AI in finance OECD Business and Finance Outlook 2021 : AI in Business and Finance
From Generative AI to machine learning and other foundation model solutions, we look at the new era of AI innovations, the tools they may offer accounting and finance, and considerations for incorporating an AI framework for success. Canoe ensures that alternate investments data, like documents on venture capital, art and antiques, hedge funds and commodities, can be collected and extracted efficiently. The company’s platform uses natural language processing, machine learning and meta-data analysis to verify and categorize a customer’s alternate investment documentation. 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. Ocrolus offers document processing software that combines machine learning with human verification.
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. Many financial institutions are incorporating AI into their portfolio valuation processes to address these challenges.
Depending on how they are used, AI algorithms have the potential to help avoid discrimination based on human interactions, or intensify biases, unfair treatment and discrimination in financial services. The risk of unintended bias and discrimination of parts of the population is very much linked to the misuse of data and to the use of inappropriate data by ML model (e.g. in credit underwriting, see Section 1.2.3). AI applications can potentially compound existing biases found in the data; models trained with biased data will perpetuate biases; and the identification of spurious correlations may add another layer of such risk of unfair treatment (US Treasury, 2018[32]). In some jurisdictions, comparative evidence of disparate treatment, such as lower average credit limits for members of protected groups than for members of other groups, is considered discrimination regardless of whether there was intent to discriminate. 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]). Such investment is not constrained in monetary resources required to be invested in AI technologies but also relates to talent and staff skills involved in such techniques.
Chief Financial Officer (CFO)
Distributed ledger technologies (DLT) are increasingly being used in finance, supported by their purported benefits of speed, efficiency and transparency, driven by automation and disintermediation (OECD, 2020[25]). Major applications of DLTs in financial services include issuance and post-trade/clearing and settlement of securities; payments; central bank digital currencies and fiat-backed stablecoins; and the tokenisation of assets more broadly. Merging AI models, criticised for their opaque and ‘black box’ nature, with blockchain technologies, known for their transparency, sounds counter-intuitive in the first instance. Similar to all models using data, the risk of ‘garbage in, garbage out’ exists in ML-based models for risk scoring.
- While many investment firms rely on fully or partially automated investment strategies, the best results are still achieved by keeping humans in the loop and combining AI insights with human analysts’ reasoning capabilities.
- Between growing consumer demand for digital offerings, and the threat of tech-savvy startups, FIs are rapidly adopting digital services—by 2021, global banks’ IT budgets will surge to $297 billion.
- In other words, the industry experts don’t believe that AI can replace the role of human financial advisors.
- The use of big data by AI-powered models could expand the universe of data that is considered sensitive, as such models can become highly proficient in identifying users individually (US Treasury, 2018[32]).
Its platform finds new access points for consumer credit products like home equity lines of credit, home improvement loans and even home buy-lease offerings for retirement. Figure Marketplace uses blockchain to host a platform for investors, startups and private companies to raise capital, manage equity and trade shares. AI assistants, such as chatbots, use AI to generate personalized financial advice and natural language processing to provide instant, self-help customer service. Alpaca uses proprietary deep learning technology and high-speed data storage to support its yield farming platform. (Yield farming is when cryptocurrency investors pool their funds to carry out smart contracts that gain interest.) Alpaca is compatible with dozens of cryptocurrencies and allows users to lend assets to other investors in exchange for lending fees and protocol rewards. The following companies are just a few examples of how artificial intelligence in finance is helping banking institutions improve predictions and manage risk.
Top 10 Biggest US Banks by Assets in 2023
However, many investors saw Nvidia’s initiation of a position in SoundHound AI as something of an endorsement of the small company. After all, the giant chipmaker recently reported blowout fourth-quarter results and predicted the strong growth will continue. Before you start using AI in finance, evaluate your data infrastructure, quality, and completeness to avoid issues.
This, in turn, translates into increased volatility in times of stress, exacerbated through the simultaneous execution of large sales or purchases by many market participants, creating bouts of illiquidity and affecting the stability of the system in times of market stress. For a preview, look to the finance industry which has been incorporating data and algorithms for a long time, and which is always a canary in the coal mine for new technology. The experience of finance suggests that AI will transform some industries (sometimes very quickly) and that it will especially benefit larger players. 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.
This is likely because the pandemic has created major movements in macroeconomic variables, such as rising unemployment and mortgage forbearance, which required ML (as well as traditional) models to be recalibrated. The OECD has undertaken significant work in the area of digitalisation to understand and address the benefits, risks and potential policy responses for protecting and supporting financial consumers. The OECD has done this via its leading global policy work on financial education and financial consumer protection. The use of AI to build fully autonomous chains would raise important challenges and risks to its users and the wider ecosystem. In such environments, AI contracts rather than humans execute decisions and operate the systems and there is no human intervention in the decision-making or operation of the system.
Better fraud detection and regulatory compliance
Artificial intelligence (AI) and machine learning in finance encompasses everything from chatbot assistants to fraud detection and task automation. Most banks (80%) are highly aware of the potential benefits presented by AI, according to Insider Intelligence’s AI in Banking report. Derivative Path’s platform helps financial organizations control their derivative portfolios.
Trim is a money-saving assistant that connects to user accounts and analyzes spending. The smart app can cancel money-wasting subscriptions, find better options for services like insurance, and even negotiate bills. Trim has saved more than $20 million for its users, according to a 2021 Finance Buzz article.
Correct labelling and structuring of big data is another pre-requisite for ML models to be able to successfully identify what a signal is, distinguish signal from noise and recognise patterns in data (S&P, 2019[19]). Different methods are being developed to reduce the existence of irrelevant features or ‘noise’ in datasets and improve ML model performance, such as the creation of artificial or ‘synthetic’ datasets generated and employed for the purposes of ML modelling. These can be extremely useful for model testing and validation purposes in case the existing datasets lack scale or diversity (see Section 1.3.4). In theory, using AI in smart contracts could further enhance their automation, by increasing their autonomy and allowing the underlying code to be dynamically adjusted according to market conditions. The use of NLP could improve the analytical reach of smart contracts that are linked to traditional contracts, legislation and court decisions, going even further in analysing the intent of the parties involved (The Technolawgist, 2020[28]). It should be noted, however, that such applications of AI for smart contracts are purely theoretical at this stage and remain to be tested in real-life examples.
AlphaSense is valuable to a variety of financial professionals, organizations and companies — and is especially helpful for brokers. The search engine provides brokers and traders with access to SEC and global filings, earning call transcripts, press releases and information on both private and public companies. Socure created ID+ Platform, an identity verification https://accountingcoaching.online/ system that uses machine learning and AI to analyze an applicant’s online, offline and social data, which helps clients meet strict KYC conditions. The system runs predictive data science on information such as email addresses, phone numbers, IP addresses and proxies to investigate whether an applicant’s information is being used legitimately.
The difficulty in decomposing the output of a ML model into the underlying drivers of its decision, referred to as explainability, is the most pressing challenge in AI-based models used in finance. In addition to the inherent complexity of AI-based models, market participants may intentionally conceal the mechanics of their AI models to protect their intellectual property, further obscuring the techniques. The gap in technical literacy of most end-user consumers, coupled with the mismatch between the complexity characterising AI models and the demands of human-scale reasoning further aggravates the problem (Burrell, 2016[37]). The quality of the data used by AI models is fundamental to their appropriate functioning, however, when it comes to big data, there is some uncertainty around of the level of truthfulness, or veracity, of big data (IBM, 2020[31]).
Valuing a portfolio is crucial for assessing its performance, making investment decisions, and reporting accurate financial information to stakeholders. However, manual valuation can be challenging as various factors influence portfolio value, including market data, pricing models, time horizon, and allocation of diverse investment types such as stocks, bonds, mutual funds, derivatives, and other securities. The pioneering approach optimizes intricate financial strategies and decision-making processes, enhancing efficiency, accuracy, and adaptability in the dynamic world of finance. As the “tip of the spear” in generative AI, finance can build the strategy that fully considers all the opportunities, risks, and tradeoffs from adopting generative AI for finance. Smart AI can improve the efficiency of financial services, support growth, and reduce costs. The efficiency is achieved through streamlining credit card and loan approval processes, using RPA for running repetitive tasks, detecting cybersecurity attacks, and more.
Research suggests that explainability that is ‘human-meaningful’ can significantly affect the users’ perception of a system’s accuracy, independent of the actual accuracy observed (Nourani et al., 2020[42]). When less human-meaningful explanations are provided, the accuracy of the technique that does not operate on human-understandable rationale is less likely to be accurately judged by the users. 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.
Inadequate data may include poorly labelled or inaccurate data, data that reflects underlying human prejudices, or incomplete data (S&P, 2019[19]). A neutral machine learning model that is trained with inadequate data, risks producing inaccurate results even when fed with ‘good’ data. Equally, a neural network8 trained on high-quality data, which is fed inadequate data, will produce a questionable output, despite the well-trained underlying algorithm. At the single trader level, the lack of explainability of ML models used to devise trading strategies makes accruals concept it difficult to understand what drives the decision and adjust the strategy as needed in times of poor performance. Given that AI-based models do not follow linear processes (input A caused trading strategy B to be executed) which can be traced and interpreted, users cannot decompose the decision/model output into its underlying drivers to adjust or correct it. That said, there is no formal requirement for explainability for human-initiated trading strategies, although the rational underpinning these can be easily expressed by the trader involved.
