Accordingly, using the tools of bibliometric 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. Artificial intelligence in finance refers to the application of a set of technologies, particularly machine learning algorithms, in the finance industry. This fintech enables financial services organizations to improve the efficiency, accuracy and speed of such tasks as data analytics, forecasting, investment management, risk management, fraud detection, customer service and more. AI is modernizing the financial industry by automating traditionally manual banking processes, enabling a better understanding of financial markets and creating ways to engage customers that mimic human intelligence and interaction.
Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis
Additionally, FinChat.io delivers a wealth of information through features such as macroeconomic indicators, ETF holdings, superinvestor holdings, and what is balance sheet definition of balance sheet, balance sheet meaning an earnings calendar. For those interested in market forecasts, it provides analyst estimates, consensus ratings and price targets. With its screening tool, users can explore every public stock globally, to identify potential investment opportunities.
- The platform offers tailored solutions for different business sectors including finance, marketing, accounting, human resources, sales, IT, and operations.
- More lately, emerging countries in Southeast Asia and the Middle East have received growing interest.
- Over the past two decades, artificial intelligence (AI) has experienced rapid development and is being used in a wide range of sectors and activities, including finance.
- AccountsIQ offers a unique, cloud-based platform designed to revolutionize traditional accounting for SMEs and fast growing businesses.
A taxonomy of AI applications in Finance
Delight your customers with human-like AI-powered contact center experiences, such as banking concierge or customer center, to lower costs, and free up your human agents’ time. Transform personal finance and give customers more ways to manage their money by bringing smart, intuitive experiences to your apps, websites, digital platforms, and virtual tools. Deliver highly personalized recommendations for financial products and services, such as investment advice or banking offers, based on customer journeys, peer interactions, risk preferences, and financial goals. Second, train staff so they have the skills to effectively interact with AI tools, building analytical capabilities that capitalize on the technology.
By analyzing intricate patterns in transaction data sets, AI solutions allow financial organizations to improve risk management, which includes security, fraud, anti-money additional accounting student resources 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.
It’s no surprise that detecting fraud without the help of advanced technology and AI is almost impossible. Fraudsters are always going to try the most advanced, newest things that they can, and traditional non cognitive approaches will not always pick up on that suspicious activity. AI tools can monitor transactions in real-time for unusual patterns that may indicate fraudulent activity, often identifying issues that would go unnoticed by traditional systems. Companies are turning to AI-powered fraud detection systems to safeguard transactions.
Data availability
Forthcoming studies should also address black box how the sale of treasury stocks affects shareholder equity 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). Managing risk is one of the most critical areas of focus and concern for any financial organization. These companies want to be financially stable, mitigate losses, and maintain customer trust. Traditional risk management assessments often rely on analyzing past data which can be limited in the ability to predict and respond to emerging threats.
The operating model with the best results
The technology lets computers and machines simulate human intelligence capabilities—such as learning, interpreting speech, problem solving, perceiving, and, possibly someday, reasoning. AI encompasses a wide variety of technologies, including machine learning (ML), decision trees, inference engines, and computer vision. GenAI is a type of AI that can produce various types of content, including text, images, code, audio, music, and videos. It works by using an ML model to process human-generated content to identify patterns and structures.
AI and blockchain are both used across nearly all industries — but they work especially well together. 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. Here are a few examples of companies using AI to learn from customers and create a better banking experience. The following companies are just a few examples of how AI-infused technology is helping financial institutions make better trades. Learn how to transform your essential finance processes with trusted data, AI insights and automation.