AI in Finance: Benefits, Real-World Use Cases, and Examples
Its benefits include increased efficiency and accuracy, reduced risks, cost containment, and more informed decision-making. Real-time financial performance insights from enhanced reporting are displayed on AI-powered dashboards for easy reference and decision-making. Machine learning algorithms crunch the numbers, assess risk, and personalize offerings faster and potentially fairer than traditional methods.
- The identification of converging points, where human and AI are integrated, will be critical for the practical implementation of such a combined ‘man and machine’ approach (‘human in the loop’).
- AI has given the world of banking and finance new ways to meet the customer demands of smarter, safer and more convenient ways to access, spend, save and invest money.
- The decision for financial institutions (FIs) to adopt AI will be accelerated by technological advancement, increased user acceptance, and shifting regulatory frameworks.
- 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.
- 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.
The OECD has done this via its leading global policy work on financial education and financial consumer protection. In the future, the use of DLTs in AI mechanisms is expected to allow users of such systems to monetise their data used by AI-driven systems through the use of Internet of Things (IoT) applications, for instance. Potential consequences of the use of AI in trading are also observed in the competition field (see Chapter 4). Traders may intentionally add to the general lack of transparency and explainability in proprietary ML models so as to retain their competitive edge. This, in turn, can raise issues related to the supervision of ML models and algorithms. In addition, the use of algorithms in trading can also make collusive outcomes easier to sustain and more likely to be observed in digital markets (OECD, 2017[16]).
What is machine learning (ML)?
The Review will include considering digital developments and their impacts on the provision of financial services to consumers. Synthetic datasets can also allow financial firms to secure non-disclosive computation to protect consumer privacy, another of the important challenges of data use in AI, by creating anonymous datasets that comply with privacy requirements. Traditional data anonymisation approaches do not provide rigorous privacy guarantees, as ML models have the power to make inferences in big datasets. 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]). Facial recognition technology or data around the customer profile can be used by the model to identify users or infer other characteristics, such as gender, when joined up with other information. AI techniques could further strengthen the ability of BigTech to provide novel and customised services, reinforcing their competitive advantage over traditional financial services firms and potentially allowing BigTech to dominate in certain parts of the market.
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. 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.
Principle 8: Protection of Consumer Data & Privacy
Nanonets Flow is a solution that uses AI in finance to streamline processes, increase efficiency, and improve results. This tool extracts data from documents like invoices and receipts, automates processes, manages workflows, and integrates with your current financial and accounting platforms. Humans are inefficient and error-prone when discerning between normal versus abnormal or suspicious behavior for fraud detection. AI tools efficiently analyze real-time data to accurately detect potentially fraudulent behavior and notify relevant parties for further review. As the technology evolves, financial professionals continue to find innovative applications for artificial intelligence that enhance decision-making, streamline operations, and drive efficiency across the industry. Below, we’ll explore 10 compelling use cases demonstrating how AI transforms the finance sector.
Are the ERP applications cloud-enabled?
AI is particularly helpful in corporate finance as it can better predict and assess loan risks. For companies looking to increase their value, AI technologies such as machine learning can help improve loan underwriting and reduce financial risk. AI can also lessen financial crime through advanced fraud detection and spot anomalous activity as company accountants, analysts, treasurers, and investors work toward long-term growth. One of the most significant business cases for AI in finance is its ability to prevent fraud and cyberattacks. Consumers look for banks and other financial services that provide secure accounts, especially with online payment fraud losses expected to jump to $48 billion per year by 2023, according to Insider Intelligence.
These are naturally not captured by the initial dataset on which the model was trained and are likely to result in performance degradation. Synthetic datasets and alternative data are being artificially generated to serve as test sets for validation, used to confirm that the model is being used and performs as intended. Synthetic databases provide an interesting alternative given that they can provide inexhaustible amounts of simulated data, and a potentially cheaper way of improving the predictive power and enhancing the robustness of ML models, especially where real data is scarce and expensive. Some regulators require, in some instances, the evaluation of the results produced by AI models in test scenarios set by the supervisory authorities (e.g. Germany) (IOSCO, 2020[39]). For example, AI can be a powerful tool to optimise windmill operations and safety, analyse traffic patterns in transportation, and improve operations in energy grids. What is more, the deployment of AI by traders could amplify the interconnectedness of financial markets and institutions in unexpected ways, potentially increasing correlations and dependencies of previously unrelated variables (FSB, 2017[11]).
And, in finance, we see this emerging technology used for data analysis, fraud detection, forecasting, and compliance monitoring. Many financial institutions are incorporating AI into their portfolio valuation processes to address these challenges. Financial institutions can enhance accuracy, efficiency, and decision-making with ai-powered asset valuation that is automated and accurate. These models can instantly consider factors such as historical market data, current market behavior, pricing models, proprietary research, and performance indicators. Cybercrime costs the world economy around $600 billion annually (that is 0.8% of the global GDP).
Machine learning algorithms and pattern recognition allow businesses to go beyond the typical examination of credit scores and credit histories to rate borrowers’ creditworthiness when applying for credit cards and other loans. Other forms of AI include natural language processing, robotics, computer vision, and neural networks. Natural language processing and large language models (LLM) form the basis of chatbots like ChatGPT.
2.2. Algorithmic Trading
It has been argued that at this stage of development of the infrastructure, storing data off chain would be a better option for real time recommendation engines to prevent latency and reduce costs (Almasoud et al., 2020[27]). Challenges also exist with regards to the legal status of smart contracts, as these are still not considered to be legal contracts in most jurisdictions (OECD, 2020[25]). Until it is clarified whether contract law applies to smart contracts, enforceability and financial protection issues will persist. That said, some AI use-cases are proving helpful in augmenting smart contract capabilities, particularly when it comes to risk management and the identification of flaws in the code of the smart contract.
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. Its offerings include checking and savings accounts, small business loans, student loan refinancing and credit score insights.
The opacity of algorithm-based systems could be addressed through transparency requirements, ensuring that clear information is provided as to the AI system’s capabilities and limitations (European Commission, 2020[43]). Separate disclosure should inform consumers about the use of AI system in the delivery of a product and their interaction with an AI system instead of a human being (e.g. robo-advisors), to allow customers to make conscious choices among irs releases 2020 standard mileage rates competing products. Suitability requirements, such as the ones applicable to the sale of investment products, might help firms better assess whether the prospective clients have a solid understanding of how the use of AI affects the delivery of the product/service. To date, there is no commonly accepted practice as to the level of disclosure that should be provided to investors and financial consumers and potential proportionality in such information.
Algorithmic trading (aka algo trading) allows traders to execute trades more accurately and faster. Using AI to unlock the potential in the finance sector offers limitless possibilities. It’s a journey that financial chiefs need to consider and open the door to more innovations.
Because the company does not know the customer, it must conduct a comprehensive credit review before proceeding. The company’s traditional credit review process sought to identify problematic legal or business issues by gathering information from the customer supplemented with additional data collected through third-party sources and internet searches. To expedite the latter task, the credit analyst decides to utilize an internet-enabled generative AI tool.Input. The analyst inputs a process document and prior credit reviews, including supporting https://intuit-payroll.org/ customer information, such as company name, website, and other identifiers.Query. 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.
Access to customer data by firms that fall outside the regulatory perimeter, such as BigTech, raises risks of concentrations and dependencies on a few large players. Unequal access to data and potential dominance in the sourcing of big data by few big BigTech in particular, could reduce the capacity of smaller players to compete in the market for AI-based products/services. The largest potential of AI in DLT-based finance lies in its use in smart contracts11, with practical implications around their governance and risk management and with numerous hypothetical (and yet untested) effects on roles and processes of DLT-based networks. Smart contracts rely on simple software code and have existed long before the advent of AI.
These algorithms can suggest risk rules for banks to help block nefarious activity like suspicious logins, identity theft attempts, and fraudulent transactions. Generally, artificial intelligence is the ability of computers and machines to perform tasks that normally require human intelligence, such as identifying a type of plant with just a picture of it. For example, CitiBank has inked a deal with data science market leader Feedzai, which helps to flag suspicious payments and safeguard trillions of dollars in daily operations.
Quantitative trading is the process of using large data sets to identify patterns that can be used to make strategic trades. AI-powered computers can analyze large, complex data sets faster and more efficiently than humans. Zest AI is an AI-powered underwriting platform that helps companies assess borrowers with little to no credit information or history. If there’s one technology paying dividends for the financial sector, it’s artificial intelligence. AI has given the world of banking and finance new ways to meet the customer demands of smarter, safer and more convenient ways to access, spend, save and invest money.