AI in Finance: Applications, Examples & Benefits

finance ai

The company’s cloud-based platform, Derivative Edge, features automated tasks and processes, customizable workflows and sales opportunity management. There are also specific features based on portfolio specifics — for example, organizations using the platform for loan management can expect lender reporting, lender approvals and configurable dashboards. Workiva offers a cloud platform designed to simplify workflows for managing and reporting on data across finance, risk and ESG teams.

Preliminary results of the content analysis

finance ai

The second sub-stream focuses on mortgage and loan default prediction (Feldman and Gross 2005; Episcopos, Pericli, and Hu, 1998). For instance, Chen et al. (2013) evaluate real estate investment returns by forecasting the REIT index; they show that the industrial production index, the lending rate, the dividend yield and the stock index influence real estate investments. All the forecasting techniques adopted (i.e. supervised machine learning and ANNs) outperform linear models in terms of efficiency and precision.

A Survey of Trendy Financial Sector Applications of Machine and Deep Learning

The stream “AI and the Stock Market” comprises two sub-streams, namely algorithmic trading and stock market, and AI and stock price prediction. The first sub-stream deals with the impact of algorithmic trading (AT) on financial markets. In this regard, Herdershott et al. https://www.accountingcoaching.online/what-are-operating-expenses-definition-and-meaning/ (2011) argue that AT increases market liquidity by reducing spreads, adverse selection, and trade-related price discovery. This results in a lowered cost of equity for listed firms in the medium–long term, especially in emerging markets (Litzenberger et al. 2012).

Companies Using AI in Quantitative Trading

Modern neural networks, such as LSTM and NARX (nonlinear autoregressive exogenous network), also qualify as valid alternatives (Bucci 2020). Another promising class of neural networks is the higher-order neural network (HONN) used to forecast the 21-day-ahead realised volatility of FTSE100 futures. Thanks to its ability to capture higher-order correlations within the dataset, HONN shows remarkable performance in terms of statistical accuracy and trading efficiency over multi-layer perceptron (MLP) and the recurrent neural network (RNN) (Sermpinis et al. 2013). Through our analysis, we also detected the key theories and frameworks applied by researchers in the prior literature.

finance ai

AI and portfolio management

Today, companies are deploying AI-driven innovations to help them keep pace with constant change. According to the 2021 research report “Money and Machines,” by Savanta and Oracle, 85% of business leaders want help from artificial intelligence. In this paragraph, we shortly illustrate some relevant characteristics of our sub-sample made up of 110 studies, including country and industry coverage, method and underpinning theoretical background. Table 2 comprises the list of countries under scrutiny, and, for each of them, a list of papers that perform their analysis on that country. We can see that our sample exhibits significant geographical heterogeneity, as it covers 74 countries across all continents; however, the most investigated areas are three, that is Europe, the US and China.

  1. The main uses of AI in Finance and the papers that address each of them are summarised in Table 7.
  2. David Walker joined Westpac Group in August 2019 as group chief technology officer.
  3. Nvidia, the star stock of the AI boom, has more than tripled in the past year and just replaced Microsoft as the world’s most valuable company worth $3.3 trillion.
  4. FinanceGPT Chat lets you build your own AI co-pilots for personalized financial insights, market analysis, and smarter decision-making.
  5. Alpaca uses proprietary deep learning technology and high-speed data storage to support its yield farming platform.

AI and credit risk in banks

Additionally, 41 percent said they wanted more personalized banking experiences and information. The market value https://www.personal-accounting.org/ of AI in finance was estimated to be $9.45 billion in 2021 and is expected to grow 16.5 percent by 2030.

The company offers simulation solutions for risk management as well as environmental, social and governance settings. Simudyne’s secure simulation software uses agent-based modeling to provide how to calculate bad debt expenses a library of code for frequently used and specialized functions. Enova uses AI and machine learning in its lending platform to provide advanced financial analytics and credit assessment.

AI assistants, such as chatbots, use AI to generate personalized financial advice and natural language processing to provide instant, self-help customer service. Meta developed AI that can understand the things people type on Facebook or Instagram (natural language processing) and the photos users post (computer vision). That helps it understand the content it’s showing other users and makes it easier to block hate speech and other violence on its platforms. It developed a new machine-learning algorithm in the early 2010s to recommend content in users’ feeds. AI then powered its advertising business, ensuring the right users saw the right ad at the right time.

finance ai

Forthcoming research may analyse the effect of investor sentiment on specific sectors (Houlihan and Creamer 2021), as well as the impact of diverse types of news on financial markets (Heston and Sinha 2017). In this respect, Xu and Zhao (2022) propose a deeper analysis of how social networks’ sentiment affects individual stock returns. They also believe that the activity of financial influencers, such as financial analysts or investment advisors, potentially affects market returns and needs to be considered in financial forecasts or portfolio management.

Ocrolus offers document processing software that combines machine learning with human verification. The software allows business, organizations and individuals to increase speed and accuracy when analyzing financial documents. Ocrolus’ software analyzes bank statements, pay stubs, tax documents, mortgage forms, invoices and more to determine loan eligibility, with areas of focus including mortgage lending, business lending, consumer lending, credit scoring and KYC. 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.

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. Certain services may not be available to attest clients under the rules and regulations of public accounting. Sameena Shah is a Managing Director, Artificial Intelligence Research in Digital & Platform Services, where she and the team work across the firm to create Artificial Intelligence technologies for business transformation and growth. She is a highly accomplished leader with over 20 years of educational and industry experience in AI, engineering, data. Her leadership has resulted in award-winning AI technologies that have transformed products and businesses.

The advent of cloud computing and software-as-a-service (SaaS) deployments are at the forefront of a change in the way businesses think about ERP. Moving ERP to the cloud allows businesses to simplify their technology requirements, have constant access to innovation, and see a faster return on their investment. Hence, future contributions may advance our understanding of the implications of these latest developments for finance and other important fields, such as education and health. The last group studies intelligent credit scoring models, with machine learning systems, Adaboost and random forest delivering the best forecasts for credit rating changes. These models are robust to outliers, missing values and overfitting, and require minimal data intervention (Jones et al. 2015).

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