Generative AI redefines business processes in every industry that deals with documentation and content generation. Previously, we have discussed the use cases for Generative AI in the healthcare industry. Today, let’s discuss the top 5 use cases for this artificial intelligence technology in the finance industry.
The finance industry is already using AI extensively to automate processes – we call this application of AI autonomous finance. Generative AI strengthens autonomous finance by augmenting its capabilities. Leading financial companies such as Morgan Stanley and Stripe have already implemented Open AI’s GPT-4-based solutions in their operations, including customer support and fraud detection.
Top use cases for Generative AI in finance:
Fraud prevention
How big is the problem of financial fraud? A study reported that 70% of financial institutions lost at least half a million dollars in 2022 alone – CFO. Fraudulent transactions is a growing problem for financial institutions.
Due to the sheer volume of transactions happening over their networks, most fraudulent transactions go unnoticed. In most cases, institutions may have already experienced significant damage by the time fraudulent transactions are detected. Another pitfall helping fraudsters is the fragmented data management in institutions.
Generative AI helps financial organizations control fraud. You can generate examples of fraudulent transactions using synthetic data to train and augment the large language models. These trained models can distinguish fraudulent patterns in transaction data from legitimate ones. Thus, you can detect fraud faster and prevent losses to your organization.
Generative AI also analyzes large volumes of financial data and identifies patterns indicative of fraudulent activities. By learning from historical data, generative AI models can detect anomalies and flag suspicious transactions. This will help you combat fraud and boost customer trust.
Risk assessment
Risk is inevitable for financial institutions. They face operational, market, compliance, and credit risks, among many others, making risk assessment critical for their operations. Poor financial risk assessment results in increased load default rates, market volatility, investment losses, ineffective hedging strategies, and compliance issues.
Financial risk assessment failures can lead to global catastrophes such as the 2008 crisis. Following the great depression, the US Financial Crisis Inquiry Commission highlighted that a combination of excessive borrowing, risky investments, and lack of transparency put the financial system on a collision course with crisis.
Financial institutions face several challenges in risk assessment and portfolio optimization. Mainly due to the vast amount of data spread across systems, lack of access to historical data, and changing regulations.
Generative AI can help financial institutions assess risk and optimize investment portfolios. By analyzing historical data, generative AI models can simulate various market conditions, identify potential risks, and recommend optimal portfolio allocations to achieve desired risk-return profiles.
Customer service
Modern-day customers prioritize customer service. In finance matters, customers expect quick resolutions to their problems. However, manual processes, limited resources, and complex financial products often hinder institutions from meeting these expectations. This is where Generative AI emerges as a game-changer.
Generative AI has the potential to revolutionize customer service in the finance industry by leveraging its advanced capabilities in natural language processing and machine learning. With the ability to analyze vast amounts of data, Generative AI models can generate personalized recommendations, financial advice, and product offerings based on individual customer preferences and financial goals. By understanding natural language queries, generative AI can provide tailored responses to customer inquiries, ensuring that they receive the relevant information they need promptly. This not only improves customer satisfaction but also enhances customer engagement and loyalty.
Generative AI-powered customer service in finance can also address the challenge of scalability. With traditional customer service methods, scaling operations to handle a large volume of customer inquiries can be cumbersome and time-consuming. Generative AI enables process automation by automatically generating responses and recommendations, enabling financial institutions to handle a higher volume of customer requests efficiently.
Moreover, Generative AI models continuously learn and improve over time. They can adapt to evolving customer needs, market trends, and regulatory changes. This ensures that the recommendations and advice generated remain up-to-date and relevant, providing customers with accurate and valuable insights.
Underwriting
Underwriting in finance encounters challenges in data quality, privacy, and security. Obtaining accurate and comprehensive data points can be difficult, while regulatory compliance and protecting customer information add complexity. The evolving risk landscape requires underwriters to stay informed about emerging risks. Complex risk assessments, subjective judgments influenced by biases, and technological integration pose further challenges. Overcoming these hurdles enables financial institutions to enhance underwriting accuracy, pricing decisions, and risk management.
AI-powered underwriting algorithms leverage extensive data analysis, including customer information, claims history, and other relevant data, to identify risk factors and make predictions about future claims. This advanced underwriting approach enhances accuracy, enabling insurers to set policy prices more precisely. By avoiding underpriced policies, insurers mitigate the risk of financial losses.
Financial report generation
Report generation is crucial in financial institutions for effective communication of complex financial information to stakeholders and regulators. Reports provide insights into financial performance, risk exposure, and compliance. They support informed decision-making and improve transparency.
Financial institutions face challenges in report generation, including manual processes that are time-consuming, error-prone, and resource-intensive. Consolidating data, ensuring accuracy, and meeting customization needs are complex tasks. Real-time reporting puts further pressure on financial institutions.
Generative AI improves report generation for financial institutions by automating the process. Using NLP and machine learning, AI models analyze data, extract relevant information, and generate comprehensive reports quickly. This reduces errors, ensures consistency, and adapts to evolving regulatory requirements, enhancing efficiency and decision-making capabilities.
The applications of generative AI in finance go beyond these five use cases. You can create generative AI-powered applications on top of your tech stack using cloud services like Azure OpenAI Service and Azure Cognitive Search.
Not sure where and how to get started with generative AI for your finance organization?
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