Artificial intelligence AI in finance

These tools and other rules-based innovations are pervasive, but AI is entering a new era. AI is having a moment, and the hype around AI innovation over the past year has reached new levels for good reason. It is transforming from rules-based models to foundational data-driven and language models.

AI Companies Managing Financial Risk

For this reason, substantial arbitrage opportunities are available in the Bitcoin market, especially for USD–CNY and EUR–CNY currency pairs (Pichl and Kaizoji 2017). Likewise, the feed-forward neural network effectively approximates the daily logarithmic returns of BTCUSD and the shape of their distribution (Pichl and Kaizoji 2017). After that, focussing on the more pertinent (110) articles, we checked the journals in which these studies were published.

AI and investor sentiment analysis

At the heart of this revolution lies the capabilities of generative AI tools like ChatGPT, which have garnered significant attention due to both their versatile applications and potential pitfalls, which I will discuss below. During the conference, researchers explored how large language models, a type of natural language processing (NLP), could benefit economic research. Professor Anton Korinek highlighted the potential for large language models to become helpful as research assistants and tutors for tasks like ideation, writing, background research, data analysis, coding, and more. Additionally, Professor Baozhong Yang discussed how he and his coauthors employed ChatGPT to extract managerial insights from corporate policy disclosures. Anne Hansen and Sophia Kazinnik of the Richmond Fed presented on the applications of GPT and its efficacy in analyzing “Fedspeak,” or Federal Reserve communications, to classify the overall stance of monetary policy.

Kensho Technologies

It helps businesses raise capital and handle automated marketing and messaging and uses blockchain to check investor referral and suitability. Additionally, Wealthblock’s AI automates content and keeps investors continuously engaged throughout the process. The platform validates customer identity with facial recognition, screens customers to ensure they are compliant with financial regulations and continuously assesses risk.

With the increasing complexity of regulatory compliance around the globe, the cost and resource burden of regulatory reporting has soared in recent years. Organizations devote significant time and resources to meeting those requirements. AI can take on a portion of the workload by automating compliance monitoring, audit trail management, and regulatory report creation. ARTIFICIAL INTELLIGENCE (AI) is the theory and development of computer systems able to perform tasks normally requiring human intelligence. Open access funding provided by Università Politecnica delle Marche within the CRUI-CARE Agreement. We are granted with research funds by our institution which would allow us to cover the publication costs.

1. In the NVIDIA survey, more than 80% of respondents reported increased revenue and decreased annual costs from using AI-enabled applications.
2. Hence, new AI approaches should be developed in order to optimise cryptocurrency portfolios (Burggraf 2021).
3. Here are a few examples of companies using AI to learn from customers and create a better banking experience.
4. For the CFO and their teams, there is a wealth of opportunity here, but also not inconsiderable risk.
5. The journey should begin with a sound strategy and a few use cases to test and learn with well-governed and accessible data.
6. Dixon et al. (2017) argue that deep neural networks have strong predictive power, with an accuracy rate equal to 68%.

This structure—where a central team is in charge of gen AI solutions, from design to execution, with independence from the rest of the enterprise—can allow for the fastest skill and capability building for the gen AI team. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. ArXiv is committed to these values and only works with partners that adhere to them. Robust compute resources are necessary to run AI on a data stream at scale; a cloud environment will provide the required flexibility. Managing these and other AI risks that are likely to emerge is possible through a framework and AI policy, but it is crucial to understand these risks so governance mechanisms can be built into an AI policy.

We included CS as a research area to capture a more technical angle to the topic and IS as research on the intersection of technology and practical implementation. We included peer-reviewed publications in leading international journals and conference proceedings to ensure high-quality standards. Besides, we limited the search to publications since 2010 to provide a more recent overview of current research in a rapidly changing environment of XAI research endeavors. The same search strategy (i.e., search terms and criteria) is independently applied to each outlet and database. The search took place from October 2021 to January 2022 to provide the most recent picture of current research, up to and including the year 2021.

The credit analyst asks the generative AI tool to search for any potential red flags concerning the customer, requesting specific examples of issues such as ongoing legal disputes, business-related concerns, liens, or public disagreements with other vendors.Output. Based on this output and an assessment of the information submitted by the customer, the credit analyst determines that the requested line of credit is acceptable and grants approval. If the tool had identified any red flags, the credit analyst would have needed to validate the information before incorporating it into the final credit decision. While many tasks will be automated or delegated to AI systems, the finance profession will still need human involvement to provide what AI cannot—including human creativity, judgment, emotional intelligence, relationship building, and critical thinking. Instead of being replaced, finance staff augmented by AI tools will focus on the most complex analysis and strategic decision-making. AI in finance can help reduce errors, particularly in areas where humans are prone to mistakes.

We discuss our results by summarizing the implications for both researchers and practitioners. Finally, we present the limitations of our study and avenues for future research regarding XAI in Finance. Table 5 gives an overview of XAI methods concerning their application areas in Finance. This overview allows us to indicate blind spots, deriving fruitful avenues for future research, especially from a Finance point of view.

Advanced NNs, such as Higher-Order Neural network (HONN) and Long Short-Term Memory Networks (LSTM), are more performing than their standard version but also much more complicated to apply. These methods are usually compared to autoregressive models and regressions, such as ARMA, ARIMA, and GARCH. Finally, we observe that almost all the sampled papers are quantitative, whilst only three of them are qualitative and four of them consist in literature reviews. Artificial intelligence (AI) in finance is the use of technology, including advanced algorithms and machine learning (ML), to analyze data, automate tasks and improve decision-making in the financial services industry. Ayasdi creates cloud-based machine intelligence solutions for fintech businesses and organizations to understand and manage risk, anticipate the needs of customers and even aid in anti-money laundering processes.

Second, train staff so they have the skills to effectively interact with AI tools, building analytical capabilities that capitalize on the technology. Giving finance staff increased understanding of AI will also be critical in ensuring the proper security, controls, and appropriate use of the technology. AI-based anomaly detection models can also be trained to identify transactions that could indicate fraud. AI systems in this case are continuously learning, and over time can reduce the instances of false positives as the algorithm is refined by learning which anomalies were fraudulent transactions and which weren’t. Task automation is an obvious cost reduction tactic, letting companies decrease their labor costs, fill workforce gaps, improve productivity and efficiency, and have employees focus on strategic, value-adding activities.

By that, AI can discover a broader range of trading opportunities where humans can’t detect. 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. AI can analyze relevant financial information and provide insights about financials by leveraging techniques like machine learning and natural language processing. Instead of conducting numerous calculations in spreadsheets or financial documents, AI can rapidly handle large volumes of documents and deliver insights without missing an important point.

This AI-powered prediction engine is designed to quickly analyze and adapt to changing market conditions and help deliver data-driven trading decisions. Kensho, an S&P Global company, created machine learning training and data analytics software that can assess thousands of datasets and documents. Its data training software uses a combination of machine learning, cloud computing and natural language processing, and investigation it can provide easily understandable answers to complex financial questions, as well as extract insights from tables and documents quickly. Traders with access to Kensho’s AI-powered database in the days following Brexit used the information to quickly predict an extended drop in the British pound, Forbes reported. Ocrolus offers document processing software that combines machine learning with human verification.

Transparent models must satisfy specific properties to be explainable to decision-makers. First, they are decomposable in that each part is fully explainable or interpretable by design, e.g., input parameters or computations. For instance, complex and non-interpretable input features managing an audit would fail this criterion, making an AI model less understandable (Lou et al. 2012). Second, transparent models should satisfy algorithmic transparency, i.e., a decision-maker’s need to understand the process of the model to produce its output derived from its inputs.

AI can even help make pricing personalized, using real-time insights about individual customer preferences, market changes, and competitor activity to optimize price and discounts. AI can then use the data to help generate financial statements, such as income statements, balance sheets, and cash flow statements, transforming the data into reports that highlight key single step income statement performance indicators (KPIs), trends, and observations. GenAI can fill out the needed forms with data provided by the finance team for the staff to review and confirm. AI can help automate and enhance multiple aspects of the financial reporting and analysis process. In the initial stages, it can extract relevant financial information from various data sources.