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. To compete and thrive in this challenging environment, traditional banks will need to build a new value proposition founded upon leading-edge AI-and-analytics straight line depreciation method capabilities. Many bank leaders recognize that the economies of scale afforded to organizations that efficiently deploy AI technologies will compel incumbents to strengthen customer engagement each day with distinctive experiences and superior value propositions.

A checklist of essential decisions to consider

On a retail level, advanced random forests accurately detect credit card fraud based on customer financial behaviour and spending pattern, and then flag it for investigation (Kumar et al. 2019). Similarly, Coats and Fant (1993) build a NN alert model for distressed firms that outperforms linear techniques. On a macroeconomic level, systemic risk monitoring models enhanced by AI technologies, i.e. k-nearest neighbours and sophisticated NNs, support macroprudential strategies and send alerts in case of global unusual financial activities (Holopainen, and Sarlin 2017; Huang and Guo 2021). 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.

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.

1. In return, the team delivers a family of products or services either to end customers of the bank or to other platforms within the bank.
2. Past studies have developed AI models that are capable of replicating the performance of stock indexes (known as index tracking strategy) and constructing efficient portfolios with no human intervention.
3. Specific theories concerning types of neural networks and learning methods are used too, such as the fuzzy set theory, which is mentioned in 8% of the sample, and to a lesser extent, the Naive Bayes theorem, the theory of neural networks, the theory of genetic programming and the TOPSIS analytical framework.
4. As we will explain, when these interdependent layers work in unison, they enable a bank to provide customers with distinctive omnichannel experiences, support at-scale personalization, and drive the rapid innovation cycles critical to remaining competitive in today’s world.
5. Financial services organizations are embracing artificial intelligence (AI) for various reasons, such as risk management, customer experience and forecasting market trends.
6. Natural language processing technologies are being used in banking to efficiently and accurately process and analyze large volumes of documents, Gupta said.

Identification of the major research streams

To overcome this limitation, Durango‐Gutiérrez et al. (2021) combine traditional methods (i.e. logistic regression) with AI (i.e. Multiple layer perceptron -MLP), thus gaining valuable insights on explanatory variables. With the scope of preventing further global financial crises, the banking industry relies on journal entries for inventory transactions financial decision support systems (FDSSs), which are strongly improved by AI-based models (Abedin et al. 2019). Through our analysis, we also detected the key theories and frameworks applied by researchers in the prior literature.

Centralized steering allows enterprises to focus resources on a handful of use cases, rapidly moving through initial experimentation to tackle the harder challenges of putting use cases into production and scaling them. Financial institutions using more dispersed approaches, on the other hand, struggle to move use cases past present value of a single amount the pilot stage. 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. 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. A practical way to get started is to evaluate how the bank’s strategic goals (e.g., growth, profitability, customer engagement, innovation) can be materially enabled by the range of AI technologies—and dovetailing AI goals with the strategic goals of the bank.

GenAI is also enabling banks and financial institutions to automate internal processes as much as possible. This includes areas such as data extraction, incident resolution, or the generation of quick documents and summaries to understand internal policies and procedures — “anything and everything that allows a bank to function day to day,” Sindhu said. This will lead to productivity gains by freeing up staff to do more strategic work.Right now, banks and financial institutions remain more focused on prioritizing internal use cases over customer-facing use cases, she added. They are trying to determine how they can manage risk and the cost-effectiveness of AI systems, how they can demonstrate ROI, and whether these investments are successful, Sindhu said. “These are the three top questions leaders are trying to work around as they scale their GenAI efforts.” There is high momentum for using AI technology, including GenAI tools, for fraud detection and regulatory compliance.

Customer experience

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. These results corroborate the fact that the above-mentioned regions are the leaders of the AI-driven financial industry, as suggested by PwC (2017). The United States, in particular, are considered the “early adopters” of AI and are likely to benefit the most from this source of competitive advantage. More lately, emerging countries in Southeast Asia and the Middle East have received growing interest. Finally, a smaller number of papers address underdeveloped regions in Africa and various economies in South America. After that, focussing on the more pertinent (110) articles, we checked the journals in which these studies were published.

Each platform team controls their own assets (e.g., technology solutions, data, infrastructure), budgets, key performance indicators, and talent. In return, the team delivers a family of products or services either to end customers of the bank or to other platforms within the bank. Business platforms are customer- or partner-facing teams dedicated to achieving business outcomes in areas such as consumer lending, corporate lending, and transaction banking. Enterprise platforms deliver specialized capabilities and/or shared services to establish standardization throughout the organization in areas such as collections, payment utilities, human resources, and finance. And enabling platforms enable the enterprise and business platforms to deliver cross-cutting technical functionalities such as cybersecurity and cloud architecture. For this purpose, sentiment analysis extracts investor sentiment from social media platforms (e.g. StockTwits, Yahoo-finance, eastmoney.com) through natural language processing and data mining techniques, and classifies it into negative or positive (Yin et al. 2020).

Built In strives to maintain accuracy in all its editorial coverage, but it is not intended to be a substitute for financial or legal advice.Jessica Powers, Ana Gore and Margo Steines contributed to this story. Additionally, 41 percent said they wanted more personalized banking experiences and information. Gradient AI specializes in AI-powered underwriting and claims management solutions for the insurance industry. For example, the company’s products for commercial auto claims are able to predict how likely a bodily injury claim is to cross a certain cost threshold and how likely it is to lead to costly litigation. The market value of AI in finance was estimated to be $9.45 billion in 2021 and is expected to grow 16.5 percent by 2030.