ICAIF’23 4th ACM International Conference on AI in Finance

Major FinTech companies are slowly moving away from storing data in traditional database like SQL towards using blockchain that provides better encrypted platform for storing sensitive information. Leading lenders, like Ally, are also automating the process of approving the loan and predicting the maximum amount a customer may borrow and the pricing of the loan using AI and ML models. This can be accomplished by looking at real-time indicators that aren’t considered in a typical credit score, such as whether the borrower spends their money on necessities or luxuries, their income level, employment opportunities, and potential to earn.

• AI in trading is used for core aspects of trading strategies, as well as at the back-office for risk management purposes.
• The most visible prospect for the customer will be that of ‘augmented’ banking or insurance advisors.
• AI techniques such as NLP12 are already being tested for use in the analysis of patterns in smart contract execution so as to detect fraudulent activity and enhance the security of the network.
• Generative AI can be employed by financial institutions to produce synthetic data that adheres to privacy regulations such as GDPR and CCPA.
• ATP Bot, a leading digital currency platform, launched an AI bot for quantitative trading similar to ChatGPT, providing investors with a scientific and effective way to invest.

It entails using machine learning algorithms to generate new data and valuable insights that can assist in making informed financial decisions. AI plays a significant role in the banking sector, particularly in loan decision-making processes. It helps banks and financial institutions assess customers’ creditworthiness, determine appropriate credit limits, and set loan pricing based on risk. However, both decision-makers and loan applicants need clear explanations of AI-based decisions, such as reasons for application denials, to foster trust and improve customer awareness for future applications. Generative AI can generate synthetic data that simulates various compliance scenarios and regulatory reporting requirements. This synthetic data provides a controlled environment for compliance testing, enabling financial institutions to assess their systems, processes, and controls.

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. One of the main challenges of AI in financial services is the amount of data collected from the customers, which contains sensitive and confidential information like transaction history, account information, or loan details. For example, voice-activated programs are used to save time searching for customer information in a database or through piles of documents. What’s more, some banks and investment firms are connecting their technology with Alexa, allowing their customers to check their account balance, make payments, place orders, or ask customer service for help. Notable features of ATPBot include cutting-edge algorithms that incorporate multiple factors to identify profitable methods from complex data types.

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. The main use-case of AI in asset management is for the generation of strategies that influence decision-making around portfolio allocation, and relies on the use of big data and ML models trained on such datasets. In this article, we’ll go over the top 7 AI tools for finance teams and how they are reshaping the finance industry by streamlining processes and eliminating manual work.

AI and Personalized Banking

AI can help companies drive accountability transparency and meet their governance and regulatory obligations. For example, financial institutions want to be able to weed out implicit bias and uncertainty in applying the power of AI to fight money laundering and other financial crimes. For many IT departments, ERP systems have often meant large, costly, and time-consuming deployments that might require significant hardware or infrastructure investments. The advent of cloud computing and software-as-a-service (SaaS) deployments are at the forefront of a change in the way businesses think about ERP. Moving ERP to the cloud allows businesses to simplify their technology requirements, have constant access to innovation, and see a faster return on their investment. Specific software, such as enterprise resource planning (ERP,) is used by organizations to help them manage their accounting, procurement processes, projects, and more throughout the enterprise.

Financial reporting narratives (as well as commentary) play a pivotal role in providing meaningful insights and contextual understanding of a company’s financial performance. We must transform from manual processes (that require meticulous analysis, critical thinking and effective communication skills) to AI-powered processes that streamline and improve operational efficiency. Bank unlocks and analyzes all relevant data on customers via deep learning to help identify bad actors. It’s been using this technology for anti-money laundering and, according to an Insider Intelligence report, has doubled the output compared with the prior systems’ traditional capabilities. In advanced deep learning models, issues may arise concerning the ultimate control of the model, as AI could unintentionally behave in a way that is contrary to consumer interests (e.g. biased results in credit underwriting). 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]).

Automating middle-office tasks with AI has the potential to save North American banks $70 billion by 2025. Further, the aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total. Developed economies have regulations in place to ensure that specific types of data are not being used in the credit risk analysis (e.g. US regulation around race data or zip code data, protected category data in the United Kingdom). Regulation promoting anti-discrimination principles, such as the US fair lending laws, exists in many jurisdictions, and regulators are globally considering the risk of potential bias and discrimination risk that AI/ML and algorithms can pose (White & Case, 2017[22]). In addition to its transaction sorting capabilities, Rebank serves as a reliable transfer tool for companies engaged in cross-border transactions.

• Generative AI-generated transaction data can be used to train machine learning models specifically designed for fraud prediction.
• The “moving average” part, in the case of ARMA models, refers to the dependence on past forecast errors or residuals.
• In this section, we explore three areas where AI applications are fast becoming industry standard for the financial sector.
• Companies in various industries, even conservative ones such as banking and law, are now turning to AI to help improve customer service and the overall customer experience.
• Customer service is crucial in the banking industry and good customer service can often differentiate one institution from another and retain valuable customers, including high-net-worth individuals.

The use of the term AI in this note includes AI and its applications through ML models and the use of big data. Kill switches and other similar control mechanisms need to be tested and monitored themselves, to ensure that firms can rely on them in case of need. Nevertheless, such mechanisms could be considered suboptimal from a policy perspective, as they switch off the operation of the systems when it is most needed in times of stress, giving rise to operational vulnerabilities.

Principle 8: Protection of Consumer Data & Privacy

As technology has become more advanced and sophisticated, companies have turned their eyes on artificial intelligence (AI) as a way to solve a whole range of issues for them. New AI applications are being developed to automate a wider range of tasks, from customer service to fraud detection. One place where AI can have a real impact is automating manual tasks, especially in finance.

Generative AI models are crucial in generating trading signals and identifying investment opportunities. By analyzing vast amounts of historical market data, generative AI algorithms can identify patterns, trends, and correlations that may not be evident to human traders or investors. These models can generate trading signals, indicating optimal entry and exit points for specific financial assets. Generative AI empowers traders and investors to make data-driven decisions and identify potential opportunities that align with their investment objectives.

We can help you build a strategic roadmap for transformation, so generative AI can deliver immense business value and improve operational efficiency. AI analyzes financial statements, notes, disclosures and other and applicable data, then translates and interprets the data to provide context-rich answers to your questions. For instance, we have illustrated how generative AI can improve cycle times when generating financial report narratives and commentary. Figure 1 shows financial processes that might have taken nearly two weeks to complete, and Figure 2 shows how those processes are now accelerated with the application of generative AI throughout, resulting in real-time commentary and narrative generation.

Prebuilt AI solutions enable you to streamline your implementation with a ready-to-go solution for more common business problems. Oracle’s AI is embedded in Oracle Cloud ERP and does not require any additional integration or set of tools; Oracle updates its application suite quarterly to support your changing needs. Companies that take their time incorporating AI also run the risk of becoming less attractive to the next generation of finance professionals. 83% of millennials and 79% of Generation Z respondents said they would trust a robot over their organization’s finance team. Millennial employees are nearly four times more likely than Baby Boomers to want to work for a company using AI to manage finance.

How to improve your finance operation’s efficiency with generative AI

Digital banks and loan-issuing apps use machine learning algorithms to use alternative data (e.g., smartphone data) to evaluate loan eligibility and provide personalized options. The models that serve to refine ‘customer risks’ are not only based on financial ratios and the intrinsic characteristics of abnormal transactions, Patrice Latinne believes. Large amounts of external public or paid data are automatically analyzed, aggregated and integrated by AI. “The first tools allowed us to improve the ‘cost to serve’ of our operational functions,” says Nicolas Goosse. In the beginning, it was about automated algorithmic models that allowed for a certain amount of prediction. At that time, there was no advanced artificial intelligence that enabled machines to derive rules automatically and learn by themselves.

The advisor trades on an investor’s behalf and manages their account using survey responses which human advisors usually run. AI Autotrade is thriving, and it’s developing entirely autonomous trading machines that combine technical analysis with AI self-learning algorithms whose task is to manage deposits for profit. Recent studies show that machine learning algorithms already close approximately 80% of all trading operations on US exchanges. Additionally, AI and Cognitive ML models can decrease the likelihood of false positives or the rejection of otherwise legitimate transactions (such as a credit card payment that was mistakenly refused), thus increasing customer satisfaction. But AI can’t rely on real-time data for training due to the already introduced bias in the current system. Some recent studies show that predictive systems trained on real people’s mortgage data skew automated decision-making in a way that disadvantages low-income and minority groups.

Enforcement authorities need to be technically capable of inspecting AI-based systems and empowered to intervene when required (European Commission, 2020[43]). The upskilling of policy makers will also allow them to expand their own use of AI in RegTech and SupTech, an important area of application of innovation in the official sector (see Chapter 5). 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, Increased Investment in Subsidiary Journal Entry 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 human parameter is critical both at the data input stage and at the query input stage and a degree of scepticism in the evaluation of the model results can be critical in minimising the risks of biased model decision-making.

Generative AI in finance and banking: The current state and future implications

Simplify the governance of data access, deliver a seamless user experience, and adapt identity and access governance. EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. As you encounter new generative AI solutions and unique AI foundation models for F&A, you may find yourself overwhelmed by all the options. It will be important for you to be selective and confident that the model you choose can effectively accelerate adoption and reduce time to value for your F&A use case overall. But that transformation depends on the technology foundation of a financial management system.

This enhances investment efficiency and stability while reducing reliance on subjective judgment and experience-based decision-making. Transform finance operations with AI-powered insights, recommendations, and automation built into your SAP applications. Infusing your finance processes with AI will help grow your finance team’s efficiency, business foresight, and enhance your organization’s security and compliance.

This automation not only streamlines the reporting process and reduces manual effort, but it also ensures consistency, accuracy, and timely delivery of reports. However, enterprise generative AI has unique challenges and finance executives are not aware of most generative AI applications in their industry which slows down adoption. The Snowfox.AI service can route and post your purchase invoices automatically with artificial intelligence. For example, decision-making that calls for human judgment and experience are still in your hands.

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.