Thanks to recent technological advances in generative artificial intelligence foundation models and record-breaking rates of consumer adoption, it’s no longer a question whether your company will use this technology. It’s a question of when and how.
Trained on enormous volumes of data and adapted to many applications, foundation models are more sophisticated, complex, and capable than prior AI tools, especially at handling unstructured data. Increasingly offered as a service, they are also much easier and economical to adopt. But concerns about unforeseen consequences and potential misuse of the technology make it urgent for business leaders to understand the privacy, fairness, ethical, and social implications of generative AI, and to balance those risks against its promising commercial potential.
Managing and mitigating the new risks that come with technological advance is familiar terrain for financial service institutions. Generative AI will amplify some well-known concerns but will also present new ones. For example, the risk of bias, long managed through fairness policies and compliance efforts, could now inadvertently be built into applications based on these models. The risk faced by any individual company will depend on two things: first, where and how it applies generative AI, and second, the maturity of its AI governance. Whatever their level of risk, any company using generative AI must identify relevant and emerging risks; understand how their applications map to existing and new regulations; and enhance internal functions, such as machine learning engineering, technology, and legal, in anticipation of new risks.
Financial services applications of generative AI
Generative AI has the potential to significantly improve the productivity and quality of many types of knowledge work, increase revenue, and reduce costs. Consequently, financial service organizations are likely to use it in a variety of ways. These may include augmenting the productivity of their workforces, personalizing content for consumers, and, eventually, improving consumer self-service. Traditional AI has already been used extensively in financial services, typically with structured data for prediction and segmentation. Today’s foundation models could be used for converting unstructured data—like text, images, and audio—as well as data sets—such as communications, legal documents, and written financial reports—into structured data, which could then be used for strengthening these existing AI risk models.
The breadth and scale of generative AI’s likely uses combined with its evolving social and ethical risks make creating and managing a comprehensive governance program complex (see Figure 1).
Generative AI foundation models carry new risks, and their scale and broad application augment existing risks
Regulatory, compliance, and legal risks: inheritance, ownership of training data, developing regulation, data privacy, IP ownership of created content, job displacement
Regulators are clearly still catching up to the rapid evolution of generative AI and foundation models. In the coming months, executives will have to watch for upcoming regulations and proactively manage them. These will come from existing regulatory bodies that are forming their perspectives, as well as from new regulatory entities that may be created specifically for this technology, such as those envisioned in the European Union’s AI Act.
Generative AI also exposes organizations to increased legal risk from inadvertent or unintentional exposure of customer data by employees experimenting on public or shared systems, uncertainties in the provenance of data used in training foundation models, and potential copyright risks on content generated using these technologies.
Additionally, the economic risks from regulatory noncompliance must also be considered—the draft European regulations are suggesting stiff financial penalties, similar to fines for noncompliance with data privacy regulations (GDPR).
Operational risks including data, IT, and cyber resilience and cybersecurity: data management and governance, fraud, adversarial/cyberattacks, vendor risk
Given the rapid pace of advances in generative AI, many features and capabilities are being launched to support experimentation. Until these solutions are hardened to support scaling, control privacy, monitor performance, manage security anomalies, follow data sovereignty, access regulations, and meet enterprise service levels, their commercial use must be very carefully considered.
Excessive complexity can make these systems brittle and more vulnerable to new vectors of cybersecurity attack, like training data poisoning and prompt injection attacks (see Glossary). It is likely, too, that the technology’s ease of use may enable the generation of malicious emails, phishing attacks, and “deepfakes” of voices and images, among other issues. Vendor risk relates both to locking into a “walled garden,” especially as the vendor ecosystem grows, and to the possibility that some vendors will not survive in this increasingly busy space. Open-source models may have their own complexity of maintenance and upgrades.
Model risks including fairness: hallucination, bias, accuracy, accountability, explainability, transparency
The financial services industry has well-developed policies of fairness, accuracy, explainability, and transparency built in compliance with regulatory guidelines. Generative AI intensifies some existing risks associated with AI while requiring a different approach to others. Given the large amount of data that goes into creating foundation models, for example, it is likely that bias will creep into some aspects of the data. And with foundation models mostly available as a service, new and derivative applications will inherit their risk of bias. Earlier machine learning models produced structured output for specific tasks, while generative AI creates novel results whose fidelity and accuracy can be difficult to assess. One particular concern: It can “hallucinate” output that was not present in its training data. That’s a desirable result when looking for innovative content, but unacceptable if presented without verification or qualification.
As with any new technology, unless planned correctly, generative AI initiatives run the risk of becoming expensive experiments that don’t deliver shareholder value. There is a risk of underestimating the extent to which an organization and its people will need to transform in order to realize the benefits of generative AI. Given the technology’s evolving nature, companies risk investing in the wrong technology or failing to hit the right balance between what they choose to build in-house and what they buy from outside vendors. Ultimately, every executive worries they might lose out to a competitor that deploys the technology in a way that is so appealing to customers it renders their current business model obsolete.
The tectonic shift generative AI is precipitating brings fear of automation and the potential impact on employment, employees, and society at large. Stakeholders, including customers, employees, and investors, have all demonstrated, as they have with ESG, that they place a high level of emphasis on social responsibility, and this technology will be no exception.
The five design principles of responsible generative AI
Building the organizational capability to responsibly design and deploy generative AI will require an investment of significant resources. By focusing that investment on five principles, companies can begin to mitigate risk and achieve their responsible AI goals while delivering on their strategic ambitions (see Figure 2).
Five principles of responsible AI
Generative AI is no longer futuristic but an imminent reality, one offering financial services leaders both unparalleled opportunities and new business and societal risks. Financial services firms can responsibly embrace this transformative technology by building robust governance frameworks and upskilling and reskilling employees to adapt to the AI-driven workplace.
This starts with a conscious decision to prioritize responsible AI practices that are designed with their broader impact in mind and aligned with the organization’s core values and long-term strategic objectives. By pioneering an appropriate model for deploying generative AI, financial services organizations have the opportunity to not only gain competitive advantage in an increasingly digital world, but also set an example of responsibility and foresight.