Large Energy Requirement

Generative AI in financial services often requires significant computational power and energy consumption. The complex algorithms and foundational models used in genAI can put a strain on the resources needed to train and deploy these systems, leading to increased costs and taxing of other internal resources.

Poor Input, Poor Output

The quality of the data sets used in generative AI models directly impacts the quality of the responses and insights generated. In financial services institutions, where accurate and reliable data is crucial, poorly reported data can lead to inaccurate or unreliable outputs, resulting in significant miscommunications or falsified results. It is essential to ensure that the input data used in generative AI models is of high quality and is properly validated and vetted to mitigate this risk.

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Cybersecurity Threat

Generative AI systems in financial services can be vulnerable to cybersecurity threats, as they rely on large amounts of data that could be susceptible to hackers and malicious actors. Breaches in the security of these systems can lead to unauthorized access to sensitive financial information, financial fraud, and other cybersecurity risks. Robust cybersecurity measures and constant monitoring are necessary to protect their integrity.

Governance and Regulatory Compliance

The use of generative AI solutions in financial services raises governance and regulatory compliance challenges. Institutions need to ensure that their actions comply with industry regulations and guidelines. This includes considerations such as transparency, explainability, and fairness in the decision-making processes of generative AI systems. Adhering to governance and regulatory requirements is crucial to maintain trust and mitigate potential legal and reputational risks.

Data Privacy & Security

Any genAI tool relies on vast amounts of data, including sensitive and personal information, which means ensuring data privacy and security is of utmost importance to protect the confidentiality and integrity of this information. Financial institutions must implement robust data protection measures, including encryption, access controls, and data anonymization techniques to safeguard the privacy of individuals and comply with protection regulations.