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March 1, 2024

Balancing act: Ten promises and ten risks of generative artificial intelligence in finance

In the evolving world of finance, where innovation is the heartbeat of progress, generative AI emerges as a transformative force reshaping traditional decision-making and financial strategy patterns. Beyond its applications in creative fields, generative AI is making profound strides in the financial space. It offers possibilities that range from personalized investment recommendations to revolutionary advancements in algorithmic trading.

As businesses navigate this intersection of technology and finance, it becomes imperative to delve into the nuances and challenges of generative AI while exploring its potential and role in the future of financial services.

Understanding generative AI

A subset of artificial intelligence, generative AI is a technology that creates new, original content based on patterns and information it has learned from existing data. Unlike other forms of AI, which recognize objects or make decisions, generative AI focuses on creating new, human-like content. This content can include anything from text and images to music and videos.

The power of generative AI in finance

Generative AI is making significant inroads in the financial space, introducing innovative applications that enhance decision-making, streamline processes, and personalize services.

Some ways generative AI finds use in finance include:

Algorithmic trading

Generative AI analyzes vast datasets of market information to identify patterns and trends. This analysis can develop algorithms that automate trading decisions, optimizing investment strategies that respond to real-time market fluctuations.

Personalized investment recommendations

Generative AI can assess an individual's financial profile, risk tolerance, and investment goals to generate personalized investment recommendations. This level of customization provides investors with tailored advice that aligns with their unique financial circumstances.

Market analysis and forecasting

Generative AI models can analyze historical market data, news, and other relevant information to generate insights and forecasts. These forecasts help financial analysts and institutions make informed decisions about market trends, potential risks, and investment opportunities.

Fraud detection and prevention

Generative AI algorithms can identify abnormal patterns and behaviors in financial transactions. The technology assists with fraud detection by recognizing unusual activities and flagging potential fraudulent transactions.

Customer service chatbots

Generative AI in chatbots can interact with customers and help with routine inquiries, account information, and basic financial advice. Chatbots can understand and respond to user queries using natural language processing.

Credit scoring and risk assessment

Generative AI models can analyze extensive datasets to assess creditworthiness and calculate risk profiles for individuals and businesses. Financial institutions use this information to make accurate lending decisions and manage risk.

Portfolio management

Generative AI assists in optimizing investment portfolios by considering factors like risk tolerance, market conditions, and financial goals. Depending on the model results, institutions can adjust asset allocations to help achieve ideal returns.

Regulatory compliance

Generative AI can help streamline compliance processes by analyzing regulatory texts and relevant information. Financial institutions can use this information to adhere to complex regulatory requirements.

Market simulation and scenario analysis

Generative AI can simulate various market scenarios and assess potential impacts on investment portfolios. Financial professionals use these simulations to evaluate the resilience of their strategies under different economic conditions.

Credit card fraud prevention

Generative AI models analyze patterns in credit card transactions to detect unusual activities that may indicate fraud. This real-time analysis helps financial institutions prevent unauthorized transactions and protect cardholders.

Generative AI's versatility and ability to process large volumes of data make it a valuable tool in the financial sector. As technology evolves, businesses can expect further innovations and applications that enhance financial services’ efficiency, security, and personalization.

The challenges of generative AI

While generative AI presents numerous opportunities, it also comes with challenges. Some obstacles associated with the use of generative AI in finance include:

Data privacy and security

The extensive use of data in generative AI applications raises concerns about data privacy and security. Financial institutions must ensure robust measures are in place to protect sensitive customer information from potential breaches or misuse.

Bias and fairness

Fairness and mitigating bias in AI systems remains a critical challenge. Generative AI models may inadvertently perpetuate biases present in the training data. In finance, these biases can result in biased decision-making in areas like lending and credit scoring.


Many generative AI models have complex neural networks that make it challenging to interpret their decision-making processes. As financial applications rely on understanding the reasoning behind decisions crucial for regulatory compliance, clear transparency becomes essential.

Ethical use and accountability

The ethical use of generative AI, especially in financial decision-making, is a trend to monitor. Institutions can establish accountability through guidelines and frameworks that ensure responsible AI use.

Regulatory compliance

The financial industry is subject to strict regulations, and implementing generative AI solutions requires an institution to follow these regulations carefully. Navigating compliance requirements, especially in regions with evolving regulatory landscapes, poses a significant challenge.

Robustness to adversarial attacks

Generative AI models may be vulnerable to adversarial attacks, where intentionally manipulated inputs can lead to incorrect or undesirable outputs. Financial systems can secure the integrity of their decision-making processes by becoming resilient to possible AI incidents.

Lack of historical precedents

In finance, where historical data often informs decision-making, a challenge of using AI is a need for historical precedents for some AI-generated scenarios. These lacking areas add complexity in predicting and evaluating the potential risks of novel AI-generated insights.

Human oversight and decision-making

While generative AI can automate many tasks, human oversight still exists in the background. Striking the balance between AI-driven automation and human decision-making is key in scenarios where ethical considerations and regulatory compliance are paramount.

Integration with existing systems

Integrating generative AI solutions with existing financial systems can be complex. Legacy systems may require substantial efforts for AI integration and compatibility.

Education and skill gaps

Implementing generative AI in finance requires a workforce with the knowledge and skills to develop, deploy, and manage these systems. Addressing skill gaps and providing ongoing education for professionals in the finance sector is crucial.

Addressing these challenges is vital for realizing the full potential of generative AI in finance. With proper planning, institutions can ensure responsible and ethical use of AI that aligns with regulatory standards and societal expectations.

Managing the power of generative AI with Marsh McLennan Agency

In the dynamic realm of finance, where every algorithmic pulse echoes with implications, generative AI stands as a beacon of innovation and a challenge to conventional norms. In exploring the marriage of AI and finance, it becomes evident that a suite of ethical, technical, and strategic considerations accompanies the promises of generative AI.

The financial landscape is at the intersection of human expertise and machine intelligence, and how businesses navigate this juncture will define the trajectory of financial services in the years to come. As the finance industry embraces the transformative potential of generative AI, institutions can ensure that they commit to responsible and ethical implementation principles to forge a future that harmonizes technological prowess with the human touch.

Whether you're navigating investment decisions, enhancing risk management for your organization, or seeking personalized financial advice, Marsh McLennan Agency can help.

Reach out to a member of our Retirement & Wealth team today.