According to Thompson Reuters, more than one-third of professionals in regulated industries are hesitant about GenAI, and nearly half have no plans to use the technology. While GenAI has enormous potential to revolutionize the way we all work, this uneasiness is perfectly logical, especially for the finance industry.
GenAI hallucinations have made countless headlines, from a chatbot selling a car for $1 to Google’s Bard presenting false information in a demonstration. The consequences of such mistakes can be catastrophic for financial professionals handling people’s money. Data security, confidentiality, regulatory compliance and integration with legacy systems present additional concerns for this industry.
These hurdles do not mean that GenAI is a nonstarter for financial institutions. To effectively use this technology, organizations must leverage “Safe AI” principles and maintain human oversight.
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The Components of Safe AI
Safe AI is an approach that prioritizes accuracy, compliance and trust by leveraging controlled data sources, robust security measures and rigorous monitoring processes to deliver reliable and contextually appropriate outputs.
We’ve all heard the AI adage “garbage in, garbage out,” meaning data quality directly impacts AI accuracy. Financial institutions must ask, “What makes quality data?” The following aspects are critical for successful AI implementation.
- Completeness: Datasets must contain all fields and records required to support AI processes and compliance requirements. Missing or incomplete data can lead to biased results and unreliable model outputs.
- Consistency: Data must maintain uniform formats, naming conventions and values. Variations alter how an algorithm interprets the information.
- Accuracy: Data must be free from errors and inaccuracies. A misplaced decimal or incorrect calculation will skew the tool’s answers.
- Relevance: Data must directly apply to the business goals or AI use case. Extraneous information dilutes output and can contribute to hallucinations.
Large language models (LLMs) can contain irrelevant and unverified data that negatively impacts performance and creates significant risks in highly regulated environments. Financial institutions should fully control what data AI accesses during training and use. Ideally, platforms will be trained exclusively on compliance-approved documents, current regulations and internal knowledge bases to deliver precise and accurate outputs.
Exclusion mechanisms are critical in highly regulated environments. By adhering to the minimum data necessary principle, organizations limit the amount of sensitive data they process to refine answers and reduce the risk of data breaches and regulatory fines. Implementing zero-retention architectures prevents the storage of personally identifiable information beyond its immediate use. Additionally, masking sensitive data during AI interactions adds another layer of protection.
Retrieval-augmented generation (RAG) supports data control by combining language models’ generative capabilities with real-time access to specified, vetted datasets. Instead of relying solely on the model’s training data, RAG pulls current, context-specific data to reduce hallucinations and inaccuracies. Limiting the retrieval process supports compliance with regulatory standards, like SOC 2 and GDPR, and gives organizations control over data use.
Internal monitoring and quality assurance tools detect and prevent inaccuracies and biases in AI’s output. Regular audits and third-party assessments monitor compliance with industry standards, identify potential risks and inform necessary strategic adjustments. Continuous evaluation ensures that AI systems remain reliable, accurate and aligned with business goals.
Don’t Forget the Humans
AI can automate routine tasks and handle a significant portion of customer interactions, but regulated environments often require nuanced decision-making, empathy and an understanding of complex scenarios.
Humans should always review AI decisions in critical situations. For example, an account balance request probably doesn’t need oversight, but a real person should scrutinize a credit risk assessment. Customer-facing AI tools should operate within clearly defined boundaries, seamlessly transitioning to human intervention when they reach the limits of their knowledge or encounter complex or sensitive cases.
The Art of Incremental Innovation
Taking a “Big Bang” approach to GenAI implementation will almost certainly cause chaos. Financial institutions must be slow and deliberate with adoption to reduce its risks.
With incremental innovation, organizations make small changes that allow them to use existing systems and platforms, minimizing operational disruptions and building on what already works. This gradual approach spreads out expenses while keeping risks manageable.
For instance, financial institutions could begin by focusing on service optimization through the automation of simple tasks and inquiries or after-hours support before gradually scaling to more complex applications such as personalized product recommendations.
Teams can test and refine new features based on actual performance and user feedback, scaling up successful elements while adjusting or pausing others as needed. Gradual implementation also reduces employee and stakeholder resistance.
AI isn’t a one-size-fits-all solution. Financial institutions have very specific needs. Through design partnerships with tech providers, these organizations can build AI solutions aligned with business objectives, compliance requirements and operational realities. These collaborations will pinpoint practical, low-risk use cases that deliver measurable value and ensure that AI tools are scalable, compliant and evolve alongside the institution’s needs.
AI Delivers More than Cost Reductions
GenAI solutions offer far more than cost savings — they have the power to drive significant revenue growth. When leveraged well, these solutions increase deposits, expand wallet share and retain customers by:
- Enhancing the customer experience.
- Proactively engaging dormant accounts.
- Building customer loyalty.
- Identifying opportunities for cross-selling and upselling.
Revenue growth won’t happen overnight. While the journey requires experimentation, incremental progress and patience, GenAI’s cumulative impact can redefine how financial institutions operate, compete and serve their customers.
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