31 Examples of AI in Finance 2024

Natural Language Processing (NLP), a subset of AI, is the ability of a computer program to understand human language as it is spoken and written (referred to as natural language). Smart contracts are distributed applications written as code on Blockchain ledgers, automatically executed upon reaching pre-defined trigger events written in the code (OECD, 2020[25]). Spoofing is an illegal market manipulation practice that involves placing bids to buy or offers to sell securities or commodities with the intent of cancelling the bids or offers prior to the deal’s execution. It is designed to create a false sense of investor demand in the market, thereby manipulating the behaviour and actions of other market participants and allowing the spoofer to profit from these changes by reacting to the fluctuations.

• 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.
• In some jurisdictions, comparative evidence of disparate treatment, such as lower average credit limits for members of protected groups than for members of other groups, is considered discrimination regardless of whether there was intent to discriminate.
• These younger consumers prefer digital banking channels, with a massive 78% of millennials never going to a branch if they can help it.
• These mechanisms are the ultimate line of defence of traders, and instantly switch off the model and replace technology with human handling when the algorithm goes beyond the risk system and do not behave in accordance with the intended purpose.
• And if we look at the spend management process specifically, AI can be used to detect fraudulent invoices, duplicate payments, and expenses that breaching company policies.

Such investment is not constrained in monetary resources required to be invested in AI technologies but also relates to talent and staff skills involved in such techniques. Such risk of concentration is somewhat curbed by the use of third-party vendors; however, such practice raises other challenges related to governance, accountability and dependencies on third parties (including concentration risk when outsourcing is involved) (see Section 2.3.5). 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. Kensho, an S&P Global company, created machine learning training and data analytics software that can assess thousands of datasets and documents. Its data training software uses a combination of machine learning, cloud computing and natural language processing, and it can provide easily understandable answers to complex financial questions, as well as extract insights from tables and documents quickly.

Anti-Money Laundering and Fraud

Moving ERP to the cloud allows businesses to simplify their technology requirements, have constant access to innovation, and see a faster return on their investment. Despite its remarkable potential to help finance organizations navigate complex, high-volume data, generative AI’s limitations introduce real challenges that CFOs must raise when considering use of generative AI in finance and across the organization. Successful CFOs partner with senior technology leadership (e.g., the CIO, chief data officer, chief information security officer) to distinguish hype from reality, and then share the results of those conversations with other executive leadership team members. Shapeshift is a decentralized digital crypto wallet and marketplace that supports more than 750 cryptocurrencies. The platform provides users access to nine different blockchains and eight different wallet types.

• Most banks (80%) are highly aware of the potential benefits presented by AI, according to Insider Intelligence’s AI in Banking report.
• Financial institutions get real-time data analysis and insights with AI-powered analytics and predictive modeling.
• It can also amplify network effects, such as unexpected changes in the scale and direction of market moves.

Cloud computing services such as AWS or Google Cloud Platform are helping companies develop innovative AI solutions that quickly assess market risks in real-time and accurately identify potential compliance issues. Chatbots are becoming increasingly popular in financial services as they can provide customers with personalized advice or recommendations regarding their financial decisions based on ML techniques. Further, the use of NLP can aid text mining and analysis of social media data such as tweets, Instagram posts, and Facebook posts, which impact trading decisions.

Financial Transformation on the Rails: A smooth ride for Stadler with Yokoy

Figure Marketplace uses blockchain to host a platform for investors, startups and private companies to raise capital, manage equity and trade shares. The platform validates customer identity with facial recognition, screens customers to ensure they are compliant with financial regulations and continuously assesses risk. Additionally, the platform analyzes the identity of existing customers through biometric authentication and monitoring transactions. The following companies are just a few examples of how artificial intelligence in finance is helping banking institutions improve predictions and manage risk. If there’s one technology paying dividends for the financial sector, it’s artificial intelligence.

Automated receipt processing and expense categorization

AI models executed on a blockchain can be used to execute payments or stock trades, resolve disputes or organize large datasets. An f5 case study provides an overview of how one bank used its solutions to enhance security and resilience, while mitigating key cybersecurity threats. The company’s applications also helped increase automation, accelerate private clouds and secure critical data at scale while lowering TCO and futureproofing its application infrastructure. The finance industry is undergoing significant transformation, driven by AI, creating new opportunities for growth and reshaping service delivery.

AI in Finance: 10 Use Cases You Should Know About in 2023

Given the sky-high demand for Nvidia’s GPUs, the company has made significant investments in manufacturing and production. I see this as an opportunity for AMD to make inroads in the data center AI market and acquire additional market share. Because as Nvidia’s backlog continues to grow, I see customers diversifying their GPU needs and turning to more than one provider. AI-driven investment strategies are becoming increasingly popular as they enable financial advisors to tailor their advice based on a customer’s risk profile. Data-driven decisions enable organizations to make more accurate predictions about financial trends and create better strategies for their business operations.

Of course, concerns around AI remain an industry priority, particularly when the conversation turns to the use of sensitive financial data in these systems. How do we prevent AI from being fed with and then producing data that will lead to erroneous conclusions? On the training side, we have to make sure we are feeding the right kind of data into AI tools—that we aren’t feeding data with a lot of “one-off” numbers, which would then become normalized. Essentially, we have to teach the AI that certain data are incorrect and should be discarded. Explore what generative artificial intelligence means for the future of AI, finance and accounting (F&A).

Artificial intelligence is behind the virtual assistants of many banks, providing personalized financial advice and recommendations to customers. As AI technology continues to advance, it is expected that the use of artificial intelligence technologies in fraud detection will expand further, resulting in increased efficiency, accuracy, and security in the finance industry. Overall, AI can help with process automation, streamlining the VAT reclaim process, reducing the time and resources required to manage tax reclaims, and minimizing the risk of human errors. This can lead to significant cost savings for companies and provide greater accuracy and efficiency in the VAT reclaim process.

Alpaca uses proprietary deep learning technology and high-speed data storage to support its yield farming platform. (Yield farming is when cryptocurrency investors pool their funds to carry out smart contracts that gain interest.) Alpaca is compatible with dozens of cryptocurrencies and allows users to lend assets to other investors in exchange for lending fees and protocol rewards. Quantitative trading is the process of using large data sets to identify patterns that can be used to make strategic trades. AI-powered computers can analyze large, complex data sets faster and more efficiently than humans.

Darktrace’s AI, machine learning platform analyzes network data and creates probability-based calculations, detecting suspicious activity before it can cause damage for some of the world’s largest financial firms. The platform lets investors buy, sell and operate single-family homes through its SaaS and expert services. Additionally, Entera can discover market trends, match properties with an investor’s home and complete transactions. Zest AI is an AI-powered underwriting platform that helps companies assess borrowers with little to no credit information or history.

Enova has a lending platform powered by AI and ML, and the technologies help with advanced financial analytics and credit assessment. The company has provided over 8 million customers with over $49 billion in loans and financing with market-leading products guiding them to improve their financial health. They have also been helping small businesses and non-prime customers what is process costing what it is and why its important to help solve real-life problems, which include emergency costs and bank loans. Finally, another general area where artificial intelligence can be used is data analysis and forecasting. Instead of relying on outdated methods, finance teams can use AI and machine learning algorithms to analyze historical data and make predictions about future trends with much more ease.