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Similar to other types of models, contingency and security plans need to be in place, as needed (in particular related to whether the model is critical or not), to allow business to function as usual if any vulnerability materialises. Possible risks of concentration of certain third-party providers may rise in terms of data collection and management (e.g. dataset providers) or in the area of technology (e.g. third party model providers) and infrastructure (e.g. cloud providers) provision. AI models and techniques are being commoditised through cloud adoption, and the risk of dependency on providers of outsourced solutions raises new challenges for competitive dynamics and potential oligopolistic market structures in such services. Lack of interpretability of AI and ML algorithms could become a macro-level risk if not appropriately supervised by micro prudential supervisors, as it becomes difficult for both firms and supervisors to predict how models will affect markets (FSB, 2017[11]). In the absence of an understanding of the detailed mechanics underlying a model, users have limited room to predict how their models affect market conditions, and whether they contribute to market shocks. Users are also unable to adjust their strategies in time of poor performance or in times of stress, leading to potential episodes of exacerbated market volatility and bouts of illiquidity during periods of acute stress, aggravating flash crash type of events (see Section 1.2.2).

Financial forecasting and planning

Scaling isn’t easy, and institutions should make a push to bring gen AI solutions to market with the appropriate operating model before they can reap the nascent technology’s full benefits. By leveraging financial models, institutions can make faster and more informed decisions in response to changing market conditions. To extract relevant insights, They can use models to analyze unstructured data sources, such as news articles, social media feeds, and research reports.

Elizabeth Bramson-Boudreau, CEO and Publisher, MIT Technology Review

In addition, the autonomous behaviour of some AI systems during their life cycle may entail important product changes having an impact on safety, which may require a new risk assessment (European Commission, 2020[43]). Human oversight from the product design and throughout the lifecycle of the AI products and systems may be needed as a safeguard (European Commission, 2020[43]). Data is the cornerstone of any AI application, but the inappropriate use of data in AI-powered applications or the use of inadequate data introduces an important source of non-financial risk to firms using AI techniques. Such risk relates to the veracity https://www.kelleysbookkeeping.com/ of the data used; challenges around data privacy and confidentiality; fairness considerations and potential concentration and broader competition issues. The provision of infrastructure systems and services like transportation, energy, water and waste management are at the heart of meeting significant challenges facing societies such as demographics, migration, urbanisation, water scarcity and climate change. Modernising existing infrastructure stock, while conceiving and building infrastructure to address these challenges and providing a basis for economic growth and development is essential to meet future needs.

Companies Using AI in Finance

AI in finance automates transactions, enhances data analysis, improves customer service, and boosts security through fraud detection and risk management systems. AI dramatically accelerates customer service and response times in finance by processing information at speeds far beyond traditional methods. This rapid processing capability allows financial institutions to offer instant financial services such as real-time transaction processing, immediate customer feedback, and quick resolution of inquiries and issues. Additionally, in credit risk assessment, AI models evaluate potential borrowers more accurately, reducing the risk of defaults and improving portfolio performance. By integrating AI, financial entities not only gain a competitive edge but also enhance operational efficiency and risk management, leading to more robust financial health and customer trust.

Operating-model archetypes for gen AI in banking

Nevertheless, we notice that support vector machine and random forest are the most widespread machine learning methods. Backpropagation, Recurrent, and Feed-Forward NNs are considered basic neural nets and are commonly employed. 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.

AI assistants, such as chatbots, use AI to generate personalized financial advice and natural language processing to provide instant, self-help customer service. Zest AI is an AI-powered underwriting platform that helps companies assess borrowers with little to no credit information or history. 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.

Examples of back-office operations and functions managed by ERP include financials, procurement, accounting, supply chain management, risk management, analytics, and enterprise performance management (EPM). Without the right gen AI operating model in place, it is tough to incorporate enough structure and move quickly enough to generate enterprise-wide impact. To choose the operating model that works best, financial institutions need to address some important points, such as setting expectations for the gen AI team’s role and embedding flexibility into the model so it can adapt over time. That flexibility pertains to not only high-level organizational aspects of the operating model but also specific components such as funding. We recently conducted a review of gen AI use by 16 of the largest financial institutions across Europe and the United States, collectively representing nearly $26 trillion in assets. Our review showed that more than 50 percent of the businesses studied have adopted a more centrally led organization for gen AI, even in cases where their usual setup for data and analytics is relatively decentralized.

The findings of the aforementioned papers confirm that AI-powered classifiers are extremely accurate and easy to interpret, hence, superior to classic linear models. A quite interesting paper surveys the relationship between face masculinity traits in CEOs and firm riskiness through image processing (Kamiya et al. 2018). The results reveal that firms lead by masculine-faced CEO have higher risk and leverage ratios https://www.business-accounting.net/knowing-your-debits-from-your-credits/ and are more frequent acquirers in MandA operations. Through our analysis, we also detected the key theories and frameworks applied by researchers in the prior literature. Finance theories (e.g. Arbitrage Pricing Theory; Black and Scholes 1973) are jointly employed with portfolio management theories (e.g. modern portfolio theory), and the two of them account together for 21% (15) of the total number of papers.

The possible simultaneous execution of large sales or purchases by traders using the similar AI-based models could give rise to new sources of vulnerabilities (FSB, 2017[11]). Indeed, some algo-HFT strategies appear to have contributed to extreme market volatility, reduced liquidity and exacerbated flash crashes that have occurred with growing frequency over the past several years (OECD, 2019[12]) . In addition, the use of ‘off-the-shelf’ algorithms by a large part of the market could prompt herding behaviour, convergence and one-way markets, further amplifying volatility risks, pro-cyclicality, and unexpected changes in the market both in terms of scale and in terms of direction. In the absence when to prepare multiyear financial statements of market makers willing to act as shock-absorbers by taking on the opposite side of transactions, such herding behaviour may lead to bouts of illiquidity, particularly in times of stress when liquidity is most important. Asset managers and the buy-side of the market have used AI for a number of years already, mainly for portfolio allocation, but also to strengthen risk management and back-office operations. AI is also used by asset managers and other institutional investors to enhance risk management, as ML allow for the cost-effective monitoring of thousands of risk parameters on a daily basis, and for the simulation of portfolio performance under thousands of market/economic scenarios.

1. It encourages financial education policy makers to cooperate with the authorities in charge of personal data protection frameworks and it identifies additional elements pertaining to personal data to complement the core competencies identified in the G20 OECD INFE Policy Guidance note.
2. Given the investment required by firms for the deployment of AI strategies, there is potential risk of concentration in a small number of large financial services firms, as bigger and more powerful players may outpace some of their smaller rivals (Financial Times, 2020[6]).
3. In addition, the introduction of automated mechanisms that switch off the model instantaneously (such as kill switches) is very difficult in such networks, not least because of the decentralised nature of the network.
4. Over-reliance on outsourcing may also give rise to increased risk of disruption of service with potential systemic impact in the markets.

Here, AI systems are being used to look over documentation and speed up the assessment of whether a consumer can afford credit products, such as mortgages. The use of the term AI in this note includes AI and its applications through ML models and the use of big data. Smart contracts facilitate the disintermediation from which DLT-based networks can benefit, and are one of the major source of efficiencies that such networks claim to offer. They allow for the full automation of actions such as payments or transfer of assets upon triggering of certain conditions, which are pre-defined and registered in the code.

Chat-bots powered by AI are deployed in client on-boarding and customer service, AI techniques are used for KYC, AML/CFT checks, ML models help recognise abnormal transactions and identify suspicious and/or fraudulent activity, while AI is also used for risk management purposes. When it comes to credit risk management of loan portfolios, ML models used to predict corporate defaults have been shown to produce superior results compared to standard statistical models (e.g. logic regressions) when limited information is available (Bank of Italy, 2019[17]). AI-based systems can also help analyse the degree of interconnectedness between borrowers, allowing for better risk management of lending portfolios. Banks and other financial institutions should balance speed and innovation with risk, adapting their structures to harness the technology’s full potential. As financial-services companies navigate this journey, the strategies outlined in this article can serve as a guide to aligning their gen AI initiatives with strategic goals for maximum impact.

While non-financial information has long been used by traders to understand and predict stock price impact, the use of AI techniques such as NLP brings such analysis to a different level. Text mining and analysis of non-financial big data (such as social media posts or satellite data) with AI allows for automated data analysis at a scale that exceeds human capabilities. Considering the interconnectedness of asset classes and geographic regions in today’s financial markets, the use of AI improves significantly the predictive capacity of algorithms used for trading strategies. 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). The resulting sentiment is regarded either as a risk factor in asset pricing models, an input to forecast asset price direction, or an intraday stock index return (Houlihan and Creamer 2021; Renault 2017).

About 30 percent use the centrally led, business unit–executed approach, centralizing decision making but delegating execution. Roughly 30 percent use the business unit–led, centrally supported approach, centralizing only standard setting and allowing each unit to set and execute its strategic priorities. The remaining institutions, approximately 20 percent, fall under the highly decentralized archetype. These are mainly large institutions whose business units can muster sufficient resources for an autonomous gen AI approach.

“We have 15 different AI models live on our platform, performing different functions,” explains Stuart Cheetham, chief executive of mortgage lender MPowered Mortgages. Different models check which bank a statement is from, examine its veracity, and transform it into machine readable data which can be used to help make a decision. Financial institutions now hope that generative AI could replace these systems with alternatives that are more capable of responding to complex requests, learning how to deal with specific customer needs, and improving over time. Reinforcement learning involves the learning of the algorithm through interaction and feedback.