AI in Finance: Benefits, Real-World Use Cases, and Examples

The platform acquires portfolio data and applies machine learning to find patterns and determine the outcome of applications. Scienaptic AI provides several financial-based services, including a credit underwriting platform that gives banks and credit institutions more transparency while cutting losses. Its underwriting platform uses non-tradeline data, adaptive AI models and records that are refreshed every three months to create predictive intelligence for credit decisions. Enova uses AI and machine learning in its lending platform to provide advanced financial analytics and credit assessment.

Insurance is a close cousin of finance as both industries rely on financial modeling and need to accurately estimate risk in order to be successful. Generally, artificial intelligence is the ability of computers and machines to perform tasks that normally require human intelligence, such as identifying a type of plant with just a picture of it. With ChatGPT setting off a new revolution in AI, we could just be seeing the start of AI in the financial industry as these companies find new ways to use this breakthrough technology. Gen AI can act as an assistant or a coach to employees by helping them do their job more efficiently and ultimately enabling them to focus on strategic, high-impact activities. For example, coding assistance and generation, such as Codey, which is a family of code models built on PaLM 2, can dramatically increase programming speed, quality, and comprehension.

It allows applying for a fast personal loan, auto refinancing, or debt consolidation– all online. Equipped with these powerful technologies, AI is being applied in numerous innovative ways within the financial sector. Expense fraud is a pervasive problem that continues to plague companies of all sizes and industries. In fact, a recent survey by the Association of Certified Fraud Examiners found that organizations lose an estimated 5% of their revenue to fraud each year, with expense reimbursement fraud being one of the most common types of fraud.

In this article, I’ll discuss 5 ways AI is revolutionizing fintech, with real-life examples to illustrate its impact. The financial technology (Fintech) world is experiencing a drastic change, and AI is leading the charge. ESG Scoring is meant to complete the traditional rating, providing more transversal and global information, thus improving the investment choices. People’s most important life choices often depend on credit history, since having good credit means receiving better financing options, or even renting the house you want to live in. Therefore, a quicker and more effective approval process for loans and cards is a necessity. As a matter of fact, AI enables 24/7 customer interactions, relieving the personnel from repetitive work, reducing false positives and human error.

Challenges and Risks of AI in the Financial Sector

Completes repetitive tasks 

Repetitive tasks like data collection, anomaly detection, and transaction matching are relatively menial, but they consume the valuable time and brain space of finance teams. It can organize data from multiple sources, dimensions, and types for analysis, identify outliers in large datasets, and reconcile information on behalf of finance teams. Machines are far better at identifying errors in spreadsheets with thousands of cells than the hardworking teams that have been staring at those numbers all day. That’s why the market size of Generative AI in finance is projected to reach $4,030 million by 2033.

“A detailed account of the literature on AI in Finance”, the literature on Artificial Intelligence in Finance is vast and rapidly growing as technological progress advances. There are, however, some aspects of this subject that are unexplored yet or that require further investigation. In this section, we further scrutinise, through content analysis, the papers published between 2015 and 2021 (as we want to focus on the most recent research directions) in order to define a potential research agenda. “Identification of the major research streams”, we report a number of research questions that were put forward over time and are still at least partly unaddressed. Furthermore, Table 6 summarises the key methods applied in the literature, which are divided by category (note that all the papers employ more than one method).

It enables finance businesses to address cybersecurity challenges and enhance data security. AI-driven investment strategies are becoming increasingly popular in wealth management. AI systems enable financial advisors to tailor their advice based on a customer’s risk profile.

Overall, the use of artificial intelligence in finance processes is a true game-changer, and I’m curious to see how these trends will progress in the future. Learn how AI-powered invoice automation works and how it can help you save time, reduce risks, and improve your view of cash flow. First, artificial intelligence can be used to automate the receipt processing step and the categorization of expenses by extracting data from invoices, and then interpreting the data. As shown above, the data extraction step is done through OCR technology, while the actual interpretation of the information is done through AI algorithms. And as AI technology continues to advance and become more accessible, it’s expected that more finance departments will adopt it. In fact, it’s likely that most of the processes that can be automated with machine learning and AI will be.

The Future of AI in Financial Services

This instrument grants financial advisors quick access to a vast repository of around 100,000 research reports. Designed to interpret and respond to queries in complete sentences, it closely mirrors human interaction, thereby enriching the user experience. Are you still unsure about artificial intelligence, or maybe just testing it in smaller ways? We’ll uncover how the top applications of Generative AI in finance can solve the industry’s ten biggest bottlenecks for optimal safety and ROI. Moreover, generative AI models can be used to generate customized financial reports or visualizations tailored to specific user needs, making them even more valuable for businesses and financial professionals. AI can spot anomalies in your data, bringing to your attention outliers and subtle human errors.

The term dates back to 1959, but the area of study began to receive a lot more attention starting in the early 2000s as computational power increased and the internet helped support a trove of data available to train ML models. John Deere’s use of AI demonstrates how technology can radically boost efficiency. By implementing AI to fine-tune every step of the farming process—from identifying weeds to adjusting tractors in real time—John Deere is able to slash waste and cut costs.

They presented various models predicting stock returns and compared them in terms of efficiency and accuracy. The best performers were trees and neural networks—statistical methods modeled on decisions and outcomes, and on the human brain, respectively. The Chat GPT paper has been widely cited in research, racking up more than 1,800 citations so far. Furthermore, AI-driven predictive analytics allow firms to anticipate financial trends, manage risks proactively and provide their clients with deeper financial insights.

For a long time, the finance industry has been combating fraud as it grows with technological advancements. It’s essential to prevent fraud proactively so that it impacts the financial system. Gen AI excels in detecting fraudulent activity patterns in real-time transactions by continuously monitoring financial stats and using encryption techniques.

It will deal with clients in a more personalized and engaging way, much like having a personal financial advisor who knows individual tastes and preferences. 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. This can also include non-traditional data like rental history or utility payments.

Robo-advisors are automated investment advice platforms that use algorithms to manage portfolios according to a customer’s needs. These automated tools provide personalized asset allocation and portfolio optimization recommendations based on a user’s risk profile, age, income level, etc. Blockchain and crypto technology also see increased usage by financial institutions for risk management, as it allows for secure and transparent transactions. By leveraging AI solutions, financial institutions gather insight into customer behavior, which helps them gain a competitive advantage in the market. In this article, we’ll explore how AI in finance is revolutionizing the future of financial management.

We’ve helped many businesses on their journey of building spectacular AI solutions. AI-powered solutions can help you harness the power of analytics and automation. If you’re considering building a game-changing AI solution and don’t know where to start, talk to us. Built In strives to maintain accuracy in all its editorial coverage, but it is not intended to be a substitute for financial or legal advice.

By establishing oversight and clear rules regarding its application, AI can continue to evolve as a trusted, powerful tool in the financial industry. Overall, the integration of AI in finance is creating a new era of data-driven decision-making, efficiency, security and customer experience in the financial sector. While finance will always require a human touch and human judgment for some decisions and relationships, organizations are likely to outsource more work to AI algorithms and other tools like chatbots as the technology improves. However, a new effort by the Biden administration to make it easier for customers to get in touch with a human could hamper some of the push into AI customer service.

In addition, AI can perform the tasks of junior-level analysts, especially in companies that trade a wide range of instruments. For example, you may need analysts to work with different sectors or products. Still, you can entrust the preliminary collection and processing of data to AI, leaving only the final part of the analysis to experts.

According to Forbes, 70% of financial firms are using machine learning to predict cash flow events, adjust credit scores and detect fraud. The finance industry have led the way in really understanding the applications and benefits of ai and data science in terms of specific applications and use cases. By integrating AI solutions, financial companies streamline operations and build trust with regulators and clients. They can ultimately create a more stable and transparent economic environment.

In simple words, artificial intelligence in finance refers to the utilization of AI technologies to streamline and enhance financial services and operations. This involves using ML algorithms, natural language processing, and other AI techniques to analyze data. We observed that the technologies are also used to forecast trends, manage risks, and deliver insights that were previously unattainable with traditional analytical approaches. Businesses and the financial services industry are rapidly evolving toward an algorithmic future, powered by artificial intelligence (AI), machine learning (ML), and other advanced technologies. Companies are leveraging these powerful AI tools in finance to revolutionize how they manage processes, from forecasting market trends to making workflows more efficient, analyzing results, and deploying chatbots.

Wealthblock.AI is a SaaS platform that streamlines the process of finding investors. It helps businesses raise capital and handle automated marketing and messaging and uses blockchain to check investor referral and suitability. Additionally, Wealthblock’s AI automates content and keeps investors continuously engaged throughout the process. AI and blockchain are both used across nearly all industries — but they work especially well together.

Use of AI Chatbots for Customer Support

This confirms that the application potential of AI is very broad, and that any industry may benefit from it. High-paying career opportunities in AI and related disciplines continue to expand in nearly all industries, including banking and finance. If you’re looking for a new opportunity or a way to advance your current career in AI, consider the University of San Diego — a highly regarded industry thought leader and education provider. USD offers an innovative, online AI master’s degree program, the Master of Science in Applied Artificial Intelligence, which is designed to prepare graduates for success in this important fast-growing field. This program includes a significant emphasis on real-world applications, ethics, privacy, moral responsibility and social good in designing AI-enabled systems.

Generative AI is a class of AI models that can generate new data by learning patterns from existing data, and generate human-like text based on the input provided. This capability is critical for finance professionals as it leverages the underlying training data to make a significant leap forward in areas like financial reporting and business unit leadership reports. The finance https://chat.openai.com/ industry is heavily regulated; regulations keep changing monthly or quarterly. Keeping up with changing rules, trends, and financial market conditions takes time and effort. Gen AI helps finance businesses sift through and analyze large amounts of information and regulatory data to provide insights for the upcoming changes in regulatory code or trends to reduce regulatory risks.

TQ Tezos aims to ensure that organizations have the tools they need to bring ideas to life across industries like fintech, healthcare and more. A Vectra case study provides an overview of its work to help a prominent healthcare group prevent security attacks. Vectra’s platform identified behavior resembling an attacker probing the footprint for weaknesses and disabled the attack. Having good credit makes it easier to access favorable financing options, land jobs and rent apartments. So many of life’s necessities hinge on credit history, which makes the approval process for loans and cards important. One report found that 27 percent of all payments made in 2020 were done with credit cards.

Here are a few examples of companies using AI to learn from customers and create a better banking experience. At its core, artificial intelligence empowers machines to perform tasks previously thought to require human intelligence. The technology has existed in early versions for decades, having emerged with a problem-solving computer program, the Logic Theorist, built in the 1950s at what’s now Carnegie Mellon University. Or IBM’s chess-playing computer, Deep Blue, which managed to beat the world champion Garry Kasparov in 1997.

This ensures that payments and reimbursements are approved quickly and efficiently. Next to these use cases, AI algorithms can be used to match invoices with purchase orders and receipts, ensuring that the amounts and details on the invoice are correct. Machine learning can also be used to train an AI engine to recognize different formats and layouts of invoices, making it more accurate and efficient at extracting the data. AI can also automatically match receipts with the corresponding transactions, improving accuracy and reducing the effort required by manual reconciliation. This step is further simplified by the use of smart corporate cards for business-related purchases.

Now, thanks to AI chatbots and virtual assistants, customers can get instant help, 24/7. You can foun additiona information about ai customer service and artificial intelligence and NLP. AI is changing the game for financial customer service, making it faster, smoother, and much more convenient. This includes their income, how they spend money, what they invest in, and even what they do online. With this information, they create a detailed financial profile for each customer. Discover how AI is transforming FinTech by enhancing financial services, improving security, and driving innovation in banking and payments.

ZestFinance is another fintech startup using AI to process alternative data to assess so-called “thin file borrowers”– who have little or no credit history. It provides companies with tools to build tailored underwriting models that can spot good borrowers overlooked by national credit scores. Bookkeeping and accounting processes like recording transactions, reconciling accounts, etc., are highly data-intensive with substantial scope for manual errors. AI can automate these tasks, increase accuracy, and enable employees to focus on higher-value tasks. For example, Scotiabank, one of Canada’s Big Five banks, uses Google AI solutions such as NLP, Voice, and Vision capabilities to automate document processes and customer onboarding– thus improving customer interactions.

Alpaca uses proprietary deep learning technology and high-speed data storage to support its yield farming platform. At an Alorica call center in Albuquerque, New Mexico, one customer-service rep had been struggling to gain access to the information she needed to quickly handle calls. After Alorica trained her to use AI tools, her “handle time’’ — how long it takes to resolve customer calls — fell in four months by an average of 14 minutes a call to just over seven minutes. The Swedish furniture retailer IKEA, for example, introduced a customer-service chatbot in 2021 to handle simple inquiries. Instead of cutting jobs, IKEA retrained 8,500 customer-service workers to handle such tasks as advising customers on interior design and fielding complicated customer calls. Yacine Jernite, who works on policy research at the AI company Hugging Face, said the flops metric emerged in “good faith” ahead of last year’s Biden order but is already starting to grow obsolete.

For more on credit scoring, feel free to read our article on the topic or access an interactive list of leading vendors in the space. Thanks to document capture technologies, financial institutions can automate their credit applicant evaluation processes. This will enable banks and financial institutions to conclude credit applications faster and with fewer errors. Understanding examples of AI in finance is crucial as it illustrates how technology enhances efficiency, innovation, and regulatory compliance in the industry.

These were instrumental in capturing and predicting patterns in real estate price data, ensuring a nuanced and accurate prediction. Moreover, as new data accumulates, model retraining can ensure their predictive capabilities adapt to evolving market conditions, consumer behavior trends, and other dynamics that influence risk. Churn prediction

For existing customers, AI models can forecast churn months in advance based on usage patterns, sentiments, demographics, and other factors.

Through our analysis, we also detected the key theories and frameworks applied by researchers in the prior literature. As shown in Table 4, 73 (out of 110) papers explicitly refer to some theoretical framework. Finance theories (e.g. Arbitrage Pricing Theory; Black and Scholes 1973) are jointly employed with portfolio management theories (e.g. modern portfolio theory), and the two of them account together for 21% (15) of the total number of papers. Finally, bankruptcy theories support business failure forecasts, whilst other theoretical underpinnings concern mathematical and probability concepts. For Chase, consumer banking represents over 50% of its net income; as such, the bank has adopted key fraud detecting applications for its account holders.

Finally, CFOs must remember that the success of niche technologies will depend on the capabilities of the people using them. AI and ML can help optimize and automate countless processes, leading to augmented operational efficiency. It has become a game-changer with tasks that require substantial time and effort. Here are a few examples of companies providing AI-based cybersecurity solutions for major financial institutions.

Artificial intelligence is behind the virtual assistants of many banks, providing personalized financial advice and recommendations to customers. That explains why artificial intelligence is already gaining broad adoption in the financial services industry through chatbots, machine learning algorithms, and other methods. It excels in finding answers in large corpuses of data, summarizing them, and assisting customer agents or supporting existing AI chatbots. For example, in this video, we explore how gen AI can speed up credit card fraud resolution — a win-win for customers and customer service agents. Bankruptcy and performance prediction models rely on binary classifiers that only provide two outcomes, e.g. risky–not risky, default–not default, good–bad performance.

Using NLP and natural language understanding (NLU), they can interpret customer queries expressed in conversational language across multiple channels, such as websites, mobile apps, messaging platforms, etc. NLU can also allow chatbots to determine the underlying intent, e.g., checking account balances, disputing transactions, etc. Simform developed an integrated platform for accounting, invoicing, and payments

The app facilitates comprehensive invoicing management, allowing efficient handling of invoices and payment requests. It seamlessly integrates with both internal and external ERPs such as QuickBooks, Xero, and Sage.

AI developers are doing more with smaller models requiring less computing power, while the potential harms of more widely used AI products won’t trigger California’s proposed scrutiny. Existing publicly released models “have been tested for highly hazardous ai in finance examples capabilities and would not be covered by the bill,” Wiener said. Even when you start small, you need to think big — not just in terms of potential ROI, but also in terms of change management, human resistance to change, leadership alignment and IT alignment.

The company aims for financial firms to have increased accuracy and efficiency. Canoe ensures that alternate investments data, like documents on venture capital, art and antiques, hedge funds and commodities, can be collected and extracted efficiently. The company’s platform uses natural language processing, machine learning and meta-data analysis to verify and categorize a customer’s alternate investment documentation. Simudyne’s platform allows financial institutions to run stress test analyses and test the waters for market contagion on large scales. The company offers simulation solutions for risk management as well as environmental, social and governance settings.

• The financial technology (Fintech) world is experiencing a drastic change, and AI is leading the charge.
• It ensures the protection of sensitive information and transactions from cyber threats.
• It can organize data from multiple sources, dimensions, and types for analysis, identify outliers in large datasets, and reconcile information on behalf of finance teams.
• Human-identified data trends tend to be linear and one-dimensional in scope.
• But it misses the fact that increased taken with costs is negative and that offset changes the meaning of revenue gains.

The higher the K Score, the more likely the stock will outperform the market. In the context of conversational finance, generative AI models can be used to produce more natural and contextually relevant responses, as they are trained to understand and generate human-like language patterns. As a result, generative AI can significantly enhance the performance and user experience of financial conversational AI systems by providing more accurate, engaging, and nuanced interactions with users. For instance, Morgan Stanley employs OpenAI-powered chatbots to support financial advisors by utilizing the company’s internal collection of research and data as a knowledge resource.

Advanced algorithms help financial entities interpret and extract information from images, minimizing errors. Machine learning models empower financial advisors to optimize asset allocation strategies, which can be tailored to individual risk profiles and financial goals. AI-driven recommendations enhance customer engagement by delivering timely insights and actionable suggestions, which breeds trust and satisfaction.

The growth in Gen AI usage was led by advancements in machine learning, an increase in data volume, and reduced operational costs. As a financial business, if you want to leverage generative AI services to revolutionize processes with gen AI algorithms, this blog will help. AI enhances finance through efficiency and cost savings from business process automation, detecting data pattern anomalies, and improving controls and risk management. Although your company will not need to make as many hires with the right finance automation solution, your company’s entire finance team will not be replaced. Such models can predict future market trends based on past data, allowing businesses to make more informed decisions and increase profitability.