10+ Generative AI Finance Use Cases in 2023

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10+ Generative AI Finance Use Cases in 2023

This allows financial professionals to develop and fine-tune their investment strategies, optimize risk-adjusted returns, and make more informed decisions about managing their portfolios. Generative AI can be used for fraud detection in finance by generating synthetic examples of fraudulent transactions or activities. These generated examples can help train and augment machine learning algorithms to recognize and differentiate between legitimate and fraudulent patterns in financial data. In the context of conversational finance, generative AI models can be used to produce more natural and contextually relevant responses, as they are trained to understand and generate human-like language patterns. As a result, generative AI can significantly enhance the performance and user experience of financial conversational AI systems by providing more accurate, engaging, and nuanced interactions with users. For instance, Morgan Stanley employs OpenAI-powered chatbots to support financial advisors by utilizing the company’s internal collection of research and data as a knowledge resource.

• Generative AI-driven cyber-attack simulations provide valuable insights into the effectiveness of existing security measures and aid in developing proactive cybersecurity strategies.
• Deep learning, a subset of machine learning, utilizes neural networks and is applied to machine learning problems simultaneously perform feature extraction and prediction within the neural network architecture.
• Large amounts of external public or paid data are automatically analyzed, aggregated and integrated by AI.
• One place where AI can have a real impact is automating manual tasks, especially in finance.
• Generative AI enables the analysis of historical market data and the application of advanced algorithms to uncover insights and patterns.

Extracts from publications may be subject to additional disclaimers, which are set out in the complete version of the publication, available at the link provided. Smart contracts are distributed applications written as code on Blockchain ledgers, automatically executed upon reaching pre-defined trigger events written in the code (OECD, 2020[25]). Such tools can also be used in high frequency trading to the extent that investors use them to place trades ahead of competition. 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.

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The company’s cloud-based platform, Derivative Edge, features automated tasks and processes, customizable workflows and sales opportunity management. There are also specific features based on portfolio specifics — for example, organizations using the platform for loan management can expect lender reporting, lender approvals and configurable dashboards. Zest AI is an AI-powered underwriting platform that helps companies assess borrowers with little to no credit information or history. These AI-enabled toolkits look for outliers that demonstrate data bias and remove them from  the data flow. It’s also helpful to generate synthetic data by analysing clustered data points to increase the efficiency of the models involved. Machine learning applications in the finance sector are likely to take security to the next level through the use of voice and face recognition, as well as other biometric data.

It combines real-time market data provided by Bloomberg with an advanced learning engine to identify patterns in price movements for high-accuracy market predictions. Applying AI to predictable finance processes and tasks that are traditionally labor intensive is essential for modernizing the financial services industry. For example, finance teams have traditionally spent an inordinate amount of time gathering information and reconciling throughout the month and at period end.

BloombergGPT developed on a vast corpus of over 700 billion tokens, utilizes generative AI techniques to comprehend and interpret financial data, enabling it to perform a wide range of NLP tasks specific to the finance industry. It is part of the FinNLP project, which aims to democratize Internet-scale financial data and provide accessible tools for language modeling in finance. FinGPT leverages the strengths of existing open-source large language models (LLMs) and is fine-tuned using financial data for language modeling tasks in the financial domain. Generative AI plays a significant role in reducing operational costs and improving customer service quality. By leveraging generative AI-powered chatbots, financial institutions can automate routine and repetitive customer support tasks, reducing the need for manual intervention.

By automating compliance processes, Generative AI helps identify potential compliance breaches and mitigate risks promptly. It enables real-time monitoring of transactions, identification of anomalies, and detection of patterns that indicate potential compliance violations. Generative AI can also analyze regulatory changes and update systems and processes accordingly, ensuring ongoing compliance with evolving requirements. By leveraging Generative AI, financial institutions can enhance their risk management practices, minimize penalties and legal risks, and maintain a strong reputation for regulatory compliance. Moreover, the use of generative AI to train machine learning models with synthetic data improves fraud prediction accuracy, reducing financial losses due to fraudulent transactions. By minimizing false positives and false negatives, financial institutions can efficiently identify and block fraudulent transactions while minimizing the disruption to legitimate customer activities.

What is ERP finance?

In the context of finance, VAEs work by encoding the input financial data into a lower-dimensional latent space representation. The encoded data is then decoded back into Intro to Bookkeeping & Special Purpose Journals the original data space, reconstructing the input data. It does this through repeated simulations (via trial and error) with a reward structure for good outcomes.

When a threat is detected, Generative AI-powered systems can initiate immediate response mechanisms, such as isolating affected systems, blocking malicious IP addresses, or alerting security teams for further investigation and remediation. The banking industry faces numerous challenges regarding compliance and regulatory reporting. Financial institutions must adhere to a complex web of regulations and guidelines imposed by regulatory authorities. Compliance involves ensuring that operations, transactions, and practices comply with applicable laws and regulations, while regulatory reporting entails submitting accurate and timely reports to regulatory bodies.

In theory, it could act as a safeguard by testing the veracity of the data provided by the Oracles and prevent Oracle manipulation. Nevertheless, the introduction of AI in DLT-based networks does not necessarily resolve the ‘garbage in, garbage out’ conundrum as the problem of poor quality or inadequate data inputs is a challenge observed equally in AI-based applications. 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.

There must be a mechanism to instantly locate anomalies throughout the entire pipeline, pinpoint the problem, and resolve it. That’s exactly why some businesses are built around this idea and offer git-like version control for even their own data. In practice this means that several isolated instances of incorrect or biased data supplied into a trading algorithm can have catastrophic effects on the entire system and result in losing trades and money.

AI finds application in enabling better credit systems by developing a system where lenders can more correctly determine a borrower’s risk with the aid of AI regardless of the social-demographic conditions. Over the 2020–2030 decade, global spending on AI is anticipated to double, rising from USD50 billion in 2020 to more than USD110 billion in 2024. A recent forecast by Business Insider shows that AI in finance can save banks and corporate institutions $447 billion by 2023. The COVID-19 global crisis has accelerated and heightened the digitalization trend, including the application of AI in the finance industry. Well, if you can’t think of at least one AI application in finance, you must have been living under a rock.

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IGAFN integrated heterogeneous credit data, addressing the data imbalance issue and outperforming other methods in credit scoring. These studies demonstrate GANs’ efficacy in credit card fraud detection and their potential for enhancing risk assessment in the financial sector. It’s difficult to overestimate the impact of AI in financial services when it comes to risk management. Enormous processing power allows vast amounts of data to be handled in a short time, and cognitive computing helps to manage both structured and unstructured data, a task that would take far too much time for a human to do. Algorithms analyze the history of risk cases and identify early signs of potential future issues.

Generative AI can be employed to simulate cyber-attacks and test the effectiveness of security systems. Using advanced algorithms, Generative AI can replicate various attack scenarios, including malware infections, phishing attempts, and network intrusions. These simulations enable financial institutions to assess the vulnerabilities in their systems, identify potential security gaps, and enhance their defenses. Generative AI-driven cyber-attack simulations provide valuable insights into the effectiveness of existing security measures and aid in developing proactive cybersecurity strategies. By offering personalized experiences, financial institutions can deepen their relationships with customers.

The platform acquires portfolio data and applies machine learning to find patterns and determine the outcome of applications. Automating financial processes relies on artificial intelligence’s ability to gain insights from existing data to optimize credit decisions, risk assessment, and auditing, among others. Generative AI facilitates personalized product recommendations and offers, benefiting both customers and financial institutions. By analyzing customer behavior, preferences, and transaction history, generative AI algorithms can generate tailored product recommendations, such as credit cards, loans, insurance policies, or investment products. These personalized recommendations help customers discover relevant products that align with their needs, increasing the likelihood of customer satisfaction and conversion.

Generative AI can be used to process, summarize, and extract valuable information from large volumes of financial documents, such as annual reports, financial statements, and earnings calls, facilitating more efficient analysis and decision-making. Banks want to save themselves from relying on archaic software and have ongoing efforts to modernize their software. GenAI models can convert code from old software languages to modern ones and developers can validate the new software saving significant time. In this article, we explain top generative AI finance use cases by providing real life examples.

• Contact LeewayHertz, and our expert team will help you harness the power of Generative AI to improve your business processes.
• This protects the institution’s financial interests and ensures a smooth and secure experience for customers.
• As AI techniques develop, however, it is expected that these algos will allow for the amplification of ‘traditional’ algorithm capabilities particularly at the execution phase.
• The Snowfox.AI service can route and post your purchase invoices automatically with artificial intelligence.
• These intelligent systems track income, essential recurring expenses, and spending habits and come up with an optimized plan and financial tips.

Similarly, AI applications can improve on-boarding processes on a network (e.g. biometrics for AI identification), as well as AML/CFT checks in the provision of any kind of DLT-based financial services. AI applications can also provide wallet-address analysis results that can be used for regulatory compliance purposes or for an internal risk-based assessment of transaction parties (Ziqi Chen et al., 2020[26]). The significance of generative AI in financial services lies in its ability to generate synthetic data, automate processes, and provide valuable insights for decision-making.

What is machine learning?

This protects the institution’s financial interests and ensures a smooth and secure experience for customers. Generative AI-generated transaction data can be used to train machine learning models specifically designed for fraud prediction. By incorporating the synthetic data into the training process, these models can learn from a wider range of fraudulent patterns, improving predictive capabilities. Machine learning models trained with generative AI-generated data can detect fraudulent activities more accurately, reducing false positives and negatives. This leads to more efficient fraud detection and a lower impact on legitimate customer transactions. Generative AI can automate document verification and risk assessment processes in loan underwriting.

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.

When automating finance processes with artificial intelligence, you first need to identify the parts that are sensible to automate. Reviewing the components of each task and step will help you to determine whether it is a suitable candidate. Financial administration processes like data entry, processing, and analysis, can be streamlined with AI. AI and blockchain are both used across nearly all industries — but they work especially well together.

By analyzing vast amounts of data, including customer interactions, historical data, and relevant knowledge bases, generative AI algorithms can generate responses that are tailored to the specific query and the customer’s context. This personalization and contextual understanding level enable virtual agents to provide accurate and relevant information, improving the overall customer experience. GANs have emerged as a powerful tool for credit card fraud detection, particularly in handling imbalanced class problems. Compared to other machine learning approaches, GANs offer better performance and robustness due to their ability to understand hidden data structures. Ngwenduna and Mbuvha conducted an empirical study highlighting the effectiveness of GANs and their superiority over other sampling models. They also compared GANs with resampling methods like SMOTE, showing GANs’ superior performance.