Naré Vardanyan, Co-Founder & CEO of Ntropy, a platform that permits developers to parse financial transactions in under 100ms with super-human accuracy, unlocking the trail to a brand new generation of autonomous finance, powering services and products which have never before been possible. It converts raw streams of transactions into contextualized, structured information by combining data from multiple sources, including natural language models, engines like google, internal databases, external APIs, and existing transaction data from across our network.
You grew up in Armenia, without electricity during a war. Could you share some details regarding these early days, and the way this led you to work for the United Nations?
That have was shared by a complete generation in Armenia. It fostered in me a way of imagination and the power to seek out solutions even with little means. Like others who grew up in a conflict zone, this era in my life had a profound impact on how I see the world. These demanding circumstances nurtured a way of shared responsibility throughout the community and a resolute drive to bring about positive change. Realizing that our challenges prolonged beyond individual struggles, I felt a calling to think on a broader scale and channel my endeavors. This, in turn, steered me towards the United Nations.
The UN emerged as the perfect platform to contribute meaningfully. Given Armenia’s precarious geopolitical position and my aspiration to influence global matters, I believed that collaborating with the UN would offer a possibility to actually make a difference. By being a part of consequential discussions and decisions, I aimed to have a meaningful impact on the world’s issues.
You soon became disillusioned with the United Nations, how did you then shift to wanting to work in tech?
The disillusionment with the UN was rooted in its slow and bureaucratic nature, which eventually prompted a shift in my profession aspirations. While the UN had its benefits, I got here to understand that it often lacked effective motion and the power to drive authentic change. This realization guided me to redirect my focus toward the realm of technology – a dynamic and unrestrictive space.
On the planet of technology, modern tools are available and continually advancing, granting individuals the power to spark transformation without unnecessary hurdles. This environment fosters the transformation of ideas into reality, unhindered by unnecessary permissions – a facet that basically fascinated me. The potential to make a considerable, widespread impact through technology became an irresistible calling, compelling me to immerse myself on this vibrant field.
What were among the first data projects that you just worked on?
Certainly one of my earlier projects was creating an app focused on teenage mental health. The app used passive haptics data and conversational intelligence to discover early signs of bipolar disorder. At the moment, the sector of natural language processing was not as advanced because it is today, which is sort of remarkable considering it was only about six years ago when this project was initiated. Our work was one in all the primary research and development initiatives on this space, and we later sold our IP to insurers for internal analytics and underwriting.
You previously invested in AI and ML firms through the London-based AI Seed, what were among the common traits that you just observed with successful AI startups?
A relentless thread was having exclusive access to data, together with the power to harness this data to tackle real-world problems. Furthermore, it’s crucial to acknowledge that throughout the realm of applied AI firms, the emphasis goes beyond just constructing models; it shifts towards creating impactful, beneficial products. Teams that grasp and embrace this viewpoint are those that genuinely thrive within the AI/ML landscape. For instance, Predina uses AI to predict the chance of a vehicle accident for a given location and time, while Observe Technologies uses proprietary algorithms to support fish farms to sustainably grow food.
Could you share the genesis story behind Ntropy?
Ntropy was born out of the concept among the world’s most vital information is hidden in financial transactions. Until now, this data has lived in silos, which is messy and difficult to work with. We created Ntropy to be the primary truly global, cross-industry, cross-geo, and multilingual financial data engine that may provide human-level accuracy. By creating a typical language and system to know financial data, we’re equalizing trust and access to money for businesses and individuals anywhere. By having the power to know and interpret these transactions, the dynamics of cash may be redefined, together with accessibility to it.
We’ve had quite the archetypal startup story. To start with, my co-founder Ilia and I were operating from an abandoned dusty school constructing basement. We began with 20k transactions and a distilled BERT model trained on them. The info was bootstrapped from a consumer app on Typeform with a Plaid connection, and supported by family and friends. We were working long hours and strapped for money at first, but fueled by determination and dedication to this business.
Fast forward to today, our journey has led us to research and label billions of transactions. Consequently, we now have one in all the world’s most comprehensive merchant databases with near 100M+ merchants enriched with names, addresses, industry tags, and more. We have consistently expanded our repository of transactions – harnessing the ability of LLMs on this financial data has delivered unparalleled cost-efficiency and speed. This capability holds the potential to revolutionize the financial landscape.
Why is financial data one in all the nice equalizers?
Financial data emerges as a strong equalizer on account of its capability to level the playing field, reduce uncertainty, and foster trust. When data is abundant and refined, it translates to diminished risks linked with financial decision-making. As risk becomes more manageable, a shift happens. The associated fee of uncertainty diminishes, enabling individuals to make more informed and equitable decisions, which in turn levels the playing field. For instance, if now we have greater access to data and now not make decisions based on a really narrow set of parameters, a brand new immigrant has the identical potential as someone from a well-established lineage to secure favorable terms on a automobile loan or mortgage. Essentially, the obstacle presented by financial imbalances begins to dissolve, introducing an era where a wider range of individuals can access advantageous financial opportunities.
What are among the challenges behind constructing an AI that may read and understand financial transactions like a human would?
Developing AI able to comprehending financial transactions like humans can is difficult on account of its probabilistic nature, which may result in errors. Unlike humans, AI systems still lack accountability structures. The primary challenge is refining AI systems to cut back errors and their impact while ensuring scalability. Interestingly, larger models can alleviate this challenge by progressively improving accuracy over time. Amplified capabilities and a wealth of information can enhance AI’s interpretive accuracy, ultimately cultivating a more lenient error-tolerant environment and expediting the widespread adoption of those systems.
Are you able to discuss how Ntropy offers standardized financial data?
Ntropy functions as an all-encompassing platform, bringing together a spectrum of language models, spanning from probably the most extensive to probably the most compact, at the side of heuristics. These models are trained using raw financial data, expert insights, and machine-labeled samples. Our goal is to extract meaningful insights from a wide range of transaction strings and present them cohesively in an easily comprehensible way. Our suite comprises APIs and an intuitive dashboard, enabling the rapid conversion of monetary data inside milliseconds. This functionality seamlessly integrates into users’ services and products.
What are among the use cases behind this data?
The applications for this data are extensive, spanning the whole thing of monetary operations. It empowers diverse functions including payments, underwriting, accounting, investing, and more. The adaptability of the information becomes clear in its ability to affect various facets of monetary activities, whether it involves fund transfers, meticulous record-keeping, or optimizing capital utilization.
Consider bank transactions or a budgeting app. A fast look reveals the difficulties in understanding purchases on account of non-standard merchant names and descriptions. While many firms have attempted to deal with this issue through internal solutions, they often fall short when it comes to scalability, maintenance, and generalization. A custom model is mostly only 60-70% accurate and might take months to construct.
Ntropy’s technology combines billions of information points from global merchant databases, engines like google, and language models trained on a condensed version of the online to process banking data across 4 different continents and six-plus different languages. We’re enabling using large language models at scale in finance to support all back-office functions.
What’s your vision for the long run of Ntropy?
Our vision for Ntropy is evident: We aim to develop into the go-to Vertical AI company for financial services. Our strong foundation of information and intuition, supported by a dedicated team, has uniquely positioned us to drive real change. So, what does this actually mean in practice? It’s about leveraging the newest advancements to rework finance and unlock latest levels of productivity that were previously out of reach.
Everyone knows banking may be expensive. But imagine if we could change that. By reducing costs, we’re not only cutting expenses, we’re encouraging healthy competition, improving the economics of the system, and ultimately making financial services more accessible and efficient for everybody. That is the long run we’re working towards – a financial landscape that is fairer and more user-friendly.