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

As more financial institutions recognize the value of integrating generative AI into their operations, we can expect to see a growing number of innovative applications and use cases emerging in the near future. Artificial intelligence (AI) is transforming the financial services industry, making it faster, more efficient, and more personalized than ever before. From fraud detection to chatbots to investment advice, AI is being used in a variety of ways to improve the financial services experience for both businesses and consumers.

Underwrite.ai uses AI models to analyze thousands of financial attributes from credit bureau sources to assess credit risk for consumer and small business loan applicants. 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. Ocrolus offers document processing software that combines machine learning with human verification.

Plaid works as a widget that connects a bank with the client’s app to ensure secure financial transactions. AI-powered solutions can streamline these processes, optimize logistics, and mitigate risks. By analyzing Chat GPT historical data and real-time information, AI can predict delays, automate paperwork, and even provide financing options for trade transactions, enabling smoother and more efficient global trade.

Thus, embracing AI technologies can give financial institutions a competitive edge in today’s rapidly evolving digital landscape. Besides that, AI can assist with long-term financial planning based on personal financial histories and see how particular financial decisions may affect goals in the future. In this case, artificial intelligence (AI) takes on the role of managing the many consumer sentiments through the review of a product.

The integration of generative AI in finance promises to elevate the quality of trading decisions, refining both trading strategies and investment portfolios. An AI-based platform provides lending companies with a quick and accurate assessment of a potential borrower by considering several factors, such as their credit score and history. In other words, lenders can make better-informed, data-backed decisions to identify credit-worthy applicants and accelerate the overall credit risk evaluation process. The Appian AI Process Platform includes everything you need to design, automate, and optimize even the most complex processes, from start to finish. The world’s most innovative organizations trust Appian to improve their workflows, unify data, and optimize operations—resulting in better growth and superior customer experiences. Careful design, diligent auditing and testing of ML models can further assist in avoiding potential biases.

Fintech companies use computer vision to extract and verify information from documents like IDs, passports, and financial statements. This technology streamlines customer onboarding, reduces the risk of identity fraud, and ensures compliance with regulatory requirements. Additionally, NLP-driven chatbots enhance customer service by understanding and responding to customer inquiries and complaints, providing a more personalized and efficient experience. Financial institutions operate under regulations that require them to issue explanations for their credit-issuing decisions to potential customers. This makes it difficult to implement tools built around deep learning neural networks, which operate by teasing out subtle correlations between thousands of variables that are typically incomprehensible to the human brain.

It has also been employed for sentiment analysis tasks, such as analyzing financial news sentiment to generate responses and accurately predict sentiment categories based on those responses. Additionally, generative AI can enable banks to take a more detailed approach when providing portfolio strategies to customers. AI-driven data science can enhance decision-making in real-time, while automation provides cost savings and faster transactions. By deploying accurate algorithms and predictive models with new technologies in software, financial institutions and businesses can automate their operations and gain valuable insights into customer behavior. By utilizing machine learning algorithms and predictive analytics, the use of AI in financial services enables the analysis of vast amounts of data to identify and prevent fraud in real time.

Example 5. JPMorgan Chase’s COiN Platform (this time I’m going over their COiN platform).

This development is a big step in AI for market intelligence promising more efficiency and accuracy in research. While recognizing AI’s potential benefits, financial regulators are also keenly aware of its risks and challenges. https://chat.openai.com/ For example, the Federal Reserve Bank of Richmond is taking a cautious and deliberate approach to understanding AI’s implications, particularly for its supervision, financial stability, and monetary policy responsibilities.

Systems with artificial intelligence (AI) can analyze data from hundreds of sources and predict what will work and what won’t. AI can also conduct in-depth customer data analyses and make predictions about consumer preferences, product development, and distribution methods. Around 48% of companies use AI in fintech to address data quality challenges and enhance analytics, based on the O’Reilly report. The benefits of AI in fintech are numerous, and it’s no wonder that many financial institutions are turning to this technology. “What I’m saying is that companies with well-structured, good data have already been able to put AI to good use in detecting fraud,” she said. As companies improve their data collection and algorithms become more advanced, the benefit to financial firms is growing.

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. With rising interest rates, the banking crisis, and increasing pressure on borrowers, shares of Upstart have come crashing down as its growth has stalled. But that’s no reason to doubt the underlying AI technology behind this business, as AI and machine-learning algorithms are designed to make inferences and judgments using large amounts of data. Wealthblock.AI is a SaaS platform that streamlines the process of finding investors.

Generative AI applications need access to huge amounts of reliable training data for scaling up operations. Inadequate data can lead to biased or inaccurate results, which could have serious consequences for financial institutions and their customers. Generative AI-generated synthetic data offers a diverse and representative dataset of various borrower characteristics and risk factors, enabling more accurate and robust machine learning models for loan underwriting purposes. By automating document verification and risk assessment processes in loan underwriting, generative AI not only improves the precision of decisions but also reduces the time and effort required for manual review.

In addition to enhancing customer service, PKO Bank Polski has also implemented AI solutions to automate and optimize internal processes, such as loan underwriting and mortgage approval, risk assessment, and CRM. These AI solutions demonstrate the potential of generative AI to transform the finance and banking industry, driving customer satisfaction and operational efficiency. Even the popular ChatGPT, a natural language processing (NLP) based AI technology that can analyze unstructured data, is a prime example of the future of finance and the use of generative AI in finance. This technology offers conversation-based automated customer service and even generates financial advice. 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.

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. The resulting algorithmic trading processes automate ai in finance examples trades and save valuable time. 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.

Let’s explore a few use cases and success stories before delving into actionable mitigation strategies inspired by these illustrations. 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. Ultimately, the goal should be to harness AI to enhance human decision-making rather than replace it entirely.

By automating routine tasks, financial institutions can streamline operations, reduce costs, and enhance accuracy. Moreover, employees can focus on higher-value activities like financial analysis and decision-making, leading to improved strategic outcomes. The integration of AI and ML in finance is enabling algorithmic trading systems to continuously learn and adapt to market conditions.

By integrating AI into customer service, customer requests are addressed faster, the workload of call center workers would be reduced, and they can focus on more complex customer requests. Complying with regulatory requirements is essential for banks and other financial institutions. AI can leverage Natural Language Processing (NLP) technologies to scan legal and regulatory documents for compliance issues. As a result, it is a scalable and cost-effective solution because AI can browse thousands of documents rapidly to check non-compliant issues without any manual intervention. Implementing robust data encryption techniques for enhanced privacy, developing explainable AI models for better interpretability, and offering comprehensive training programs to bridge talent gaps are potential solutions to these challenges. Additionally, Generative AI assists in generating synthetic financial data for training predictive models, optimizing portfolio management, and streamlining financial document processing.

According to a survey conducted by American Express, more than 60% of customers prefer self-service facilities. If an individual seeks a personalized retirement plan, they can avoid the inconvenience of waiting in long bank queues for assistance from a single employee by utilizing conversational AI. Moreover, customers can even go for digital ID verification with a conversational AI.

Automation in Accounting and Bookkeeping

It will also assist them in setting up automatic transfers to their investment account. For example, a user can transfer money to a shopkeeper by using their conversational AI within the banking application in voice format. Conversational AI integrates with a customer’s device geolocation to detect the location.

Artificial intelligence encourages more informed decision-making, future-proofing the business for global shifts, the discovery of untapped opportunities, and ultimately, greater profitability for both the financial institution and its clients. Such innovations significantly improve client satisfaction through curated advice and proactive assistance. Ultimately, financial settings gain a competitive edge by offering a superior, personalized CX. The need to handle redundant and time-consuming duties, such as manually entering data, and summarizing lengthy papers.

Keep reading and learn how AI will help fintech companies and how AI is already changing the fintech industry. Better chatbot experiences have resulted from machine learning in finance, which has enhanced client satisfaction. ML-based chatbots can answer client questions with speed and accuracy because they have powerful natural language processing engines and the capacity to learn from previous interactions.

Anomaly detection algorithms are a prime example of AI for finance in fraud detection. They can identify unusual patterns and deviations from normal behavior, raising alerts for further investigation. For instance, if a customer suddenly conducts multiple high-value transactions from an unfamiliar location, the AI system can promptly flag it as a potential fraud case. EY writes that ultimately, finance teams need to see AI as a collaboration where AI can do the repetitive work and finance teams can do the strategic work. “While AI can process vast amounts of data at a rapid pace, it lacks the critical thinking and decision-making capabilities of people.

Risks of market manipulation or tacit collusions are also present in non-explainable AI models. Synthetic datasets can also allow financial firms to secure non-disclosive computation to protect consumer privacy, another of the important challenges of data use in AI, by creating anonymous datasets that comply with privacy requirements. Traditional data anonymisation approaches do not provide rigorous privacy guarantees, as ML models have the power to make inferences in big datasets. The use of big data by AI-powered models could expand the universe of data that is considered sensitive, as such models can become highly proficient in identifying users individually (US Treasury, 2018[32]).

• AI innovations like machine learning may enhance loan underwriting and lower financial risk for businesses wanting to grow their value.
• There are a variety of frameworks and use cases for AI technologies in the finance industry.
• Importantly, the lack of explainability makes discrimination in credit allocation even harder to find (Brookings, 2020[20]).
• This would naturally increase customer retention and satisfaction by instilling trust through a secure and seamless authentication process.

TQ Tezos aims to ensure that organizations have the tools they need to bring ideas to life across industries like fintech, healthcare and more. Time is money in the finance world, but risk can be deadly if not given the proper attention. According to Forbes, 70% of financial firms are using machine learning to predict cash flow events, adjust credit scores and detect fraud. Check customer preferences and demand to bring updates and changes to the existing system. These insights will help to fine-tune the conversational AI and enhance overall customer satisfaction. Moreover, it helps around 32 million customers daily with their day-to-day financial needs and queries.

NLP algorithms can analyze vast amounts of textual data, including news articles, social media posts, and customer feedback, to gauge market sentiment and make informed investment decisions. Fintech firms use NLP-powered tools to track news and social media around financial assets, helping traders and investors react swiftly to market trends and news events. Big-data-enhanced fraud prevention has already made a significant impact on credit card processes, as noted above, and in areas such as loan underwriting, as discussed below. By looking at customer behaviors and patterns instead of specific rules, AI-based systems help banks practice proactive regulatory compliance, while minimizing overall risk. For their operations to succeed, large firms and financial institutions rely on precise market forecasts.

While AI and automation can be the industry’s most significant assets, with the potential to increase efficiency and accuracy, there are concerns about unfair or exploitative practices. For example, Wealthfront’s AI-driven investing platform considers the customer’s risk tolerance, goals, and preferences to create an optimized portfolio. Finance Artificial Intelligence (AI) is a broad term that refers to any system or machine capable of completing tasks via finance automation and algorithms, without human intervention.

Financial institutions need robust defenses, with cyberattacks becoming increasingly sophisticated. AI-powered RegTech solutions can help identify suspicious activities, comply with regulations, and prevent financial crime. The banking, retail, and healthcare sectors have made the biggest investments in AI technology development. 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.

Lloyds Banking Group’s AI for Fraud Prevention

Banks can leverage this synthetic data by training machine learning models to make pivotal decisions, such as determining credit eligibility. You can foun additiona information about ai customer service and artificial intelligence and NLP. An IBM study highlighted that AI-driven financial forecasting reduced forecast errors by over 20% for many companies. By analyzing historical data, it identifies patterns, enabling banks to simulate diverse financial visualizations and strategize accordingly.

Section three offers policy implications from the increased deployment of AI in finance, and policy considerations that support the use of AI in finance while addressing emerging risks. It provides policy recommendations that can assist policy makers in supporting AI innovation in finance, while sharpening their existing arsenal of defences against risks emerging from, or exacerbated by, the use of AI. Financial institutions that embrace AI technologies stand to gain a significant competitive advantage in terms of enhanced efficiency, security, and customer satisfaction.

They can employ well-known methods like Principal Components Analysis (PCA) and Linear Discriminant Analysis for the latter (LDA). There must be a mechanism to instantly locate anomalies throughout the entire pipeline, pinpoint the problem, and resolve it. That’s exactly why some businesses are built around this idea and offer git-like version control for even their own data. In practice this means that several isolated instances of incorrect or biased data supplied into a trading algorithm can have catastrophic effects on the entire system and result in losing trades and money.

Some also use voice-controlled virtual assistants to provide better customer service. AI-based applications employ algorithms that track customers’ regular expenses, income, and purchasing habits to offer personalized financial advice based on the user’s financial goals. Artificial intelligence (AI) is revolutionizing the finance sector, emerging as a vital tool for industry leaders to rapidly enhance services and expand their customer base.

According to Deloitte, the world’s top 14 investment banks could potentially boost their front-office productivity by 27% to 35% by leveraging generative AI. McKinsey & Company predicts that AI could boost the value of the banking industry by an additional $200 billion to $340 billion annually. That might explain why IDC projects banking will be one of the two industries spending the most on AI solutions in 2024.

OCR was created by MIT researchers to quickly and accurately read and match the handwritten portions of checks, and effectively changned the perception of using AI in the banking industry. OCR can automatically recognize and extract data from scanned documents and images in a structured way and helps in reducing processing times for each document. Leading lenders, like Ally, are also automating the process of approving the loan and predicting the maximum amount a customer may borrow and the pricing of the loan using AI and ML models. For example, when bank employees give biased advice based on AI recommendations, the entire institution may start systematizing bias into the decision-making process. Unfortunately, these benefits of AI in finance and accounting do not come without risks.

Automating financial processes relies on artificial intelligence’s ability to gain insights from existing data to optimize credit decisions, risk assessment, and auditing, among others. Thanks to the development in natural language processing (NLP), AI systems swiftly determine a customer’s disposable income and ability to make timely loan payments. For example, by using Optical Character Recognition (OCR), AI can extract and process data from bank accounts, tax returns, or utility invoices. The introduction of chatbots and virtual assistants—byproducts of the AI revolution in the finance industry—has minimized wait times and sped up customer service. Customers can easily check their account balance, plan monthly payments, or review their bank account activity.

Analysis of AI transcripts can predict a customer’s mood and direct agents as to what solutions to offer them. There are hundreds, if not millions, of customers at huge financial organizations. Catering to so many different individuals without AI-powered technology would be a huge undertaking. Personalization may boost brand loyalty and customer confidence in your business.

The Financial Services Industry has entered the Artificial Intelligence (AI) phase of the digital marathon, a journey that started with the advent of the internet and has taken organisations through several stages of digitalisation. The emergence of AI is disrupting the physics of the industry, weakening the bonds that have held together the components of the traditional financial institutions and opening the door to more innovations and new operating models. Financial institutions can improve the efficacy and accuracy of their compliance testing and regulatory reporting with AI-generated synthetic data. Generative AI has revolutionised how banks approach testing and reporting, giving them more flexibility, reliability and trustworthiness.

The deployment of AI techniques in finance can generate efficiencies by reducing friction costs (e.g. commissions and fees related to transaction execution) and improving productivity levels, which in turn leads to higher profitability. In particular, the use of automation and technology-enabled cost reduction allows for capacity reallocation, spending effectiveness and improved transparency in decision-making. AI applications for financial service provision can also enhance the quality of services and products offered to financial consumers, increase the tailoring and personalisation of such products and diversify the product offering. AI-driven tools like chatbots and automated advisory services provide instant responses to customer inquiries, facilitating uninterrupted banking and financial advice. This shift not only reduces the chances of human error but also speeds up the processing of financial transactions and decisions. Automation in financial services includes applications such as data entry, analysis, and report generation, as well as more advanced functions like real-time fraud detection and risk assessment.

According to a report from Mordor Intelligence, artificial intelligence (AI) in finance is expected to register a compound annual growth rate (CAGR) of over 25% between 2022 and 2027. Unlike a human being, a machine is not likely to be biased what is quite important especially in financial app development. Things that can be predicted with AI include market movements, market behavior patterns, interest rates, and currency movements. An example of AI implementation in financial management can be seen in the OCR product from Fintelite which you can try for free.

By analyzing intricate patterns in transaction data sets, AI solutions allow financial organizations to improve risk management, which includes security, fraud, anti-money laundering (AML), know your customer (KYC) and compliance initiatives. AI is also changing the way financial organizations engage with customers, predicting their behavior and understanding their purchase preferences. This enables more personalized interactions, faster and more accurate customer support, credit scoring refinements and innovative products and 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. It enables financial institutions to streamline operations, make data-driven decisions, improve efficiency, and deliver better services to customers. Bank executives and leaders at financial services firms understand that innovation is the key to enhancing resilience and addressing new challenges in risk management.

This AI-powered platform is designed to process a vast array of data points in real-time, enabling the firm to execute trades with greater efficiency and precision. These platforms use AI algorithms to automate investment recommendations and portfolio management, making investment advice more accessible and personalized. Feedzai’s platform conducts large-scale analyses to identify and alert customers about fraudulent or dubious activities, thus safeguarding trillions of dollars in daily operations. These models are particularly useful in customer interaction, where they can provide quick, accurate responses to customer inquiries, improving the overall customer experience. By leveraging AI in finance, financial organizations are automating their operations and reaping the benefits of this technology. The forecasting and management of bad debt is a critical aspect of financial services, and the use of AI in finance is revolutionizing this aspect of financial management.

That review will identify and analyze the product to determine whether it is acceptable in a positive or negative sense. An AI algorithm will read the historical data to generate an accurate credit score so that credit-granting decisions are more precise. Because of AI, new services have been created, such as virtual assistance and chatbots, which can respond to customer complaints quickly and accurately. Before the advent of AI, we could only visit branch offices to express complaints and problems with banking services. AI’s potential to revolutionize how businesses manage their finances has become increasingly evident as organizations adopt it more significantly.