You already know that artificial intelligence (AI) can personalize and enhance your customer’s end-to-end experience, which could go a long way in building brand loyalty and ROI. And right now, we’re in a gold rush of new ideas and techniques for improving user experiences (UX) using this technology. Especially if you’re a legacy enterprise trying to keep up with other fintech, you’re probably feeling the pressure to reinvent your tech stack. The advent of ChatGPT, in particular, makes you feel like you’re racing to the finish line to keep up with other fintech brands.
There’s just one problem: it’s not secure. In fact, depending on the large language model (LLM) and its data source, your customer data — and your company’s — is at risk of exposure.
Fortunately, there are workarounds that keep your proprietary information safe as you integrate this AI. Here’s everything you need to know about generative pre-trained transformers (GPT) risks when done wrong, and its rewards when done right.
The risk of data exposure with GPT
If you don’t do your due diligence, your GPT could expose proprietary data, and the legal and reputational hits could destroy your legacy.
But before we go any further, let’s get one thing straight: ChatGPT is not the same as a GPT with customized integrations. ChatGPT is way less secure, and while it’s a brand that falls under the umbrella of LLMs, a lot of people use it as shorthand for GPT. Not here. Namely, the former has open source data while the latter doesn’t. And that modification can change everything.
Where GPT can create opportunities and solutions
With those risks in mind, there are still plenty of opportunities for secure GPT functionality. And there is an ever-growing list of use cases.You just have to pinpoint which ones make sense for your business. Here are a few areas where it can provide an assist.
Personalizing your customer experience
GPT interfaces can automate conversations with your customers so they feel like they’re talking to a financial professional. Chatbots can guide users through processes like onboarding, applying for a loan, or building an investment portfolio.
You can also create biometric profiles using AI, which add another layer of security to the experience. On a more creative level, GPT allows you to modify the voice or tone of a chat feature so that it matches your brand.
Risk management
The right machine learning (ML) algorithm can identify market risk, credit risk, and fraud. And with GPT, you can intervene before a customer gets conned. Not only does this protect your most vulnerable users, but it builds confidence in your brand.
Financial decision-making
AI’s risk assessment capabilities also mean it can optimize your customer’s portfolio, and assess which investments are worth the assumed risk. How? Put simply, AI has access to a wider database than ever before to calculate risk, all but guaranteeing improved outcomes.
How to build secure GPT for your financial services digital platforms
It’s definitely possible to integrate GPT into your experience and protect customers’ proprietary information with the right data source and algorithm. With these essential integrations, privacy and security is second nature, and neither you nor your customer will lose sleep wondering about potential data exposure. Here’s how you do it.
1. Create a vertex store
ChatGPT’s database is open source, which means that feeding it proprietary data potentially exposes that information to other parties. On the other hand, GPT with a vertex store, or secure database, can prevent data breaches by specifying which data the LLM can refer to. You have more control over where proprietary data is, how to access it, and what you can do to ensure your GPT elicits a correct response.
Let’s say you don’t use a vertex store and instead upload all your documents (including the private ones) into your LLM. One of those documents is a retirement calculation specific to your company’s strategy. Technically, if one of your users asked for that algorithm, the chatbot would be able to send it to them.
Instead, you want the GPT to only respond to the user with information that’s safe to share, such as a page that allows the user to calculate their retirement. Even if the user asks the bot, “What’s the math behind this?” or “How did you come up with this formula?” The bot would do everything that a human agent would do, including walk the user through their retirement calculation without exposing the actual steps it took for the tool to get there.
2. Keep iterating with AI
GPT is still in its “wild west” phase of development. And that’s tough because you won’t always know how best to leverage the technology to meet your needs while protecting sensitive data.
But there’s also the matter of keeping up with emerging technologies to compete in a saturated marketplace.
So be prepared to iterate and find new and better processes to secure your data and improve the experience. The overarching question to ask is: how can you leverage AI to improve your entire company’s experience, internally and customer-facing? Think creatively about potential strategies, even if you don’t have an exact roadmap to implementation. Doing so gives your institution a competitive edge.
One way to keep up with new use cases is to partner with a digital product consultancy like O3. O3 stays up-to-date on opportunities in AI and builds integrations that align with your customer needs and business goals.
Interested in learning more about how to integrate these use cases into your own financial products? Let’s chat.