5 Ways AI is Revolutionizing FinTech in 2024 Real-World Examples & Experts’ Insights

For example, AI can find patterns in customer behavior by analyzing past purchasing habits. This is particularly useful for B2C companies who want to encourage repeated purchases, as AI models can provide personalized product recommendations based on those insights, in real time. OCR technology is a subset of AI and is used extensively in financial institutions to automate tasks such as document processing, data extraction, and fraud detection. Consumers are hungry for financial independence, and providing the ability to manage one’s financial health is the driving force behind adoption of AI in personal finance. Artificial intelligence (AI) and machine learning in finance encompasses everything from chatbot assistants to fraud detection and task automation. Most banks (80%) are highly aware of the potential benefits presented by AI, according to Insider Intelligence’s AI in Banking report.

Adopting AI solutions for accounting and finance is no longer a luxury — it’s necessary to stay competitive. By utilizing AI, businesses can gain real-time insights into their financial health, enable more informed decision-making and proactive management and leverage innovation to drive growth and long-term success. Expected benefits of AI in finance and accounting include boosting productivity and efficiency, improved data accuracy and compliance and cost savings. When it comes to portfolio management, classical mathematics and statistics are most often used, and there is not much need for AI. However, it can be used, for example, to find a quantitative and systematic method to construct an optimal and customized portfolio.

• AI enhances cybersecurity in financial institutions by detecting and responding to threats in real-time, thereby safeguarding sensitive data and financial assets.
• The famous company JPMorgan Chase has used AI to reduce its documentation workload.
• Beyond handling customer inquiries, these AI-powered assistants process transactions and provide financial updates without human intervention.
• Whether optimizing operations, enhancing customer satisfaction, or driving cost savings, AI can provide a competitive advantage.
• This paper aims to provide an accurate account of the state of the art, and, in doing so, it would represent a useful guide for readers interested in this topic and, above all, the starting point for future research.

Companies can offer AI chatbots and virtual assistants to monitor personal finances. These assistants can provide insights based on target savings or spending amounts. Besides giving insights on personal finances, robo-advisors can give financial advice to help investors manage their portfolio optimally and recommend a personalized investment portfolio containing shares, bonds, and other asset types. To do that, robo-advisors use customers’ information about their investment experience and risk appetite. AI can analyze customer behaviors and preferences through sophisticated algorithms and natural language processing to offer tailored financial advice and product recommendations. This improves customer satisfaction and deepens client engagement and loyalty.

But with AI, financial institutions are better equipped than ever to protect businesses and customers. You can foun additiona information about ai customer service and artificial intelligence and NLP. AI-powered robo-advisors are democratizing access to sophisticated financial strategies for average consumers at a fraction of the cost of traditional financial advisors. Even small-scale investors can now benefit from AI-driven investment tools that were once available only to high-net-worth individuals and institutions, save money on fees, and build wealth passively. By utilizing a variety of tools to accurately assess every type of borrower, AI solutions support banks and other credit lenders in the credit decision making process.

Timely identification of emerging risks enables proactive mitigation strategies. AI frees up professionals to concentrate on more strategic initiatives that require critical thinking and analysis. It also leads to faster turnaround times, boosted performance across operations, and a profound understanding of complex financial details. 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. With platform’s help, lenders can promise higher approval rates for these underserved groups.

Is finance at risk of AI?

A study by Erik Brynjolfsson of Stanford University and Danielle Li and Lindsey Raymond of MIT tracked 5,200 customer-support agents at a Fortune 500 company who used a generative AI-based assistant. AI can also do the drudge work, freeing up people to do more creative tasks. Consider Suumit Shah, an Indian entrepreneur who caused a uproar last year by boasting that he had replaced 90% of his customer support staff with a chatbot named Lina.

In the meantime, a growing and heterogeneous strand of literature has explored the use of AI in finance. The aim of this study is to provide a comprehensive overview of the existing research on this topic and to identify which research directions need further investigation. Accordingly, using the tools of bibliometric Chat GPT analysis and content analysis, we examined a large number of articles published between 1992 and March 2021. Future research should seek to address the partially unanswered research questions and improve our understanding of the impact of recent disruptive technological developments on finance.

Potential Roadblocks

Let’s consider real challenges to AI’s ubiquitous implementation in finance and the pitfalls we need to solve now so that AI can still reach the masses. Financial markets are in constant flux, and traditional appraisal methods lag behind, leaving investors vulnerable to missed possibilities. Gen AI-powered advising leads to greater consumer satisfaction, stronger advisor-client relationships, and increased confidence in suggested decision-making guides. Let’s now examine how companies across the globe are implementing generative solutions for competitive advantage.

A Deloitte survey found that 85% of its respondents who used AI-based solutions in the pre-investment phase agreed that AI helped them generate an alpha strategy. From credit scoring that goes beyond traditional metrics to robo-advisors offering personalized investment strategies, AI is using data like never before to make financial products and services sharper. In this blog, we explore the most prominent use cases of AI in fintech along with some real-world examples.

This approach mitigates risks and promotes a healthy financial system for long-term growth. Major strides in data and computer sciences have seen AI graduate from the pages of science fiction. The true challenge will be for finance chiefs to identify where automation could transform their organizations. Further, they should check whether the opportunities to automate are in areas that consume valuable resources and slow down operations.

In reality, AI has found its place in finance and is increasingly being used to enhance various processes. Learn why digital transformation means adopting digital-first customer, business partner and employee experiences. Finally, artificial intelligence is also being used for investing platforms to recommend stock picks and content for users.

The introduction of AI-driven automation into financial workflows results in a more agile and responsive environment. Employees are relieved from mundane tasks, leading to higher job satisfaction and productivity. AI automates the processing of vast amounts of financial documents, reducing errors and increasing processing speed.

High-frequency trading

By rapidly iterating through the above workflows in milliseconds, AI can also enable high-frequency, low-latency trading strategies to capitalize on minuscule market inefficiencies for more profits. Also known as algo trading, it is one of the most popular applications of AI in fintech to rapidly identify and capitalizing on lucrative trading opportunities. Simform developed a voice-enabled smart wallet for safekeeping of credit/debit cards

We built a smart wallet product by leveraging biometric, IoT, and cloud technologies with an accompanying mobile app solution. We established a stable and secure connection between the device and the app with Bluetooth Low Energy (BLE). The connection was made exclusive and highly secure by implementing the GATT profile setup.

Explore more on how generative AI can contribute to software development and reduce technology costs, helping software maintenance. When  hiring AI developers to build a Gen AI project, ensure the solution seamlessly integrates with the existing business system. Smooth transition, glitch-free UI/UX interaction, and operations are ensured so existing workflow won’t get hampered. Organizations should also regularly test and monitor their AI models to ensure they adhere to ethical standards and legal regulations.

Lemonade uses AI for customer service with chatbots that interface with customers to offer quotes and process claims. In 2023, it set a record when AI-Jim, its AI claims processing agent, paid a theft claim in just two seconds. The company says it settles close to half of its claims today using AI technology. One of the most common applications of artificial intelligence in finance is in lending. Machine learning algorithms and pattern recognition allow businesses to go beyond the typical examination of credit scores and credit histories to rate borrowers’ creditworthiness when applying for credit cards and other loans.

Take, for example, the common yet often overlooked issue of time-consuming data retrieval processes in finance departments. On the surface, improving the speed of data access may appear to be a minor fix. However, if an AI solution could streamline these processes — reducing data retrieval times from several hours to just a few minutes — the implications would be substantial. Such an enhancement in data accessibility can significantly boost the productivity of the entire finance team. The journey of incorporating AI into finance functions often begins at a crossroads, contemplating the strategic approach to adoption.

Banks, money transfer companies, and payment processors now use AI to analyze transactions and catch anything unusual that might signal fraud. Managing huge amounts of data, Artificial Intelligence can generate tailor made financial advice, giving personalized insights for wealth management. Artificial Intelligence applied to online and mobile banking is a value added for all customers, perfecting tools to help them monitor their budget and make real-time spending adjustments.

Advanced machine learning algorithms enable financial institutions to monitor and respond to anomalies in real-time. From digital databases that store our financial information to sophisticated systems that calculate complex transactions, the success of modern financial services is inherently linked to technology. American insurance company Lemonade uses AI for customer service with chatbots that interface with customers to offer quotes and process claims.

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. By learning from historical financial data, generative AI models can capture complex patterns and relationships in the data, enabling them to make predictive analytics about future trends, asset prices, and economic indicators. Now that we’ve covered different types of AI, let’s explore what AI does for CPM processes at a functional level.

These AI tools also act as watchdogs, identifying irregularities and guaranteeing accurate reporting. AI enables financial institutions to personalize services and products for their customers. AI algorithms can identify individual preferences and behaviors by analyzing vast data sets. Data insights also help understand customers, personalize services, and predict market trends. These skills are like a superpower, helping them follow rules, innovate, stay competitive, and gain valuable insights.

Another benefit of AI is that it can analyze large amounts of complex data faster than people, which provides time and money-saving. Kavout, an AI trading service, estimates that they can approximately generate 4.84% with their AI-powered trading models. Thus, banks must use personalized banking to gain a competitive advantage, improving customer engagement and loyalty. Banks can create a more personalized experience for customers through customized products and services, which can lead to increased customer satisfaction and retention. Ultimately, banks that invest in data analytics and AI technology will continue to thrive in the digital age. AI assistants, such as chatbots, use AI to generate personalized financial advice and natural language processing to provide instant, self-help customer service.

Generative AI in fintech is becoming increasingly popular with assistant chatbots, particularly in banking. Some noteworthy examples include Bank of America’s virtual assistant Erica, Capital One’s chatbot named Eno, Wells Fargo’s bot Fargo, and Zurich Insurance’s Zara. Even large corporations like Wells Fargo are using AI models to consider alternative data points to assess applicants’ creditworthiness. For example, HSBC’s Voice ID allows you to access phone banking with your voice. It uses advanced voice biometric technology to verify your identity with your unique voice.

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. Socure created ID+ Platform, an identity verification system that uses machine learning and AI to analyze an applicant’s online, offline and social data, which helps clients meet strict KYC conditions. The system runs predictive data science on information such as email addresses, phone numbers, IP addresses and proxies to investigate whether an applicant’s information is being used legitimately.

AI systems require access to sensitive financial data, raising questions about how this information is stored and protected. Ensuring robust cybersecurity measures is essential to mitigate these risks. To achieve seamless AI integration, companies should take a strategic approach beyond adopting the technology. ​​They need to focus on preparing their workforce for the change, educating them on AI tools, and fostering a culture of adaptability.

Bank of America

By harnessing the power of machine learning and advanced analytics, firms can now sift through vast amounts of data with remarkable speed and precision, uncovering patterns previously hidden. This leap in business intelligence enables financial professionals to move beyond traditional number-crunching, allowing them to predict market movements, optimize investment strategies and personalize client services like never before. For instance, AI can predict cash flow shortages and suggest mitigation measures. When analyzing historical data, AI can identify patterns with astonishing accuracy. AI can provide valuable insights that lead to more accurate budgeting and risk management and the ability to make decisions that drive growth and efficiency.

Machine learning models are particularly helpful in corporate finance as they can improve loan underwriting. This ability applied to Finance is vital to prevent fraud – such as money laundering  – and cyberattacks. Obviously, consumers want their banks and financial institutions to be reliable, and most of all they want secure accounts, in order to avoid online payment fraud losses.

As AI is more valuable when used at scale, businesses still need to learn how to effectively integrate AI across all processes but retain its ability to be adjusted and customized. Betterment is a renowned robo-advisor that invests and manages individual, ROTH IRA, 401(k), and IRA accounts. These robo-advisors use AI to automate investment management, tailoring strategies to individual financial profiles and adjusting portfolios in response to market changes.

The famous company JPMorgan Chase has used AI to reduce its documentation workload. They use their COiN platform, which leverages AI to analyze legal documents, drastically reducing the time required for data review from hundreds of thousands of hours to seconds. According to the Federal Bureau of Investigation, the US experienced fraud losses of $4.57 Billion in 2023. This major concern can potentially be catered to by AI as it can act as a powerful defense against financial fraud.

This aids in creating a more dynamic, secure, and profitable financial landscape. AI companies need relevant financial data from diverse sources to be cleaned and pre-processed in the required format for the best data management and preparation. Also, data enhancements that align with regulatory compliance ensure winning results. As an example of AI, New https://chat.openai.com/ York-based startup Kensho Technologies offers various AI-based services for financial institutions, including algorithmic trading and risk analysis tools. AI technologies are also increasingly used for algorithmic trading in financial markets, with companies utilizing AI bots to automate trading processes and optimize strategies for maximum returns.

Some forms of AI in finance involve training computers to learn and perform complex tasks without pre-programming. Intelligent automation has the capacity to transform financial services organizations and enhance customer interactions. The possibilities of automation help the finance teams to make the best use of data. Derivative Path’s platform helps financial organizations control their derivative portfolios. The company’s cloud-based platform, Derivative Edge, features automated tasks and processes, customizable workflows and sales opportunity management.

It also supports personalized customer interactions and targeted marketing efforts, enhancing service delivery and customer satisfaction. Ultimately, predictive modeling empowers finance professionals to navigate uncertainties and capitalize on opportunities in a dynamic economic environment. Financial companies use them to manage risk better, invest smarter, and work more efficiently. These tools enable real-time dialogue across multiple platforms, enhancing customer engagement and satisfaction.

Yes, this is annoying for some, but the process will become more accessible and more pleasant over time. One day, AI will finally adjust to human communication style and become much more helpful, and the technology will become increasingly involved in customer service. While our technologies are impressive today, they are only narrow, specialized AI systems that solve individual tasks in particular fields. They do not have self-awareness, cannot think like humans, and are still limited in their abilities.

Gen AI is particularly good at discovering and summarizing complex information, such as mortgage-backed securities contracts or customer holdings across various asset classes. The content analysis also provides information on the main types of companies under scrutiny. Table 5 indicates that 30 articles (out of 110) focus on large companies listed on stock exchanges, whilst only 16 studies cover small and medium enterprises. Similarly, trading and digital platforms are examined in 16 papers that deal with derivatives and cryptocurrencies. We can notice that, although it primarily deals with banking and financial services, the extant research has addressed the topic in a vast array of industries.

According to Bloomberg, the share of hedge funds that use AI decreased by 7.3% in March 2018. AI creates numerous opportunities in the finance sector by optimizing processes and uncovering new revenue streams. This is a pivotal advancement in user experience and operational resilience in the financial sector. The benefits of AI, from precise decision-making to pattern detection, position it as a catalyst for innovation. For example, the chatbot “KAI” from Mastercard uses ML algorithms and NLP, offering consumers tailored help and financial insights across numerous channels, including WhatsApp, Messenger, and SMS.

The company aims to serve non-prime consumers and small businesses and help solve real-life problems, like emergency costs and bank loans for small businesses, without putting either the lender or recipient in an unmanageable situation. Artificial intelligence (AI) technologies are rapidly transforming today’s business models, and the emerging Generative AI and advanced applications are presenting new opportunities and possibilities for AI in finance and accounting. From Generative AI to machine learning and other foundation model solutions, we look at the new era of AI innovations, the tools they may offer accounting and finance, and considerations for incorporating an AI framework for success.

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. ShapeShift has also introduced the FOX Token, a new cryptocurrency that features several variable rewards for users. Morgan Chase found that 89 percent of respondents use mobile apps for banking. Additionally, 41 percent said they wanted more personalized banking experiences and information.

AI can fully automate loan processing, eliminating administrative overhead and enabling faster disbursements. Bring your expenses, supplier invoices, and corporate card payments into one fully integrated platform, powered by AI technology. While this may seem like an area where machines shouldn’t be involved, the advantages of artificial intelligence applications are significant. Finance AI technology can be used to automate approval flows for both expenses and invoices, based on pre-set rules, such as suppliers, categories, or spending limits.

JP Morgan utilizes AI for risk management, fraud detection, investment predictions, and optimizing trading strategies by analyzing vast amounts of financial data. This includes predicting stock market movements, customer creditworthiness, and potential fraudulent transactions. ML is pivotal in enhancing the accuracy and efficiency of financial services. RegTech, a rapidly growing field, uses AI and other technologies to automate compliance processes for banks and financial services, which face ever-changing and complex regulatory requirements. Another interesting application of finance AI is customer service, where the adoption of chatbots is on the rise.

This capability is pivotal in areas like investment management, where AI algorithms predict market trends and asset performance, helping institutions and investors make informed decisions. AI enhances the precision of financial decisions by analyzing vast datasets beyond human capability. It excels in uncovering patterns and insights from complex, voluminous data, enabling more accurate financial ai in finance examples predictions and strategies. AI is being leveraged in various facets of the financial industry to streamline operations and enhance user experiences. It aids in personalizing financial advice, managing assets, automating manual processes, and securing sensitive financial information against fraud. AI is rapidly transforming the way finance professionals approach their daily work.

Connect with reliable AI services to prioritize AI goals and implement them strategically to push the boundaries with what’s feasible. The finance solution powered by Gen AI stays abreast with evolving finance trends and technological advancements and is continuously monitored. It enables tracking solution performance that determines which improvements increase the solution’s effectiveness. To learn how Tipalti’s innovative technologies are helping your company strategically leverage its finance data and achieve cost reductions in spending, access our latest eBook.