Using AI in Finance: 4 Examples and Use Cases

For example, a company can offer car insurance to its customer who is in the process of buying car. Oliver Wyman shares that using AI insights can increase annual income from email cross-sell by four times. However, there is still a long way for AI models to be widely used in financial services.

You can foun additiona information about ai customer service and artificial intelligence and NLP. These AI accounting solutions aim to reduce manual errors, enhance compliance, and streamline financial processes. JPMorgan Chase employs artificial intelligence to bolster its fraud detection capabilities in credit card transactions. Generative AI for finance, along with ML in finance, is transforming the forecasting and management of bad debt. By leveraging AI’s analytical capabilities and automation, financial institutions can make more accurate predictions, devise effective strategies, and improve debt collection outcomes, enhancing their overall financial health. The bank has created a proprietary algorithm that examines each credit card transaction’s specifics in real-time in order to spot fraud patterns.

“Those straightforward queries can take up as much as 80% of the load in inbound questions from customers,” she said. Even a few decades ago, the world of finance was very different from the one we live in today. The increase in the number of transactions is related to the fact that the number of transactions has increased. Currently, only a quarter of consumer payments are performed in cash; most transactions are now computerised. When Excel was invented, many finance professionals were worried it would take their job. While many bookkeepers were replaced in the short term, in reality it allowed finance people to do more strategic work and they became far more valuable.

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. The role of AI in finance is nowadays becoming more prominent in the arena of generating financial reports. AI-powered systems can analyze vast amounts of financial data, including transactions, invoices, and account statements, to automate the report generation process.

Top 12 Use Cases & Examples of Conversational AI in Banking and Finance in 2024

Thanks to AI, finance professionals will be able to focus more on data driven and strategic decision making activities and less on repetitive and manual work in 2024. These eight finance tools are great examples of how AI is improving all aspects of finance. No matter what the industry is or the size of the business, there is some way that AI tools can improve the finance department in your company. A. Generative AI will transform financial services through fraud detection, conversational finance, financial forecasting, data privacy, risk management, application modernization, and more. AI algorithms, by generating synthetic data, can adeptly model market dynamics, curate innovative trading strategies, and enhance portfolio management.

Read about the transformative use cases of Generative AI in financial services and banking sectors that are benefiting businesses globally. AI enables financial services firms to analyze and detect irregular customer behaviors, locations, and spending habits in real-time. It can recognize suspicious or anomalous activity and trigger a security mechanism to reveal and prevent fraud. The opacity of algorithm-based systems could be addressed through transparency requirements, ensuring that clear information is provided as to the AI system’s capabilities and limitations (European Commission, 2020[43]).

For decades, financial services companies have relied on traditional, rule-based transaction monitoring and name screening systems, which are often prone to errors and false positives. Financial crimes have since become more prevalent and fraud patterns are continuously changing, making fraud prevention more complex than ever. Currently, financial market participants rely on existing governance and oversight arrangements for the use of AI techniques, as AI-based algorithms are not considered to be fundamentally different from conventional ones (IOSCO, 2020[39]). Model governance best practices have been adopted by financial firms since the emergence of traditional statistical models for credit and other consumer finance decisions.

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. One of the most significant business  cases for AI in finance is its ability to prevent fraud and cyberattacks. Consumers look for banks and other financial services that provide secure accounts, especially with online payment fraud losses expected to jump to $48 billion per year by 2023, according to Insider Intelligence.

But with AI models as part of the governance process, the task can be completed in a fraction of the time, by machines.” 

It’s also important to remember that AI learns based on whatever data it receives. With that in mind, it’s important that finance teams control the data machine learning processes ingest to ensure the data is relevant and to avoid introducing biases into its analysis. AI algorithms can analyze social media chatter, news articles, and financial reports to extract nuanced insights and predict market trends, guiding investment decisions and risk management strategies. AI algorithms can analyze vast amounts of financial data, news, and research reports, identifying promising investment opportunities and optimizing portfolios in real time. AI assistants, such as chatbots, use AI to generate personalized financial advice and natural language processing to provide instant, self-help customer service.

Conversational AI in banking not only analyses users’ financial and banking data but also comes up with customized suggestions and product recommendations. Conversational AI technology will replace the number of employees onboarding new customers. It will streamline these processes from account opening to KYC (Know Your Customer) submission and verification. Conversational AI is way more intelligent than the traditional chatbots used by brands.

AI applications in the fintech industry range from recognizing abnormal transactions to identifying suspicious and potentially fraudulent activities by analyzing massive amounts of data. AI can quickly gain insights that help protect organizations against losses and increase ROI for their customers. AI-driven data science can enhance decision-making in real-time, while automation provides cost savings and faster transactions that benefit both customers and credit card companies alike. In this post, we’ll delve into the transformative power of generative AI use cases in finance and banking. As adoption increases, the future of AI in finance includes fraud detection, customer service automation, and improved credit scoring for making better credit decisions. Regulatory compliance is another area where AI technologies make a big difference in finance.

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As we can see, the benefits of AI in financial services are multiple and hard to ignore. According to Forbes, 65% of senior financial management expects positive changes from the use of AI in financial services. A leading financial firm, JP Morgan Chase, has been successfully leveraging Robotic Process Automation (RPA) for a while now to perform tasks such as extracting data, comply with Know Your Customer regulations, and capture documents. RPA is one of ‘five emerging technologies‘ JP Morgan Chase uses to enhance the cash management process.

Most importantly, it automates customer service, thus letting banks handle the bulk of queries efficiently. With artificial intelligence already making considerable strides in customer support for banks and fintech businesses, customers are growing accustomed to receiving prompt replies at any time of day. To facilitate transactions and answer questions, financial institutions must be accessible around-the-clock, every day of the week. Hummingbird proudly identifies itself as a leading RegTech solution, offering a dedicated CRM platform meticulously crafted for compliance and risk teams.

The following companies are just a few examples of how AI-infused technology is helping financial institutions make better trades. 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. Socure is used by institutions like Capital One, Chime and Wells Fargo, according to its website.

Machine learning (ML) is a subset of AI that allows machines to find patterns in data by using various methods, such as deep learning. By using a proprietary algorithm, the bank can swiftly identify and prevent fraudulent activities, safeguarding both its operations and its customers. This AI in Finance examples uses smart technology to enhance the user experience and provide valuable insights to its users, making trading more accessible and informed. This chatbot, powered by machine learning, enables the bank to engage with its customers more efficiently, providing quick responses to queries and enhancing the overall customer experience.

Smarter Credit Decisions

Importantly, the lack of explainability makes discrimination in credit allocation even harder to find (Brookings, 2020[20]). The possible simultaneous execution of large sales or purchases by traders using the similar AI-based models could give rise to new sources of vulnerabilities (FSB, 2017[11]). Indeed, some algo-HFT strategies appear to have contributed to extreme market volatility, reduced liquidity and exacerbated flash crashes that have occurred with growing frequency over the past several years (OECD, 2019[12]) . In the absence of market makers willing to act as shock-absorbers by taking on the opposite side of transactions, such herding behaviour may lead to bouts of illiquidity, particularly in times of stress when liquidity is most important.

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. Artificial intelligence has streamlined programs and procedures, automated routine tasks, improved the customer service experience and helped businesses with their bottom line. In fact, Business Insider predicts that artificial intelligence applications will save banks and financial institutions $447 billion by 2023. This technology has not only simplified customer service but also bolstered security through voice biometrics, enabling secure and convenient user authentication.

One of the main challenges of AI in financial services is the amount of data collected from the customers, which contains sensitive and confidential information like transaction history, account information, or loan details. Thanks to their fraud detection capabilities, AI-based systems help consumers minimize the risk and save money from fraudulent activities. Moreover, AI can now analyze user activities and data collected by other non-banking apps and offer customized financial advice. In fact, such banks as DBS or Royal Bank of Canada (RBC) have already embraced such AI-based tools. By deploying accurate algorithms and predictive models, financial institutions can automate their operations and gain valuable insights into customer behavior.

According to a McKinsey global survey, about 60% of companies use AI in at least one business function (source ). However, as many will attest, these credit reporting systems are far from perfect and are often riddled with errors, missing real-world transaction history and misclassifying creditors. What follows is a list of the top benefits of AI in banking and finance today and a discussion of some of the risks and challenges financial services companies face when using AI. Latest developments in deep learning have increased the accuracy of picture identification beyond what is humanly possible.

Besides detecting risks, it helps customers resolve such situations with step-by-step guidance. This AI even lets users complete petty tasks such as checking account balances without having to deal with bank employees physically or on call. From creating a new bank account to applying for loans, it can automate a variety of tasks. Artificial intelligence offers the financial sector a special chance to save costs, enhance client satisfaction, and boost operational effectiveness, among other things. Financial institutions may provide their clients with top-notch financial services outside their branch offices. By providing tailored insights, preventing money laundering, and conducting credit underwriting in the back office, AI helps banks save money in all three areas of their operations.

In a hypothetical scenario, the use of AI could further increase disintermediation by bringing AI inference directly on-chain, which would render Oracles redundant. In theory, it could act as a safeguard by testing the veracity of the data provided by the Oracles and prevent Oracle manipulation. Nevertheless, the introduction of AI in DLT-based networks does not necessarily resolve the ‘garbage in, garbage out’ conundrum as the problem of poor quality or inadequate data inputs is a challenge observed equally in AI-based applications. In the future, the use of DLTs in AI mechanisms is expected to allow users of such systems to monetise their data used by AI-driven systems through the use of Internet of Things (IoT) applications, for instance.

Delivering a context-based customer experience is no longer a nice-to-have option. It’s a must-have that all institutions need to deliver in the increasingly competitive world of banking and finance. Machine learning (ML) is a subset of AI that allows machines to find patterns in data by using various methods, such as deep learning and natural language processing (NLP). Companies are leveraging these powerful tools to revolutionize how they manage their services, from forecasting market trends to deploying chatbots for customer support.

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AI-enabled applications are transforming the insurance industry by improving the accuracy and efficiency of claims. The insurance claim management process frequently employs an entirely data-driven approach wherein AI analyzes all required documents to process and automate claims. Using AI to automate claims processing also helps insurers identify fraudulent claims and offer digital services to improve customer experience.

This is because Domo advertises the software as a connector, not a data generator. Escalon has helped over 5,000 companies across various industries improve their compliance regarding internal controls and streamline processes. Like the efficiencies AI creates throughout the customer experience, it also has the ability to improve productivity for internal teams with document and query management. AI can be used to summarize documents, help craft legal agreements, extract information from research to assist research analysts, and gather details for RFPs, due diligence questionnaires, and more. Tail and unforeseen events, such as the recent pandemic, give rise to discontinuity in the datasets, which in turn creates model drift that undermine the models’ predictive capacity. These are naturally not captured by the initial dataset on which the model was trained and are likely to result in performance degradation.

AI has the ability to analyze and single-out irregularities in patterns that would otherwise go unnoticed by humans. Financial crime is a global threat, and AI is playing a crucial role in fighting it. AI-powered anti-money laundering (AML) solutions can analyze transaction data, identify suspicious activities, and predict fraudulent behavior. This helps financial institutions comply with AML regulations, protect their customers, and safeguard the integrity of the financial system. AI algorithms can analyze market data in real time, identify emerging risks, and trigger automated responses to mitigate losses and protect investments.

Companies can leverage the power of AI in financial services by utilizing machine learning algorithms that can extract relevant information, perform data validation, and generate comprehensive and error-free financial reports. AI swiftly processes vast amounts of data, uncovering patterns and relationships that can often elude human analysis. This capability facilitates quicker insights crucial for decision-making, trading, risk assessment, compliance, and various financial operations, ultimately enhancing efficiency and agility within the industry. AI’s speed enables real-time adjustments to market conditions and enhances responsiveness to dynamic financial landscapes, empowering institutions to stay ahead of the curve and capitalize on emerging opportunities with agility and precision.

In addition to chatbots, banks use AI to help recommend products for customers and manage money. Banks use AI for customer service in a wide range of activities, including receiving queries through a chatbot or a voice recognition application. These algorithms can suggest risk rules for banks to help block nefarious activity like suspicious logins, identity theft attempts, and fraudulent transactions.

Depending on how they are used, AI algorithms have the potential to help avoid discrimination based on human interactions, or intensify biases, unfair treatment and discrimination in financial services. The risk of unintended bias and discrimination of parts of the population is very much linked to the misuse of data and to the use of inappropriate data by ML model (e.g. in credit underwriting, see Section 1.2.3). Smart contracts rely on simple software code and have existed long before the advent of AI. Currently, most smart contracts used in a material way do not have ties to AI techniques.

When the time to perform routine tasks is reduced, finance teams have extra time for strategic finance initiatives to increase profitability through recommended growth in revenues and cost reductions. As AI technologies become more prevalent in the finance industry, it’s crucial to consider the ethical implications of these tools. The use of AI technologies in finance is multiplying, with startups leading the charge on digital transformation within this sector. Data scientists play an essential role in developing and implementing AI models for finance, as they are responsible for creating datasets that will train the models. Before we dive into the world of AI applications in finance, it is essential to understand the core concepts and principles that drive this technology.

Finance providers need to have the skills necessary to audit and perform due diligence over the services provided by third parties. Over-reliance on outsourcing may also give rise to increased risk of disruption of service with potential systemic impact in the markets. Similar to other types of models, contingency and security plans need to be in place, as needed (in particular related to whether the model is critical or not), to allow business to function as usual if any vulnerability materialises. Documentation of the logic behind the algorithm, to the extent feasible, is being used by some regulators as a way to ensure that the outcomes produced by the model are explainable, traceable and repeatable (FSRA, 2019[46]). Access to customer data by firms that fall outside the regulatory perimeter, such as BigTech, raises risks of concentrations and dependencies on a few large players. Unequal access to data and potential dominance in the sourcing of big data by few big BigTech in particular, could reduce the capacity of smaller players to compete in the market for AI-based products/services.

• A. AI is used in finance to automate routine tasks, analyze data for insights, improve fraud detection, optimize investment strategies, personalize customer experiences, and enhance risk assessment and management.
• Let’s have a look at the potential challenges and solutions of AI integration in FinTech.
• Acting promptly and decisively in embracing these technologies is essential for banking leaders to stay ahead in a rapidly evolving landscape.
• How to use AI responsibly is a topic of concern for companies, governments and other entities worldwide.

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. Figure Marketplace uses blockchain to host a platform for investors, startups and private companies to raise capital, manage equity and trade shares. Trim is a money-saving assistant that connects to user Chat GPT accounts and analyzes spending. The smart app can cancel money-wasting subscriptions, find better options for services like insurance, and even negotiate bills. Trim has saved more than $20 million for its users, according to a 2021 Finance Buzz article. Alpaca uses proprietary deep learning technology and high-speed data storage to support its yield farming platform.

We’ll discuss its applications in detecting anomalies, transaction processing, and leveraging data science for better insights and risk assessment to aid decision-making. KAI is an AI in Finance examples that, using machine learning algorithms and natural language processing, assists customers with inquiries, enhancing the user experience. This platform analyzes financial data to identify risks and opportunities, aiding in investment decision-making and risk management.

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. Not only has it provided better methods to handle data and improve customer experience, but it has also simplified, sped up, and redefined traditional processes to make them more efficient. Despite its immense potential for revolutionizing the finance and banking sectors, generative AI does come with its own set of challenges and limitations.

AI-powered fraud detection systems can analyze transaction patterns, identify anomalies, and predict fraudulent behavior in real time, stopping fraudsters before they can strike. AI-powered predictive analytics can analyze market trends, economic indicators, and social sentiment to identify risks and predict future performance, helping financial institutions manage risk and make informed investment decisions. The considerable interest in passive investment makes fintech companies invest in AI solutions. Robo-advisory is based on providing recommendations based on investors’ individual goals and risk preferences. Finance AI automates the investment process so that the only thing investors need to do is deposit money into an account. The most significant benefit of using this tool is offering the ability for people not familiar with finance to make investments.

Besides speeding up the procedure for approval and onboarding, it also makes documentation easy for new onboarding customers. Besides, the bot can even update customers on their application and approval status anytime on customer service requests. Not only this, it answers user’s queries more like a human and less like a chatbot. We can integrate data-driven choices into your business plan, whether made throughout the full value chain or simply in one section.

Chatbots offer 24/7 assistance by quickly and effectively responding to inquiries. AI can analyze data to provide customized financial advice and suggestions based on customers’ interests and habits, thereby improving customer experience. The decision for financial institutions (FIs) to adopt AI will be accelerated by technological advancement, increased user acceptance, and shifting regulatory frameworks. Banks using AI can streamline tedious processes and vastly improve the customer experience by offering 24/7 access to their accounts and financial advice services. AI-powered wealth management platforms are democratizing access to sophisticated investment strategies and personalized financial advice, even for small investors. These platforms can analyze individual financial goals, risk tolerance, and market conditions to create custom portfolios and generate investment recommendations, making wealth management more accessible and effective.

This capability saves time for financial analysts and improves decision-making by providing comprehensive insights. In the finance sector, Generative AI has become a tool that financial institutions cannot afford to overlook. It transforms operations and decision-making processes with unmatched capabilities.

Kavout uses machine learning and quantitative analysis to process huge sets of unstructured data and identify real-time patterns in financial markets. The K Score analyzes massive amounts of data, such as SEC filings and price patterns, then condenses the information into a numerical rank for stocks. Advanced sentiment analysis, which focuses on assessing the client’s experience, identifying gaps, and training chatbots to close those gaps, is one way AI is assisting in improving fintech customer service. AI-based solutions make communicating with the finance industry simpler and more convenient for clients. More contented clients and customer service staff translate into a more successful business.

In addition to the inherent complexity of AI-based models, market participants may intentionally conceal the mechanics of their AI models to protect their intellectual property, further obscuring the techniques. The gap in technical literacy of most end-user consumers, coupled with the mismatch between the complexity characterising AI models and the demands of human-scale reasoning further aggravates the problem (Burrell, 2016[37]). The human parameter is critical both at the data input stage and at the query input stage and a degree of scepticism in the evaluation of the model results can be critical in minimising the risks of biased model decision-making.

As finance professionals know, management loves asking “what if” and scenario questions, and FP&A Genius allows them to be answered accurately and far quicker than ever before. We Empower businesses worldwide through strategic insights and innovative solutions. As hard as it may be to https://chat.openai.com/ believe, the next ten years in risk management may be subject to more transformation than the last decade.” — McKinsey & Co. Furthermore, generative AI in banking excels at automating the creation of comprehensive financial reports, including balance sheets and income statements.

Global financial institutions often need to design models across the multiple market areas they serve. The data must be consistent across different languages, cultures, and demographics to properly customize the customer experience. No wonder that artificial intelligence outperforms human intelligence in market pattern analysis, risk management, and general trading in the market with high volatility.

With ongoing advancements in AI capabilities, the financial services industry is poised to undergo a paradigm shift, revolutionizing how financial institutions operate, engage with customers, and deliver value in the digital age. The integration of AI in finance has transformed financial planning by leveraging data analytics and machine learning algorithms. For instance, AI-powered platforms can analyze historical financial data, market trends, and economic indicators to generate accurate and personalized financial forecasts. This feature of AI helps banks in wooing millennials, who form an important customer segment in most countries. This empowers individuals and businesses to make informed decisions and optimize their financial strategies. By deploying AI-powered chatbots and virtual assistants, banks and financial institutions can handle a large volume of customer queries efficiently and in real time.

Furthermore, the company also positions itself as a leader in the industry’s technological evolution. The Fed is exploring applications such as using AI and machine learning to detect anomalies in regulatory filings and automate data classification. The right data partner will provide a range of security options, strong data protection through certifications and regulations, and security standards to ensure the customer data is handled appropriately. While the latest state-of-art neural network architecture may be appealing and provide better accuracy, it’s rarely the best tool for the job due to its complex nature.

Moreover, it’s instrumental in compliance and fraud detection, as it can analyze voice patterns to identify suspicious activities in real time. KAI, a conversational AI platform used in the banking sector to enhance client experiences, was developed by Kasisto. By providing customers with self-service alternatives and solutions, KAI helps banks lower the traffic of contact centers. Additionally, AI-powered chatbots help customers make thoughtful financial decisions by offering sage advice.

AI-driven investment strategies are becoming increasingly popular as they enable financial advisors to tailor their advice based on a customer’s risk profile. The financial industry is rapidly evolving toward an algorithmic future, powered by artificial intelligence (AI), machine learning (ML), and other advanced technologies. The Aiden platform is an example of the practical application of generative AI in finance and banking, showcasing its ability to optimize trading execution quality for clients and adapt to fluctuating market conditions.

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. Generative ai in finance examples and their use in financial trading illustrate the innovative ways is being used to create new financial products and services. The role of AI in banking is also expanding, with applications ranging from fraud detection to personalized banking experiences.

According to Forbes, 70% of financial firms are using machine learning to predict cash flow events and adjust credit scores. The COVID-19 global crisis has accelerated and heightened the digitalization trend, including the application of AI in the finance industry. Learn how Tipalti’s innovative technologies are helping your company strategically leverage its finance data. This allows for a more proactive approach, where AI is used to prevent fraud before it happens as opposed to the traditional reactive approach to fraud detection. The above-mentioned factors are constantly evolving and bringing new values and opportunities to businesses, to effectively capitalise on the advantages offered by AI.