Finance: Research in Generative AI Use Cases and Risks
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- Himalaya Quantitative Solutions
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Benchmark of GenAI on Financial Applications
Applications of GenAI in Finance
Customer Personalization
One of the primary applications of GenAI in finance is providing personalized product recommendations and offers. By analyzing user data, financial institutions can generate unique suggestions for investment portfolios, financial products, and services. This capability extends to both customers and institutions, with 72% of customers believing products are more worthwhile when tailored to their individual needs. For example, generative AI can help firms deliver flexible and relevant conversations that improve the overall customer experience by adapting the conversational style to match that of the customer
Fraud Detection and Risk Management
GenAI plays a critical role in enhancing fraud detection and risk management in financial institutions. By generating synthetic examples of fraudulent transactions, GenAI can help train machine learning algorithms to recognize and differentiate between legitimate and fraudulent patterns in financial data. This capability allows for faster detection and prevention of fraud, ultimately improving the overall security and integrity of financial operations. Additionally, by learning from historical financial data, GenAI models can make predictive analytics about future trends, asset prices, and economic indicators, providing valuable insights into potential risks and opportunities
Automation and Operational Efficiency
The adoption of GenAI in finance has led to significant improvements in automation and operational efficiency. For instance, financial services institutions leverage machine learning (ML) technologies like computer vision, optical character recognition (OCR), and natural language processing (NLP) to streamline customer onboarding and know-your-customer (KYC) processes. GenAI can automate the generation of financial reports, thereby freeing up expert time for strategic analysis and reducing errors for greater accuracy. This automation extends to various back-office functions such as accounting, legacy software maintenance, document analysis, and responding to regulator requests.
Regulatory Compliance
Financial institutions are required to explain their decisions and actions to internal and external stakeholders, including prudential supervisors. GenAI can aid in fulfilling regulatory requirements, such as those related to anti–money laundering and combating the financing of terrorism. While many regulators have yet to publish guidelines specifically for the use of AI in financial transactions, financial institutions should take internal measures to update their anti-money laundering and client relationship policies and procedures when introducing AI technology to automate their customer-facing interactions.