Generative AI in Finance: Use Cases & Real Examples

AI enables tailoring products, advising, and outreach at an individual level based on predictive analytics on customer needs and behavior patterns. Robo-advisors like Betterment provide hyper-personalized investment advice at scale. AI models can process vast amounts of data from diverse sources to make faster, more accurate decisions around lending, insurance underwriting, trading strategies, etc.

We also allow you to split your payment across 2 separate credit card transactions or send a payment link email to another person on your behalf. If splitting your payment into 2 transactions, a minimum payment of $350 is required for the first transaction. Our platform features short, highly produced videos of Chat GPT HBS faculty and guest business experts, interactive graphs and exercises, cold calls to keep you engaged, and opportunities to contribute to a vibrant online community. The technology also standardizes diagnoses across practitioners by streamlining workflows and minimizing the time required for manual analysis.

Through the use of predictive analytics, we can anticipate and address potential risks before they arise. This is essential not only for our daily activities but also for our future planning, helping us remain strong in a constantly changing market landscape. It automates complex processes and adapts to new data without human intervention.

There are also specific features based on portfolio specifics — for example, organizations using the platform for loan management can expect lender reporting, lender approvals and configurable dashboards. Workiva offers a cloud platform designed to simplify workflows for managing and reporting on data across finance, risk and ESG teams. It’s equipped with generative AI to enhance productivity by aiding users in drafting documents, revising content and conducting research.

This suggests that global financial crises or unexpected financial turmoil will be likely to be anticipated and prevented. It is a large umbrella encompassing many technologies, some of which are already widespread in society and businesses and used daily. When we talk to digital assistants, use autocomplete, incorporate process automation tools, or use predictive analytics, we are using AI. These tools and other rules-based innovations are pervasive, but AI is entering a new era.

This holistic view allows traders and fund managers to understand market drivers better and craft strategies proactively. Additionally, AI-powered stress testing and scenario planning can simulate how financial institutions or investments might perform under various adverse economic conditions, such as recessions, market crashes, booms, or specific events. Due to their inherent big data nature, credit risk assessment and loan prediction are the most notable AI in fintech examples. AI is being increasingly used to automate the KYC process, with solutions such as intelligent document processing, digital customer onboarding, and biometric authentication. While AI covers a wide range of technologies, here are some of the key ones transforming financial services.

Data Management and Preparation

Financial institutions use this analysis to make informed decisions about market trends, customer sentiment, and investment opportunities based on the tone and context of communications. In trading, speech recognition facilitates real-time updates and swift order executions. It can accurately convert spoken words into actionable data, streamlining customer service and functional workflows within financial institutions.

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Gen AI could summarize a relevant area of Basel III to help a developer understand the context, identify the parts of the framework that require changes in code, and cross check the code with a Basel III coding repository. Gen AI can give developers context about the underlying regulatory or business change that will require them to change code by providing summarized answers with links to a specific location that contains the answer. It can assist in automating coding changes, with humans in the loop, helping to cross-check code against a code repository, and providing documentation. For example, gen AI can help bank analysts accelerate report generation by researching and summarizing thousands of economic data or other statistics from around the globe.

Like many other sectors, technology has long played an integral role in finance. The arrival of AI in Finance has sparked excitement around cost savings and augmented productivity. In fact, according to a Gartner study, 80% of CFOs surveyed in 2022 expected to spend more on AI in the coming two years. TQ Tezos leverages blockchain technology to create new tools on Tezos blockchain, working with global partners to launch organizations and software designed for public use.

Get stock recommendations, portfolio guidance, and more from The Motley Fool’s premium services. Banks use AI for customer service in a wide range of activities, including receiving queries through a chatbot or a voice recognition application. Watch this video to learn how you can extract and summarize valuable information from complex documents, such as 10-K forms, research papers, third-party news services, and financial reports — with the click of a button. With millennials and Gen Zers quickly becoming banks’ largest addressable consumer group in the US, FIs are being pushed to increase their IT and AI budgets to meet higher digital standards. These younger consumers prefer digital banking channels, with a massive 78% of millennials never going to a branch if they can help it.

The need to ramp up cybersecurity and fraud detection efforts is now a necessity for any bank or financial institution, and AI plays a key role in improving the security of online finance. That same year, City University of Hong Kong’s Guanhao Feng, Yale’s Stefano Giglio, and Booth’s Xiu created an ML method to evaluate factors and identify those most relevant for asset prices. Researchers have since used ML to predict prices and construct portfolios, among other tasks. By targeting specific industry challenges—such as improving diagnostic accuracy and operational efficiency—VideaHealth illustrates how AI can complement human expertise and automate routine tasks. This strategic use of AI enables businesses to unlock significant consumer value. Advanced algorithms can meticulously scan receipts, categorize expenditures and even flag anomalies with unparalleled accuracy and speed.

Data science and analytics

By streamlining and consolidating tasks and analyzing data and information far faster than humans, AI has had a profound impact, and experts predict that it will save the banking industry about $1 trillion by 2030. Like credit applications, AI can assess customers’ risk profile and identify the https://chat.openai.com/ optimal prices to quote with the right insurance plan. This would decrease the workflow in business operations and reduce costs while improving customer satisfaction. Advanced algorithms can quickly analyze massive datasets, automating regulatory checks and ensuring companies follow the rules.

The market value of AI in finance was estimated to be $9.45 billion in 2021 and is expected to grow 16.5 percent by 2030.

• AI can also lessen financial crime through advanced fraud detection and spot anomalous activity as company accountants, analysts, treasurers, and investors work toward long-term growth.
• For example, you may need analysts to work with different sectors or products.
• For example, a few years ago, the topic of high-frequency trading (HFT) became especially relevant.
• 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.

However, we have not yet achieved this objective; moreover, we are nowhere near reaching it. Although we seem to be on the verge of introducing real AGI, there are still more than five-seven years left to do so. Today, only the lazy do not discuss Artificial Intelligence (AI) and its potential to revolutionize practically every aspect of our lives, including finance. Indeed, there is a startling growth in the AI market—it surpassed $184 billion in 2024, $50 billion more than in 2023. Moreover, this blossoming is expected to continue, and the market will exceed $826 billion by 2030. Contact Master of Code Global today and let’s explore how our customized solutions can revolutionize your financial operations.

Additionally, the extracted data can be used for spend data analysis and reporting, providing valuable insights into the business’s finances and helping to improve both control over budgets and financial decision-making. The use of AI for data extraction removes the need for manual data entry, saving time, eliminating human errors, and making it easier for finance teams to track spending and manage their finances in real time. AI technology is incredibly versatile and can be used in various applications, including chatbots, predictive analytics, natural language processing, and image recognition, among others. The use of finance AI is on the rise, a study by Gartner estimating that by 2025, 75% of finance teams will be using AI-powered applications to automate tasks and improve decision-making processes.

OpenAI has not made a final decision regarding its structure, but is considering not having the cap, the Financial Times reported. Alorica agents can use AI tools to quickly access information about the customers who call in — to check their order history, say, or determine whether they had called earlier and hung up in frustration. Yet the widespread assumption that AI chatbots will inevitably replace service workers, the way physical robots took many factory and warehouse jobs, isn’t becoming reality in any widespread way — not yet, anyway. Floating point arithmetic might sound fancy “but it’s really just numbers that are being added or multiplied together,” making it one of the simplest ways to assess an AI model’s capability and risk, Aguirre said.

Socure is used by institutions like Capital One, Chime and Wells Fargo, according to its website. It counted bigrams (two-word combinations including the constitution or public opinion) used in conjunction with the words risk and uncertainty, or their synonyms, to identify potential risks to companies. The higher the count, the greater the political risk for the company, the research finds. Subsequent papers resulted in a startup, NL Analytics, that works with central banks and international organizations to use these methods for economic surveillance. One is machine learning, which involves training algorithms to learn patterns and make predictions from data.

AI is also effective in pre-analytics when large amounts of heterogeneous information must be processed. This is especially relevant for finance, as there have always been departments of analysts engaged in uncreative but essential work. Now, when AI is attempted to be implemented for analytics, efficiency increases in this area. On Wall Street, they even believe this profession will disappear—AI software can do the analysts’ work far more quickly and cheaply. Initially, the goal was to create artificial intelligence at the level of human consciousness—the so-called strong AI—Artificial General Intelligence (AGI).

According to a survey conducted by Irish-American professional services company Accenture, 75% of consumers are more likely to do business with a bank that offers personalized services. What’s more, according to another survey, 73% of consumers are willing to share their personal data with banks in exchange for customized offers. For example, CitiBank has inked a deal with data science market leader Feedzai, which helps to flag suspicious payments and safeguard trillions of dollars in daily operations. Feedzai conducts large-scale analyses to identify fraudulent or dubious activity and alert the customer.

In addition, AI chatbots can connect with core banking systems, authentication protocols, and knowledge bases to execute tasks, retrieve updated information, and provide relevant responses. They can also learn from past interactions to enhance their responses in the future. AI-powered chatbots allow customers to self-serve hassle-free for common queries and transactions, reducing wait times. For instance, NLP can rapidly analyze massive volumes of unstructured data sources like news reports, social media, analyst reports, company filings, etc., to extract signals about market movements, trends, and risks.

They definitely prefer digital channels when it comes to their bank, and the majority of them would rather never go to a physical branch. Hiren is CTO at Simform with an extensive experience in helping enterprises and startups streamline their business performance through data-driven innovation. The financial sector is heavily regulated, with stringent rules around data privacy, security, model transparency, ethical practices, and audit trails. Fines for non-compliance can be massive, and ensuring that AI solutions adhere to these rules is a significant hurdle.

AI in CCH Tagetik runs platform-wide, augmenting the speed and accuracy of CPM processes and expanding data availability across your enterprise. Using a glass box approach, our explainable AI gives finance teams the authority to check, vet, and accept the AI’s work. Specifically, AI in CCH Tagetik can be used for data collection, anomaly detection, predictive planning, analytics, and driver-based planning. Learn more about the AI in CCH Tagetik and how it can support your finance team. Artificial Intelligence (AI) in finance refers to the application of machine learning algorithms, data science techniques, and cognitive computing to financial services to enhance performance, boost efficiency, and provide deeper insights.

• The introduction of AI-driven automation into financial workflows results in a more agile and responsive environment.
• Innovations in machine learning and the cloud, coupled with the viral popularity of publicly released applications, have propelled Generative AI into the zeitgeist.
• Such an enhancement in data accessibility can significantly boost the productivity of the entire finance team.
• Accurate forecasts are crucial to the speed and protection of many businesses.
• Alorica agents can use AI tools to quickly access information about the customers who call in — to check their order history, say, or determine whether they had called earlier and hung up in frustration.

Looking at the table, we see that machine learning and artificial neural networks are the most popular ones (they are employed in 41 and 51 articles, respectively). Nevertheless, we notice that support vector machine and random forest are the most widespread machine learning methods. On the other hand, the use of artificial neural networks (ANNs) is highly fragmented. Backpropagation, Recurrent, and Feed-Forward NNs are considered basic neural nets and are commonly employed. Advanced NNs, such as Higher-Order Neural network (HONN) and Long Short-Term Memory Networks (LSTM), are more performing than their standard version but also much more complicated to apply. These methods are usually compared to autoregressive models and regressions, such as ARMA, ARIMA, and GARCH.

This empowers professionals to focus on strategic initiatives while upholding data integrity and security standards. Intelligent document processing automates and optimizes the handling of extensive paperwork and digital documents. Speech recognition enables users to interact hands-free with banking systems, enhancing security and convenience. Customers can authenticate transactions and access account details through voice commands. We’ll also uncover the top AI applications and tools the finance sector leverages.

AI in Finance: 10 Use Cases You Should Know About in 2024

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. Companies are leveraging AI models and algorithms to detect suspicious transactions and flag them for further investigation. When it comes to automation in accounting and bookkeeping, there are several AI-powered solutions available.

Advanced algorithms and machine learning streamline tasks like data entry, reconciliation, and customer service. It also supports decision-making processes by providing insights derived from complex data analysis. Generative AI empowers finance professionals ai in finance examples to improve operational efficiency and deliver enhanced customer experiences in an increasingly data-driven world. This method enables financial institutions to anticipate market movements, understand customer preferences, and optimize investment strategies.

For example, algorithms can be used to analyze the creditworthiness of loan applicants, taking into account factors such as credit score, income level, and so on. By identifying patterns and trends, AI systems can predict the likelihood of a borrower defaulting on their loan. This AI-based way of processing invoices is much more efficient and less prone to error than the traditional one, where human intervention is needed at almost ever step.

Simudyne’s secure simulation software uses agent-based modeling to provide a library of code for frequently used and specialized functions. For example, the company’s See & Spray technology—which distinguishes crops from weeds with remarkable accuracy—utilizes computer vision and machine learning to identify weeds in real time. This targeted approach can reduce non-residual herbicide use by more than two-thirds by target-spraying weeds, leading to significant cost savings for farmers. Imagine a business where decisions are powered by intelligent systems that predict trends, optimize operations, and automate tasks. This isn’t a distant vision—it’s the reality of artificial intelligence (AI) in business today.

This development is a big step in AI for market intelligence promising more efficiency and accuracy in research. Thus, the question isn’t “to be or not to be”; rather, it’s about when you will start utilizing Generative AI in finance. Current statistics indicate that institutions in this sector are leading in workforce exposure to potential automation. Challenges like legacy technology and talent shortages might temporarily hinder the adoption of AI-based tools. For example, Bloomberg announced its finance fine-tuned generative model BloombergGPT, which is capable of making sentiment analysis, news classification and some other financial tasks, successfully passing the benchmarks. AI improves finance’s decision-making and efficiency, but what exactly does that look like in practice?

Yet, despite the advancements in this field, and despite the wide availability of fintech tools for invoice process automation, many companies still handle invoices manually. By using such techniques, AI-based invoice processing tools are able to read and extract all the relevant information from invoices quickly. This reduces the need for manual data entry and eliminates human errors, making the invoice processing workflow more time- and cost-efficient.

Kensho, an S&P Global company, created machine learning training and data analytics software that can assess thousands of datasets and documents. Traders with access to Kensho’s AI-powered database in the days following Brexit used the information to quickly predict an extended drop in the British pound, Forbes reported. Underwrite.ai uses AI models to analyze thousands of financial attributes from credit bureau sources to assess credit risk for consumer and small business loan applicants.