Why You Need a Gen AI Strategy [for Your Financial Services Business]

Learn how implementing an org-wide Gen AI strategy can help FSI leaders mitigate risk while building trust, compliance, and transparency.
December 1, 2023
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The past few years have seen a meteoric rise in generative artificial intelligence (Gen AI) capabilities. Complex processes and viable content can now be easily generated with just a few simple prompts, which means it’s more important than ever for your financial services business to create and implement a firmwide AI strategy and internal policy for what is and isn’t appropriate regarding Gen AI usage. In order to make a clear and consistent set of standards for your business, it’s important to understand how your employees use Gen AI and the risks inherent in that use. 

Given the sometimes precarious terrain of the sector, including potential budget cuts and recent rulings by the Federal Trade Commission (FTC) allowing them the use of compulsory measures in nonpublic investigations related to AI use, it is also imperative that CXOs and other C-Suite executives take ownership of AI-driven business outcomes. This underscores the importance of a comprehensive AI strategy that goes beyond the automation of technical tasks and processes to account for the ways that AI will influence a business’s operating model.

The Risks of Gen AI Without an AI Strategy

The appeal of Gen AI is that it can save your employees valuable time on simple tasks so they can devote more of their attention to more pressing matters. Your employees’ use of Gen AI may be as simple as using Chat GPT to draft a quick email reply or asking how to do a complex task in Excel. Alternatively, it could be as complex as inputting a large data set and searching for trends. One way or another, your employees are very likely to be using Gen AI regularly, so it’s important for you to be transparent about what is and isn’t appropriate.

Imagine a scenario in which an employee copies and pastes a spreadsheet of customer addresses and account histories into a large language model (LLM) application. The employee is attempting to quickly check the data for regional trends. While this may seem harmless on the surface, the question is what happens to that customer data once it’s input into a search. 

The simple act of doing so could itself violate privacy laws, as the employee is giving sensitive information to a third party without the clients’ consent. Also, LLMs learn by collecting and analyzing data, meaning there is a risk, however slight, of the LLM repeating this information back to another user.

There are also numerous ways that hackers use Gen AI to steal sensitive information, such as prompt injection, in which hackers can remotely input code into the Gen AI app as a way of tricking another user into sharing information, which can then be stolen. This is especially dangerous in industries like FSI, where datasets contain large volumes of sensitive financial information and where failure to comply with privacy and security standards can have far-reaching consequences.

Transparency and Trust

Having a transparent policy of how Gen AI technologies will be used can also increase your customers’ trust in your business, letting them know that their data will be treated responsibly. You can also better keep all your messaging and communications consistent and accurate by vetting all AI-generated content. Remember, the algorithm doesn’t know your values–it can only summarize strings of text by means of probability. While Gen AI can be a convenience, it still needs human oversight.

Since Gen AI and LLM content is the result of the analysis of vast amounts of material, the Gen AI app itself can only be as good as its training data, which is to say that it will share any biases that are common in the source data. This could lead to those biases being reflected in your company’s AI-generated materials.

Garbage In, Garbage Out

In addition to accounting for biases in an AI tool’s data source, it is imperative to make strategic choices about the quality of the data on which it is trained. For businesses looking to use Gen AI tools to perform analysis on internal data, it is imperative that the data in question is clean, of high quality, and well-governed

Training your AI model on data that is accurate, consistent, and complete will improve the quality of its outputs by minimizing bias, enhancing generalization, and reducing the occurrence of AI hallucinations. In other words, your Gen AI strategy is only as effective as your data strategy.

What Should Be Included in a Gen AI Policy

Your Gen AI policy needs to be specific, thorough, and enforceable. While crafting a Gen AI policy, you’ll need to understand the ways Gen AI content is utilized by your employees and foresee both the negative and positive aspects.

You should differentiate between vetted, trustworthy Gen AI applications, including those your organization models internally, and those that could lead to data breaches or unethical use of customer data.

You’ll need to provide guidelines for when the usage of Gen AI is acceptable, and to what extent. Explain how employees should attribute work created by AI. Provide resources for more effective usage of and understanding of Gen AI workflows. 

On the other hand, you’ll also need to clarify when it is inappropriate to use Gen AI, contextualizing how Gen AI apps use data and the risks posed to both customers and employees by inputting sensitive information into one.

You’ll also need to create reasonable consequences for the intentional, unethical use of Gen AI to hold your entire organization accountable to this policy.

Creating Your Gen AI Strategy with Hakkōda

Recent statistics show that a majority of employees are already using Gen AI in the workplace. Gen AI can be a powerful tool for increasing productivity and automating time-consuming tasks, but it is important that organizations are deliberate about its use. 

At Hakkoda, we acknowledge the unique nature of emerging Gen AI technologies and have developed our own quick start roadmap to guide clients through a productive AI integration. This strategy helps our customers account for accuracy, explainability, privacy, security, intellectual property, fairness, and compliance when modeling, training, and deploying artificial intelligence, ensuring that their AI business integration is responsible, efficient, and scalable.

Ready to explore how you can craft a Gen AI strategy that balances workplace efficiency with trust, compliance, and transparency? Let’s talk.

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