Personalized AI in Finance is coming

What to look for in 2026, the year of Personalized Financial Intelligence

The last thing the finance world needs is another expensive ChatGPT.

Next year will see a turning point and AI will finally make a significant impact on the financial sector. Drawing on our experience from Google DeepMind to some of the world’s top investment banks and asset managers, we will venture to make some predictions, or rather, observations regarding the evolution of AI in finance. Some might come as a surprise or a welcome change to professionals who have been, thus far, unimpressed with the state of AI in finance. 

Standard AI models like ChatGPT and Claude, trained on an ever-growing body of public data, have failed to penetrate the finance sector in the same way they swept across the mainstream. Why? In short, finance knowledge workers don't care for “close-enough” and generic responses are not helpful in the context of finance workflows. Personalization means the difference between utility and noise, but it’s exceptionally difficult to get right in areas like banking, sell-side research, and investment management.

Having said that, we see a major change coming to AI utilization as we turn the calendar this year. We will see AI finally making a difference in finance in 2026 by becoming hyper personalized, private to the individual user, proactive, and highly accurate. These four qualities, which have been largely missing from general AI models, will win the trust and mind share of the finance professionals and finally deliver on the AI promise for finance. 

In 2026, AI will be more personalized and proactive

Generic responses that feel regurgitated are going to be left behind in 2025. The next generation of financial AI won’t simply answer general questions but will know how you work, pulling answers with the right context and the right data, taking you all the way from prompt to the end deliverable. 

The finance professional’s most valuable information is not on public web pages like Reddit or Wikipedia, but in private data: internal notes, proprietary models, third-party data, expert networks, CRM entries, meeting transcripts, client information sitting in a proprietary database behind a firewall and without that context, AI can’t be used to the fullest.

But in 2026, personalized AI will bridge that gap by becoming an integrated part of your systems, sitting on top of your most valuable data assets and using them as a tool to create value like never before. It will learn not only from public filings and disclosures, but from the key data that defines how your firm operates.

Very soon, you will have an AI assistant that:

• Pulls figures from the latest 10-K but cross-references them against your internal notes and alerts you to things you might have missed in your investment thesis or research report.

• Understands what you mean when you say “DCF,” and “P&L” because it knows you and the lexicon you use in your models.

• Knows the difference between what’s “interesting” and what’s “investment-worthy” based on your own particular line of thinking.

This new class of AI will act less like a chatbot and more like a colleague heightening your situational awareness, alerting you to actions you can potentially take, creating artifacts for you before you even know you need them and challenging your assumptions making sure there you no blind spots in your approach

It will proactively act like a true sparring partner and critique your thesis the way a generic model can’t:

“Your comp set excludes a peer that just reported margin expansion and your model is overstating leverage vs. a new filing.”

And when it’s time to present your report, memo or thesis, it will already know how your firm wants to see it. It knows the format, the fonts, the ordering, the framing, the content.
This is what’s just around the corner: AI that understands your data, your process, and your workflows. Like a partner trained on the living knowledge base of your firm, who will not judge or complain, but simply work.

We see this shift finally making AI indispensable in finance: not its ability to chat, but its ability to think and execute with the right context.

In 2026 AI in Finance will be accurate and private

Accuracy and privacy will define the next stage of AI penetration in finance. AI needs to win the trust of finance professionals, who currently have legitimate concerns about hallucinations and compliance issues. This is one of the biggest reasons why banking and asset management have been so slow to adopt AI, and this is all about to change. 

2026 will see a new kind of AI with groundbreaking capabilities, but none of the advancements we have covered here matter without a solid foundation in accuracy, privacy, and trust. This is paramount in our field - trust comes first, features, a distant second. Once trust is established, it frees your AI assistant to truly add value to your daily workflow.

Imagine AI that you can finally rely on to be accurate and private. Every output is cited, all the data is auditable, and the sources are transparently linked down to the page and paragraph it came from. Every data trail is transparent, showing exactly which filing, transcript expert, or dataset informed the analysis. This new type of AI makes tracing the journey of the data from source to deliverable effortless and natural. Once you eliminate the fear of hallucination, you can finally rely on the AI as you would a trusted colleague.

When your AI produces a comp table or summary, you’ll click a number and trace it back to its source in a filing in real time. You’ll know which private datasets or analyst notes drove the conclusion.

Just like the data you have collected, your AI will feel like your own in 2026. The new system will combine accuracy with governance, have audit trails, version control, and full explainability, so every insight can be corroborated on demand.

Finster is building the future of finance AI today

We said that these were less predictions and more observations. We know this will happen across the entire sector because we are building it. Seeing it deployed in real time and watching it change existing workflows at scale leaves no doubt. This is not a distant future, it's already here.

When we started Finster, we set out to build a new kind of AI, one that will be worthy of trust from even the most skeptical knowledge workers. What we’ve built is an AI that knows what financial professionals need, when they need it, before they even know they need it. 

We are building the AI that finally delivers on the promise to make complex workflows more efficient, accurate, and streamlined, no matter how archaic and complex they are today. 

Join a growing number of banks and investment management firms and step into 2026 before the New Year ball drops.

Reach out to the author: [email protected]