the solution empowers retailers to protect their profits more effectively than ever before. Government use of generative AI comes with risks, including the possibility of convincing fake images, that could erode public trust.
Below are 5 major challenges financial institutions face and solutions to overcome them. Generative AI systems do a good job of analyzing customer sentiment in-depth and precisely to effectively gauge public opinion on financial products, services, or trends in financial markets. To achieve that, they examine social media, news articles, and other online content.
What Generative AI Means For Banking
Personalized solutions, tailored to individual buyer needs, will turn into the norm, navigated by deep learning capabilities. Moreover, artificial intelligence’s predictive capabilities forecast future buyer behaviors and economic indicators. Through advanced algorithms, it anticipates shifts in sentiment, enabling proactive business decisions. The foresight ensures that FinTech providers stay ahead, adapting swiftly to evolving conditions.
Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI. To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making. Chat GPT For the purposes of this report, we define generative AI as applications typically built using foundation models. These models contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain. Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks.
Customer service and support is one of the most promising Generative AI use cases in banking, particularly through voice assistants and chatbots. GenAI voice assistants can now automate a high portion of incoming queries and tasks with exceptional intelligence, accuracy and fluidity. This evolution has not only improved the quality of customer interactions, but also expanded the range of services that can be automated. We were also joined by more than 100 partners supporting the creation of AI agents and AI solutions, which you can read about in detail. Business leaders are excited about generative AI (gen AI) and its potential to increase the efficiency and effectiveness of corporate functions such as finance.
For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity. We estimate that generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending. We estimate that applying generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of generative ai finance use cases current function costs. Notably, the potential value of using generative AI for several functions that were prominent in our previous sizing of AI use cases, including manufacturing and supply chain functions, is now much lower.5Pitchbook. This is largely explained by the nature of generative AI use cases, which exclude most of the numerical and optimization applications that were the main value drivers for previous applications of AI.
To stay true to this mission, GLCU recognized that its phone banking offering needed to improve. While they offered 24/7 assistance with an IVR system, it lacked functionality and contextual-understanding that restricted the volume of calls it could handle, and the quality in which it managed them. Overall, implementing Generative AI in financial services presents unique challenges, but the rewards are worth the effort. To ensure success, prioritize information quality, explainable models, strong data governance, and robust risk control.
Generative AI capabilities in generating synthetic data and enhancing model accuracy allow it to provide a more precise credit risk evaluation. Finance leaders will have better-informed loan decisions, ultimately enhancing risk assessment and credit scoring. Generative AI in finance marks a significant leap forward, reshaping conventional practices through advanced algorithms.
Parallelly, in the insurance domain, a leading global company faced challenges stemming from manual claim processes, resulting in financial losses and inefficiencies. The absence of a fraud detection system exposed them to fraudulent claims, and rigid, human-dependent processes hindered efficient data analysis. An Accenture report suggests that such AI models can impact up to 90% of all working hours in the banking industry by introducing automation and minimizing repetitive tasks among employees.
The DataRobot firm offers AI platforms that help banks automate machine learning life cycle aspects. It allows financial institutions to gather insights with predictive analytics and helps them make better decisions, find investment opportunities, and quickly adapt to market changes. With over 20 years of proven experience in data management and AI/ML, Kanerika offers robust, end-to-end solutions that are ethically sound and compliant with emerging regulations. Our team of 100+ skilled professionals is well-versed in cloud, BI, AI/ML, and generative AI, and has integrated AI-driven solutions across the financial spectrum, ensuring institutions harness AI’s full potential. Kanerika implemented AI/ML algorithms, achieving 93% accuracy in auto-extracting information.
Possible indications for a given drug are based on a patient group’s clinical history and medical records, and they are then prioritized based on their similarities to established and evidence-backed indications. AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness.
Machine learning helps Gen AI models establish patterns and relationships in a given dataset through neural networks. Generative AI tools can draw on existing documents and data sets to substantially streamline content generation. These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing. In addition, generative AI could automatically produce model documentation, identify missing documentation, and scan relevant regulatory updates to create alerts for relevant shifts. Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities.
Combining generative AI with all other technologies, work automation could add 0.5 to 3.4 percentage points annually to productivity growth. However, workers will need support in learning new skills, and some will change occupations. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world. Featurespace recently launched TallierLT, a groundbreaking innovation in the financial services industry. The tool represents the first Large Transaction Model (LTM) powered by Generative AI for payments.
From automating data analysis and forecasting to generating personalized investment recommendations, this iteration of AI is revolutionizing the way financial professionals work. With genAI, firms can not only save time but also improve the accuracy and reliability of their insights, ultimately leading to better outcomes for their clients. The pioneering approach optimizes intricate financial strategies and decision-making processes, enhancing efficiency, accuracy, and adaptability in the dynamic world of finance. As the “tip of the spear” in generative AI, finance can build the strategy that fully considers all the opportunities, risks, and tradeoffs from adopting generative AI for finance. For the successful development and deployment of Gen AI applications, artificial intelligence consulting companies will help you identify which Gen AI use case is great for achieving AI objectives.
Additionally, even if economic incentives for deployment exist, it takes time for adoption to spread across the global economy. Hence, our adoption scenarios, which consider these factors together with the technical automation potential, provide a sense of the pace and scale at which workers’ activities could shift over time. This big potential reflects the resource-intensive process of discovering new drug compounds. Pharma companies typically spend approximately 20 percent of revenues on R&D,1Research and development in the pharmaceutical industry, Congressional Budget Office, April 2021. With this level of spending and timeline, improving the speed and quality of R&D can generate substantial value.
If your focus is just banking, a subset of these use cases are listed in generative AI use cases in banking. However, enterprise generative AI, particularly in the financial planning sector, has unique challenges and finance leaders are not aware of most generative AI applications in their industry which slows down adoption. This unawareness can specifically affect finance processes and the overall finance function.
Two scenarios are shown for early and late adoption of automation, and each bar is broken into the effect of automation with and without generative AI. The addition of generative AI increases CAGR by 0.5 to 0.7 percentage points, on average, for early adopters, and 0.1 to 0.3 percentage points for late adopters. In the overall average for global growth, generative AI adds about 0.6 percentage points by 2040 for early adopters, while late adopters can expect an increase of 0.1 percentage points.
