No one believed India when it came up with a revolutionising technology for financial payments. Many global experts refused to acknowledge India’s role in how AI will change our lives forever. Yet, we witnessed India hosting the world's biggest AI summit.
In the midst of this, something interesting is how AI in fintech is not being talked about enough. Fintech as we know it today, has gone through waves of innovation. Right from better UX, faster KYC, lower costs, to broader product access across masses. We have seen the unprecedented growth and reliability of Bharat Stack in real time.
The next optimal step anyone who builds a real solution for a real problem would look over to find the critical pieces in the product where AI adds value.
This reminds me of Andrew NG’s quote - AI is the new electricity.
Let’s think through it. Take a chariot pulled by horses.
Anyone can pass the electricity into a series of bulbs and decorate the chariot. The one with more money adds more advanced and colorful bulbs. Though that makes it more attractive, it remains the same old one. No speed up, no sturdiness up, nothing solved.
Where else can I use the electricity I got? Would you appreciate it if I replace the whip with an electric rod, so the horse can be more effectively controlled? I doubt it.
I should build motors in which I pass the electricity to make the wheels spin faster. Stop there? No, we should also build stronger wheels and body for the chariot to withstand this new speed.
And that is how we are building AI in Qonfido.
1. Research on Real Investor Behavior
The insights we use are not produced from comparing the result of multiple AI tools. We deeply analysed user queries received by financial advisors and in online personal finance conversations to identify core insights that users commonly need.
Our research told us that: :
- ~65% of queries are for personalised guidance, not generic education.
- Portfolio insights , efficient investing, and goal alignment dominate user queries.
- Everyone wants to know ‘Am I doing OK with my finances?’
These behavioural insights became the foundational layer to our design.
2. Can AI pass a Financial Certification Exam?
It’s easy to evaluate AI performance against a handful of common questions or gut-checks, but we want a more rigorous standard. So we benchmarked our AI performance against professional certification exams requirements that human financial advisors have to pass.
It was impressive to see the ease with which existing frontier AI models were able to pass a majority of the exam requirements, often with minimal tuning.The more interesting part was where they failed.
Failure modes are design signals.
Every erroneous answer revealed something about how AIs reason in complex financial conversations.
They ace in textual interpretation, mapping, sequential reasoning, explanation and logic planning. But stumble with multi-stage financial math, and often mis-select or hallucinate under heavier reasoning load.
This is the crucial phase which stopped us from replacing the whip with an electric rod.
So we separated concerns.
We built deterministic skills for financial multi-stage analysis, which can be rigorously tested for accuracy and edge cases. Agentic AI systems can then predictably perform against complex queries and generate insights that are explainable. It is not AI versus systems. It is AI amplifying human expertise.
3. The Critical Architecture Behind our Financial Nerve System
Any LLM can feed on the past behavior data and answer questions. But your financial history is remembered as any Wikipedia blob you copy paste and interrogate. That is not enough.
We needed a memory architecture that retains data at different levels and forms specially curated for finance advice, and which should be fast and accurate. An agentic engine that can pull relevant context depending on the user interactions , recency, frequency and behavioral intent.
The memory does not just hold the user's persona but also the market and regulatory insights . Market insights are brought into the system to tune the AI response and meet compliance guardrails
Regulatory conditions are regularly parsed into guardrails for gatekeeping LLM responses.
The Result
When AI is embedded this way, it becomes a compounding layer of intelligence instead of just a conversational add-on.
If you want to take a closer look at what we are building at Qonfido,
https://www.qonfido.com/home#signup
4. Agents That Build, Not Just Respond
We don't just deploy AI for customers. We deploy it internally to accelerate how we build.
internal agents are designed to understand system primitives- projections, ranking models, compliance layers and ship new capabilities faster without compromising architectural integrity.
AI scales expert throughput. Experts guide and continuously refine the system. Together, they compound.
5. Designing for Reliability in an Uncertain AI Landscape
One of the biggest misconceptions in fintech AI is treating the model as the product.
Models will evolve. Providers will change. Latency, costs, and reliability will fluctuate.
A resilient fintech system must:
- Route across any LLM providers
- Fall back to deterministic engines — when arithmetic matters, don't rely on probabilistic systems
- Store what is said to the user — every claim must be traceable
- Monitor performance and regulatory drift continuously — because both change over time
- Maintain safe modes during uncertainty — if the AI layer fails, the system doesn't collapse
When AI is embedded thoughtfully within financial infrastructure, customers don't feel disruption.
They get a Qonfidant who sails with you through the journey, learns your finance nerve, monitors the market and your portfolio to proactively guide towards defined goals.
If you want to take a closer look at what we are building at Qonfido,
Qonfido
@Qonfido