AI in Finance: Applications + Examples

This transformative impact of AI in the financial industry is largely driven by a diverse set of AI technologies, which we discuss below. The world of finance is changing rapidly, with disruptive technologies and shifting consumer expectations reshaping the landscape. Yet, despite these changes, many finance tools remain stuck in the past, with a poor user experience and interface. NLP or natural language processing is the branch of AI that gives computers the ability to understand text and spoken words in much the same way human beings can. Both OCR and artificial technology play a crucial role in automating financial processes, but their applications are distinct and serve different purposes.

We used TrueLayer’s open banking API to integrate with various banks and enable secure transactions. We employed microservices to efficiently manage critical modules such as loan calculations, affiliation processes, and user verification. VPN ensured secure communication between these modules, making it a highly responsive and reliable system. Explore the transformative impact of AI across banking, insurance, investment, and discover how to harness its power for your financial services business. This is, of course, thanks to the ability of these chatbots to handle customer inquiries around the clock, reducing the need for human customer service representatives and allowing financial institutions to operate more efficiently.

Furthermore, the company also positions itself as a leader in the industry’s technological evolution. This aspect makes the model adept at spotting complex deceptive patterns previously undetectable. Thus, professionals get a powerful tool to fight against sophisticated financial crimes.

AI Companies Managing Financial Risk

Moreover, concerns about AI’s “black box” nature today make it challenging to explain results and instill confidence, especially for high-stakes decisions like lending approvals or insurance underwriting. While AI offers immense potential in fintech, organizations face several challenges in effectively implementing and scaling AI solutions. HSBC trained Google Cloud’s AML AI on its vast range of customer data to spot suspicious activities with more precision than manual optimization. It identifies 2-4x as much suspicious activity as its previous system while reducing the number of alerts by 60%. Renaissance Technologies is widely considered one of the most successful firms in using algorithmic trading. Their flagship fund, the Medallion Fund, has an impressive track record with average annual returns of 66% since 1988.

Some candidates may qualify for scholarships or financial aid, which will be credited against the Program Fee once eligibility is determined. We expect to offer our courses in additional languages in the future but, at this time, HBS Online can only be provided in English. Imagine applying the same precision to your operations and eliminating inefficiencies, streamlining workflows, and making smarter, faster decisions. You’re not just implementing a new technology but leveraging it to bolster your organization’s productivity and give you an edge over the competition. In the healthcare industry, several companies are integrating AI into business operations.

Real-World Companies That Use AI in Business

AI significantly increases operational efficiency in finance by streamlining processes and expediting transactions and decision-making. By automating routine tasks like data analysis and report generation, AI reduces manual effort, allowing staff to focus on strategic tasks. Financial markets are largely driven by news, events, market sentiments, and multiple economic factors. By analyzing vast historical and current data using complex models, AI systems predict future risks more accurately than conventional methods. For instance, American Express runs deep learning-based models as part of its fraud prevention strategy. Their fraud algorithms monitor every transaction around the world in real time (more than $1.2 trillion spent annually) and generate fraud decisions in milliseconds.

This allows logging into payment apps and authorizing transactions with just a glance at the camera, delivering a frictionless experience far more secure than passwords/PINs. To enhance mobile security, we performed extensive security audits to ensure no application module was vulnerable ai in finance examples to attacks. We also secured the data using different standards, such as HTTP protocols, AES-256 Encryption, and voice authorization. Going beyond optimizing front-office and back-office operations, AI in fintech can also aid marketing and sales efforts for growth and profitability.

It is critical in optimizing financial operations and unveiling opportunities that drive boundless growth with incredible applications. 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. With iterative development, identifies issues that are addressed effectively by the team before it’s launched for the customers. We will walk you through Gen AI use cases leveraged at scale, famous real-life examples of some big companies using Gen AI in finance, and the Gen AI solutions implementation process. AI’s potential to revolutionize how businesses manage their finances has become increasingly evident as organizations adopt it more significantly. Additionally, algorithmic trading bots sometimes act erratically during market volatility, potentially leading to losses for investors if not adequately monitored by humans.

In this way, everything related to reducing the burden on a person in routine tasks continues to evolve. As long as AI implementation gives companies competitive advantages, they will introduce new technologies as they become available. Now that we know what business value https://chat.openai.com/ the technology proposes, it’s time to move on to discussing the strategies to manage the challenges we identified initially. At Master of Code Global, as one of the leaders in Generative AI development solutions, we have extensive expertise in deploying such projects.

AI-powered translation capabilities are transforming finance by breaking language barriers and facilitating seamless communication across global markets. Others often leverage rule-based AI for more acute processes, such as anomaly detection. These more stringent forms of AI are designed to identify and address specific issues with high precision. Grandview Research reveals the global market for artificial intelligence in financial technology was worth 9.45 billion US dollars in 2021. Algorithmic trading (aka algo trading) allows traders to execute trades more accurately and faster. The rise of Artificial intelligence (AI) in the global financial services landscape is undergoing a major transformation.

Varun Saharawat is a seasoned professional in the fields of SEO and content writing. With a profound knowledge of the intricate aspects of these disciplines, Varun has established himself as a valuable asset in the world of digital marketing and online content creation. Kensho, a top AI company owned by S&P Global, uses AI to analyze tons of financial information, news, and even things like satellite images or social media posts.

However, you’ll see that many of these use cases are applicable to other financial processes too. Much like AI algorithms do with lending or cybersecurity, machine learning algorithms can sort through large volumes of transaction data to flag suspicious activity and possible fraud. Fraud is a serious problem for banks and financial institutions, so it shouldn’t be surprising that they’re embracing new technologies to prevent it. Machine learning, which means the ability of computers to teach themselves things using pattern recognition from the data they sample, might be the best-known application of artificial intelligence.

AI in finance: Applications + examples

AI’s ability to rapidly and comprehensively read and correlate data combined with blockchain’s digital recording capabilities allows for more transparency and enhanced security in finance. AI models executed on a blockchain can be used to execute payments or stock trades, resolve disputes or organize large datasets. Order.co helps businesses to manage corporate spending, place orders and track them through its software.

• The organization leveraged Gen AI to enhance fraud detection capabilities, enable personalized financial advice, optimize portfolio management automatically, and more.
• For instance, AI-driven chatbots provide real-time assistance, while machine learning models predict customer needs and suggest relevant financial products.
• These capabilities enhance profitability, ensuring pricing decisions are always data-driven, competitive and precise.
• This could lead to a more skilled and motivated workforce, ultimately benefiting both the bank and its customers.

In the past, financial services were often the same for everyone, offering generic advice and products. But now with AI, companies can get to know customers and offer solutions that truly fit their needs. Financial markets are relying more and more on Artificial Intelligence and machine learning to create safer and more agile models for risk management. AI assistants, such as chatbots, use Artificial Intelligence and natural language processing to provide self-help customer service, 24/7.

AI in Finance: The Double-Edged Sword Redefining Financial Services

These methods may be restrictive as sometimes there is not a clear distinction between the two categories (Jones et al. 2017). Corporate credit ratings and social media data should be included as independent predictors in credit risk forecasts to evaluate their impact on the accuracy of risk-predicting models (Uddin et al. 2020). Moreover, it is worth evaluating the benefits of a combined human–machine approach, where analysts contribute to variables’ selection alongside data mining techniques (Jones et al. 2017). Forthcoming studies should also address black box and over-fitting biases (Sariev and Germano 2020), as well as provide solutions for the manipulation and transformation of missing input data relevant to the model (Jones et al. 2017). This research stream focuses on algorithmic trading (AT) and stock price prediction.

• Another interesting application of finance AI is customer service, where the adoption of chatbots is on the rise.
• This reduces the need for manual data entry and eliminates human errors, making the invoice processing workflow more time- and cost-efficient.
• We believe that the incorporation of Artificial Intelligence in finance not only boosts operational efficiency and improves customer experiences but also transforms decision-making processes.
• 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.
• 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.

Generative AI can be used for fraud detection in finance by generating synthetic examples of fraudulent transactions or activities. These generated examples can help train and augment machine learning algorithms to recognize and differentiate between legitimate and fraudulent patterns in financial data. Generative AI is a type of artificial intelligence that uses algorithms to generate complex, creative content, like audio, images, videos, and text. For example, you could ask Generative AI a question about Q2 budget variance, and it will use sophisticated linguistic models to extract information from a large data set and prepare it as a graph, ready for you to analyze. Of all the different types of AI, Generative AI has the potential to elevate the way finance teams work. Deloitte writes, “We are on the cusp of an ‘iPhone moment’ — a major revolution in our personal and business lives.

Yokoy’s AI model uses pre-defined rules and learns from each receipt and expense report processed, getting smarter with time. OCR is a technology that is designed to recognize and Chat GPT convert text from scanned documents or images into machine-readable text. It enables computers to “read” and understand printed or handwritten text and turn it into digital data.

Banks can offer tailored financial advice, customized investment portfolios, and personalized banking services. For instance, AI-driven chatbots provide real-time assistance, while machine learning models predict customer needs and suggest relevant financial products. Personalized services enhance customer satisfaction and loyalty, driving better engagement and retention. AI technologies interpret vast amounts of data, learn from them, and then make autonomous decisions or assist in decision-making processes. In finance, this often translates into applications like algorithmic trading, fraud detection, customer service enhancement, and risk management. Integrating AI into accounts payable and receivable processes has become a game-changer for accounting and finance companies.

Finally, we observe that almost all the sampled papers are quantitative, whilst only three of them are qualitative and four of them consist in literature reviews. Prioritizing cybersecurity also safeguards client assets and reinforces digital trust in financial services. Its platform finds new access points for consumer credit products like home equity lines of credit, home improvement loans and even home buy-lease offerings for retirement. You can foun additiona information about ai customer service and artificial intelligence and NLP. Figure Marketplace uses blockchain to host a platform for investors, startups and private companies to raise capital, manage equity and trade shares. The following companies are just a few examples of how AI-infused technology is helping financial institutions make better trades.

In this section, we explore the patterns and trends in the literature on AI in Finance in order to obtain a compact but exhaustive account of the state of the art. Specifically, we identify some relevant bibliographic characteristics using the tools of bibliometric analysis. After that, focussing on a sub-sample of papers, we conduct a preliminary assessment of the selected studies through a content analysis and detect the main AI applications in Finance. To conduct a sound review of the literature on the selected topic, we resort to two well-known and extensively used approaches, namely bibliometric analysis and content analysis. In this study, we perform bibliometric analysis using HistCite, a popular software package developed to support researchers in elaborating and visualising the results of literature searches in the Web of Science platform. Since artificial intelligence has become more widespread across all industries, it’s no surprise that it is taking off within the world of finance, especially since COVID-19 has changed human interaction.

These results corroborate the fact that the above-mentioned regions are the leaders of the AI-driven financial industry, as suggested by PwC (2017). The United States, in particular, are considered the “early adopters” of AI and are likely to benefit the most from this source of competitive advantage. More lately, emerging countries in Southeast Asia and the Middle East have received growing interest. Finally, a smaller number of papers address underdeveloped regions in Africa and various economies in South America.

As a result, VideaHealth reduces variability and ensures consistent treatment outcomes. Harvard Business School Online’s Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills. Offer comprehensive AI training programs to ensure your staff can use the new AI tools effectively. Encourage a culture of continuous learning to keep up as the technology advances. Moreover, concerns around data privacy are not AI’s main problem as many may think. If someone wants to get information about you, it can be done without the help of AI.

Finally, training teams to use these new systems effectively is no small task and requires time and resources. Business owners must communicate the benefits of AI and offer training to help employees adapt to new technologies. Accounting and finance are not typically the first industries people consider to use artificial intelligence (AI). A November 2023 Gartner survey found that 60% of finance respondents do not use AI. However, many of the AI capabilities in this market have already been used, and only small improvements still need to be made.

With Tipalti AI℠, businesses can make more informed decisions based on up-to-date information about payables and spending data. AI-driven tools like chatbots and automated advisory services provide instant responses to customer inquiries, facilitating uninterrupted banking and financial advice. Artificial intelligence (AI) in finance is the use of technology, including advanced algorithms and machine learning (ML), to analyze data, automate tasks and improve decision-making in the financial services industry. The resulting sentiment is regarded either as a risk factor in asset pricing models, an input to forecast asset price direction, or an intraday stock index return (Houlihan and Creamer 2021; Renault 2017). As for predictions, daily news usually predicts stock returns for few days, whereas weekly news predicts returns for longer period, from one month to one quarter.

Utilized by top banks in the United States, f5 provides security solutions that help financial services mitigate a variety of issues. The company offers solutions for safeguarding data, digital transformation, GRC and fraud management as well as open banking. An AI-powered search engine for the finance industry, AlphaSense serves clients like banks, investment firms and Fortune 500 companies. The platform utilizes natural language processing to analyze keyword searches within filings, transcripts, research and news to discover changes and trends in financial markets. Ayasdi creates cloud-based machine intelligence solutions for fintech businesses and organizations to understand and manage risk, anticipate the needs of customers and even aid in anti-money laundering processes. Its Sensa AML and fraud detection software runs continuous integration and deployment and analyzes its own as well as third-party data to identify and weed out false positives and detect new fraud activity.

Hire AI developers to enable gen AI-powered financial report generation that is accurate and produced in less time. The finance industry and businesses are undergoing significant transformation, driven by AI, creating new opportunities for growth and reshaping service delivery and operations. A business that adopts the right tools today, will gain a sharp competitive edge in tomorrow’s race. AI has the potential to spur innovation and foster growth across various business activities such as spend management, cost and procurement optimization, minimizing waste, and predicting future spend. Generative models also simulate different outcomes for financial scenarios, such as macroeconomic events or regulatory changes impacting a company’s performance. This allows lenders and borrowers alike to understand how potential changes affect their finances.

This strategic use of AI ensures that financial services remain innovative and responsive to market dynamics and customer needs. AI enhances cybersecurity in financial institutions by detecting and responding to threats in real-time, thereby safeguarding sensitive data and financial assets. In fraud detection and compliance, AI identifies unusual patterns that deviate from normative behaviors to flag potential frauds and breaches early. AI-driven speech recognition is used in finance to enhance customer interaction through voice-activated banking, helping users to execute transactions or get support without manual input. By combining AI with human expertise, we can make better decisions, handle risks more effectively, and achieve better financial results.

Oliver Wyman shares that using AI insights can increase annual income from email cross-sell by four times. Similarly, financial companies can capture relevant data from borrower companies’ financial documents, like annual reports and cash flow statements. With the extracted data, credit evaluation can be handled much accurately, and banks can provide faster services for lending operations. AI-driven translation tools streamline operations, enhance transparency, and support decision-making by providing timely access to multilingual data and insights. This capability is crucial in expanding market reach, boosting global partnerships, and driving innovation within the financial industry.

For instance, internal audit functions can be greatly enhanced by generative AI through automated analysis and reporting. For example, BloombergGPT was also evaluated in the sentiment analysis task. As a fine-tuned generative model for finance, it outperformed other models by succeeding in sentiment analysis. Financial institutions can benefit from sentiment analysis to measure their brand reputation and customer satisfaction through social media posts, news articles, contact centre interactions or other sources. By leveraging its understanding of human language patterns and its ability to generate coherent, contextually relevant responses, generative AI can provide accurate and detailed answers to financial questions posed by users.

This is incredibly valuable to leadership teams because AI can prevent mistakes and bad information from propagating into reports, plans, and decision-making. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates.

This technological empowerment enables banks and financial companies to explore untapped markets and tailor offerings to meet diverse customer needs more effectively. AI models can process alternative data sources like social media, mobile footprints, and browser histories to gain a comprehensive view of an individual’s financial behavior. Using techniques like neural networks, decision trees, and clustering algorithms, AI can discover highly complex patterns and interrelationships across hundreds of data dimensions correlating with credit risk.