Name: Fintech Sandbox Demo Day 12
Uploaded: 2026-04-28T16:19:07.796Z
Duration: 1 h 3 min 8 s
Description: Fintech Sandbox Demo Day 12

Transcript for "Fintech Sandbox Demo Day 12": Good morning. Thanks for joining us today. My name is Luke Timberlake, and I am the newly appointed executive director of the fintech sandbox. Well, it's only been two months into my new role. I'm very excited to have worked with our team and some of the startups that you will see presenting today. I'll keep it brief, but I would like to say a few words before we get started. For those of you who are not familiar, the Fintech sandbox is a nonprofit that provides free access to critical datasets and other resources to support entrepreneurs who are primarily building early stage Fintech products. How do we do this? We offer access to do this data through what we call our data access residency, which is free of charge for startups to participate in. The fintech sandbox startups in return are collaborating with the current and past data residents, with our mentors, and with our general community to share learnings and advancements that benefit the fintech ecosystem more broadly. We've been fortunate enough to have partnered with only over fifth 450 startups since our founding in 2014. And we've seen our data access residency participants raise over 2,200,000,000 in venture funding. And arguably, what's most important to us is that 80% of the companies are still with us today or have been acquired by strategic buyers. With all that being said, I wanted to thank our sponsors. Some of whom you'll be hearing from later today, and also wanted to get a shout out to our 40 data and infrastructure partners. Without them, our ability to support the 450 FinTech startups that I mentioned would not be possible. I'll leave you with what I would call a call to action. If you're interested in sponsoring the sandbox, joining as a data sponsor or applying to our data access residency, please don't hesitate to reach out to myself or the broader members of our team. We also have multiple events throughout the year and we are the organizers of Boston Fintech Week in September. So please stay tuned for that as well. And with all that being said, it's my pleasure to introduce our first presenting company. Please join us in giving a warm welcome to Victoria Eugenia Tosada Brindas, the cofounder and CEO of Absus. Hello, everyone. I am Victoria. We are Access, and we are the agricultural credit intelligence infrastructure with our first product, the AI AgClone Assistant. Imagine this. You're the CEO of a bank, and it's a Friday afternoon, and you're at the beach in The Caribbean with a drink in hand. Your Ag Lending portfolio is rolling in double digits, and you didn't have to hire a single new underwriter. Every farmer you serve is delighted with a fully digital and speeded up experience. No more chaos with spreadsheets and PDFs, just business flowing like the tide. This it's not a dream. This is what happens when your bank uses access. Now let's come back to reality. Today, for every single agricultural loan being analyzed, it take at least three people, thirty, sixty, or ninety days. The credit quality is limited, and time is wasted in manual processes just to get a yes. One loan can cost up to $4,000 just to be processed. These are problems that I have experienced as a farmer and as an independent consultant for a development bank that inspired me to create access. Now if we multiply $4,000 across the thousands of loans in your portfolio, it is clear why agricultural lending is the highest in operational costs among The US banking industry. The result is that institutions are underlending to viable producers and overpricing uncertainty. Today, 70% of US farmers, your potential customers, are underserved or non served at all, not because they are unqualified, but because it is too costly to serve them. At Axis, we started with one question. What if we could lower the cost of capital and enable lending teams to do in minutes what they now take months to do with better credit quality? We are not replacing people. We are amplifying them. That's why we built the AI Ag loan assistant that replaces the spreadsheets, PDFs, and limited agronomic expertise, cutting processing times by 95% and lowering operational costs by 50%, enabling lenders to expand their customer base with control and transparency into how the money it's being used and detect red flags in seconds. Now invest your time in building stronger relationships with your farmers and create new and better sources of income. It is not just lending automation. It is intelligence at your fingertips. Let's come back and see how it works today. A new farmer applies for a loan, and the loan officer receives dozens of PDFs, spreadsheets, scans, and reports that must be manually downloaded, reconciled, rewritten, and clarified through multiple back and forth exchanges. The process takes between two to four weeks with over half of the analyst's time on manual spreading, version control, and correcting inconsistencies. The result is higher costs, delayed decision, and avoidable errors, and highly trained professionals doing clerical work instead of underwriting risk. Now let's imagine a different scenario. In in collaboration with our data partner, Equifax, we have developed the AI ad loan assistant. The same farmer, the same documents, but the infrastructure has changed. You upload the full package, and within seconds, you have data collection that goes automatically in your forms. Financial statements are structured. Spreading is done automatically. Riches are calculated deterministically. Market prices are forecasted with USDA databases. Red flags are identified across documents. We extract intelligence from ongoing relationships that you can already have in your databases to compare terms, success rates, and behaviors. A structured, consistent credit memo is generated. We integrate satellite imagery for third party anti fraud verification, transforming behavior into creditworthiness. Also, we bring water risk validation, matching the crop with the water availability to secure a successful repayment. We do all this within seconds, and here is the key. This do not replace your judgment. It elevates it. Access combines deterministic financial modeling, visual language models, machine learning models, and large language models. Our mode is unique domain expertise plus an intelligence layer to turn deep domain knowledge into clear actionable guidance that supports faster and more accurate decisions. We don't remove the credit officer. We remove the friction around the credit officer. Before Access, growing your portfolio meant more staff, higher costs, and greater risks with little standardization. With access, every loan becomes data rich, standardized, and trackable, all at a fraction of your current costs. Access is transforming financial services that were once too slow, too expensive, or too exclusive. I am Victoria Tostalo. I am cofounder and CEO of Access, and I am a farmer, an MIT MBA, and a Fulbright scholar. My cofounder, Oscar, is a national physics Olympian absolute gold medalist, MIT double degree, and ag tech innovator. Together, we are bringing the finance platform for agriculture in the age of AI. And when you finally take that break, if your phone lights up, it will be with reports showing profitability growth. Let us earn your confidence with clear ROI. We'll turn a thirty day proof of concept at no cost without requiring a dedicated team and show you tangible efficiency gains. It is a web app that can be deployed in just a few seconds, integrated with your data. Together, we can access a new future for all. Thank you. Fintech doesn't sit neatly in a box. It operates at the intersection of technology, financial services, regulation, and capital. Understanding how those forces interact is often the difference between scaling successfully and running into unexpected friction. As companies grow, they're not just building products. They're making foundational decisions about how they're structured, how they raise capital, and how they position themselves for the future. In venture backed companies especially, the structure of early financings can significantly influence flexibility in later rounds. Terms related to governance, investor rights, and liquidation preference can easily shape how a company can raise growth capital, attract new investors, or prepare for a strategic transaction. Thinking about those considerations early can have lasting implications. At Goodwin, our global fintech team of more than a 150 lawyers works with companies and investors building the next generation of financial services, from payments and lending to the infrastructure that supports them. We support clients across the full life cycle from company formation and early venture deals through growth stage capital raises, strategic transactions, and exits, including public offerings. We bring perspective from representing both innovators as well as venture capital and growth investors across the full spectrum of capital. That dual lens helps us understand how transactions are evaluated on both sides, which can lead to more efficient transactions and more durable outcomes. The key part of our work is helping clients anticipate regulatory considerations as they scale. That includes structuring bank partnerships, designing compliant product frameworks, and navigating federal and state regimes governing payments, lending, digital assets, and cross border expansion. When challenges arise, we also assist with regulatory inquiries, investigations, and enforcement matters. FinTech continues to evolve quickly. Regulation adapts, capital markets shift, and new business models emerge. Our role is to help clients navigate that complexity with clarity, combining regulatory insight, transactional strength, and deep experience in start up and growth stage financing strategy. If you're building or investing in the future financial services, we look forward to partnering with you. Alrighty. Next, we have Daniele Azaro, cofounder and CEO of CleverChain. Hello. My name is Daniele, and now the cofounder of Credit Chain. Credit Chain is a direct stack that automates compliance at scale for most financial institutions and all companies that are required to perform due diligence on their suppliers or on their customers. Financial crime is a problem with over 4 trillions of dollars in illicit financial activity globally, which is increasing also over $1,000,000,000,000 in just two years. So this is a big problem where all of the banks of financial institution are spending over £100,000,000,000 per year, but they still have not been able to really mitigate the risk in us. And these also lead to significant fines that regulators apply to them. All of these also include the significant customer friction that then delivers over sixty eight percent of applicants who drop out due to too many requests. This problem is mainly due to three dimensions. The first one is finding the information about those entities. The data itself is very shallow, expanded in multiple different silos. There are a lot of false positives due to the fact of the name similarities. Over 90% of the work of teams often is spent to differentiate from information that are related to the purely to a customer from the one that have not. And then all of these process are very manual, so require analysts at scale that will will perform the due diligence end to end. What is our answer? So we created a unified deep intelligence hub, fully automated, that is able to find more risk 10 times more than the current processes, but at the same time removing all of it much of the noise that is there. A typical review of our business customers takes over forty hours in effort. This could be spread across multiple weeks while we are waiting for customers to provide required informations. While the industry has been improving in the last few years, thanks to some new red tags, the problem is still very much live. What we've been able to do is to really bring the best, time to a minimum. And in three minutes, we were able to perform an end to end due diligence that provide all of the information required, verify the information to take, you know, very fast informations. For us, it's very important that we apply trust in what we we do and that our customer, they can trust the decisions taken on this information. And for the reason, we work very closely with regulators. So we are part of The UK regulatory sandbox and that's the one. We won many awards, best know your business tool in the market, for the last three years in from charterers, data, and support. We work with some of the major partners out there, and thanks to the fintech sandbox for introducing them to help us using the data in an efficient manner. And our product are already validated with some of the major customers. So they're including one of the largest five banks in the world, one of the largest real estate company in the world, and the best companies. So how the solution works is that our agent is able to automatically find the relevant data globally for any scenario, automatically verify that it is updated and correct, and then analyze the data in line with what a compliance expert will do with the data to allow them to inform decisions based on on a customer on risk capitalized. So today, we're looking now at a demo of our product, Vera. From the tool, you can search any company in the world and and find all of the information that's available for the specific use case. This is an exercise that is particularly difficult because, it requires access to various source of data, but then also analyzing the discrepancies, the the quality of the sources to make sure that once the report is correct is provided is correct, and you can trust the issues that are made up on it. In particular, around the companies, one of the most difficult part is to find who is really behind the company, so called alternate beneficial owners that own or control over 25% of a company. And this is particularly difficult because many registries don't provide information about the shareholders. What we are able to do automatically is to combine over 300 company registries with the real time online informations, including annual reports and official documentation, and connect all of the dots together to then identify who's really behind those companies and then apply all the checks to identify the risk associated to all of the parties that are connected. And including then, applying customization that are applicable to every own customer's capitalized. In, this is in itself is a major problem in the market. We just say over 90% typically of these type of checks were noise, so related to individuals that would have not been applicable. We have been able to remove all of the noise applying that, contextual analysis that explain why a person is or it isn't the person that we're looking at upon comparison to the risk. Another element that we allow is then research on the report itself. That's always possible to then do deep dives and investigate any additional information that are relevant to support that that investigation. So thank you very much then for, listening. If you're, interested in the Couriers to to touch this with them and, maybe run some free sample test, please get in touch. We would be very happy to to speak with you, and, have a good day. Hello, Fintech Sandbox community. I hope you're having a fantastic demo day. My name is Dan New, and I work at Along with my colleague, Matt Hatch, we coordinate our valued relationship with the Sandbox, which has flourished over several years. At we engage with fintechs in many ways, but it all starts with building relationships, us getting to know you and you getting to know us. And the sandbox plays a crucial role in helping us discover and connect with innovative fintechs just like you. Think of us as your trusted adviser throughout your journey, whether you're just starting out and need guidance, expanding into new markets, or preparing for an IPO. I'll stop there because there's very little we can assist you with as you grow and evolve. We understand unique challenges that you face, and that's why we tailor our approach to meet your specific needs, combining deep industry experience with innovative solutions. But beyond direct fintech support, we're deeply involved in the broader entrepreneurial ecosystem, including our global entrepreneur of the year program. If you're not familiar with this program, I really encourage you to visit our website to learn more or just simply reach out to me. Several Sandbox alumni have participated in the program, including the Sandbox's own Sarah Biller and David Jagan, who have served as national independent judges. Overall, we're so proud to support this vibrant community and look forward to continuing our collaboration. Thank you again for your time today for allowing to be part of this fintech community. Now I hope you have an amazing demo day and enjoy the rest of the presentations. Great. Next, we have Nishant Tomar, chairman and CEO of Digs Fact. Hi, everyone. My name is Nishant Tamar, and I'm the chairman and CEO at DigFACT. And today, we are here to talk about financial crime, like fraud and money laundering. So if there is a financial institution in this room, you would relate to it. For fraudsters, it's they have to be right only once to call it a success. Right? But for you guys, you must be right every single time when you are dealing with transactions to prevent that fraud. And so it really becomes a struggle. How do you balance, your security with customer experience? Because if you have too many rigid rules, then the false positives start to creep up. Right? And then if your rules are not sophisticated enough, then the smarter fraudsters can easily pass through them. And that's why we have been seeing the rise in fraud over the last few years, especially in the last three years as they started using AI. So what's really behind the rise in fraud? Obviously, there's a lot of reasons, but two primary reasons that are contributing to it. First one, leveraging stolen ID. Your ID, my ID, everything is out there on the dark web. So it's very easy, accessible, and affordable for the fraudsters. Second, now they're using AI to attack, which means they can be much more faster, and they can target a financial institution multiple times, 300, 500,000, until they can get all the set of rules and then bypass those rules with a single final attempt to get inside. And that's where our solution comes into the picture, the precogs. Basically, it's, you know, your AI powered avenger to fight fraud and money laundering. So why do you care about this? The key value proposition that we bring to the table, it all starts with balancing security and customer experience. We are able to decrease both the fraud losses by over 85%, and we are able to reduce your cost of manual reviews that goes into reviewing those false positives by over 70%. Together with that, obviously, you know, there's other side, benefits. The main one is reducing, your regulatory and compliance risks. We use, explainable AI, for audit ready transparency. And then our Adjentic AI approach allows you to or leverage something that is thinking like a fraudster, but at machine speed and and trying to stay one step ahead of it. So what are some of our key features and differentiators, and why should you augment your existing fraud solutions with ours? Most of the existing fraud solutions are gonna let the fraudster go through once the login ID has been validated, the authentications have been passed, the biometrics matches. Right? Whatever checks and balances are in place, if all of them pass, then they are able to go through. In our case, we use behavior anomalies. So it's not so easy for them to just go through just because the ID match. It's not only for online transactions. Our solution actually works for even check fraud. So computer vision based techniques validate signatures and names and handwriting and all that. Well, guess what? Using AI, they can be easily mimic. In our case, looking at the behavior anomalies, we can easily catch check fraud, in addition to whatever system you have in place using computer vision. Another cool thing we use to stay ahead of the fraudsters is something called generative adversarial network. This predicts new crime patterns that haven't even emerged yet and trains itself to stay to to be able to catch those frauds when and if it does emerge. And then, the fourth most important, differentiator is our use of agentic AI to deploy agents to automate a ton of tasks with human in the loop as and where needed. So key takeaway, you're not only improving the frauds that have been detected in the past, which month with much better, rules and and protection in case. But you're also able to predict what new crime patterns come may may come in the future. What we've achieved so far, well, we have partnered with Equifax, through FinTech sandbox, to collaborate on the fraud prevention. Currently, we are processing more than 25,000,000 transactions per day, and that is obviously growing, every day, every week. Last year, we saved around $2,800,000 in median losses for our customer. And on average, we are able to reduce cost of annual reviews by over 70%. We do get asked about our TPS, so we do have some metrics around that as well. So far, we have been able to process 5,500 transactions per second at peak time. That's not our cap. That's just data point letting you know that's what has happened so far. We can handle much higher TPS. So our ask from you guys, we wanna collaborate with you. We wanna do POCs to show you these metrics for yourself. And, of course, we're looking also looking for partnership, where you may wanna improve and augment your current fraud solution with the precogs to reduce both fraud losses and the false positives. And with that, I would like to thank you all so much, and I'm happy to take questions. Morningstar has been a proactive and engaged partner to the fintech sandbox for over ten years. We are excited to contribute to their unrelenting efforts to usher fintechs to market ready status and into the wealth and investment ecosystem. So Moneystar sits at the center of a vast global ecosystem with multiple touch points with asset managers, asset owners, wealth platforms, advisors, and investors. We are, first and foremost, a research organization, and we deliver our insights in different forms from data to reports to tools, software, and indexes. We provide emerging fintech sandbox members access to a wide range of data, research, and tools To help with the essential task of validating assumptions, empowering minimally a minimum viable products, our data offerings cover traditional securities and funds, ETFs data, ESG data. Fintechs enjoy working with us since we don't limit our contribution to providing data. We're very happy to help them shape their offerings, and they appreciate the feedback we give them on product features, value propositions, and best practices to break into the market. And to this end, we've created a number of programs to provide, our younger clients, exposure and distribution, which is paramount for their success. Lately, we've noticed a surprising number of emerging fintechs that had been test driving our new AI powered MCP connectors where, their applications and agents can call directly or through platforms like Anthropic, OpenAI, and Microsoft Foundry or Copilot Studio and giving them access to tools and services which include our analyst research, our insights, our AI ready data, screeners, and fund holdings. So we would love to support you. We look forward to hearing from you and supporting you in your journey, through the vast and growing investment ecosystem. Next, we have William from Manos AI. William, I'll pass it over to you. Hello. My name is William from Mino CI. Mino CI is the AI for asset management, number one AI platform for buy side. Imagine you're a portfolio manager, and it's 02:15PM, Wednesday, 09/17/2025. The Federal Reserve is about to release its full MAC meeting results in fifteen minutes, and there are a lot of geopolitical risk news flying all over the place. You manage a large portfolio with a lot of sensitive, you know, assets to the market. Phones are running. Analysts are digging. Risk model are running. But everyone is working very hard, yet no one has to answer clear answer to what's next. This is the problem our industry faces. A lot of our workflow are broken. This those this fragmented tools and the and the workflows, making investment team very hard to systematically learn from their decisions. This is what we're trying to fix. With Milosec developed a sonar. It is a central intelligence for asset management, which turns the fragmented tools into weighted convictions and the concrete portfolio actions. This is the before and after. Before AI, you will need to work with your research analyst, quant analyst, data analyst on a very comprehensive research, which typically takes five to, four five, business days. Now with AI, you can ask a question. All the insights will be at your fingertips within seconds just like that. Talk is cheap. Let me show you our product. So Mino said, ultimately, we have built this human and the AI collaborate collaboration platform where where AI agents will work on those manual and repetitive work. Each of them handles a very specific job. In this case scenario, I will show you how research agent works. So here, you can come here and chat with our AI agent. You can ask the question. Given the geopolitical risk in Middle East, please reason what are the opportunities for 2025 across different asset class from your reasoning and, analyst review views. Let's say what AI can brings you. So our AI agents dig through all the internal, external research reports saved in your research repository, and they post all the data to give you the answer. In this case, the the analyst has suggesting the energy commodities will benefit and the defensive equities. In this case, you can also pull out your past researches and then there this research, all the, reference will be cited. You can see exactly how different countries' equity market were impacted by the geopolitical risk in this report. And, also, this is where we we reserve all the institutional memory. And as you can see, you may, someone has already in your organization has already done a very comprehensive research on how gold has performed during wartime. And in this scenario, real data has populated research on gold performance during different, you know, wartime. For example, the Russia war Russia Ukrainian wartime. And here, all the data are populated through our fintech partners. Thank thanks for SP Global and the fact that giving us all the real data to power those researches. No AI hallucination. All real data, real references, which powers those quantitative researches. So, also, not only data and research, you can also see how internal analysts see how they have been their view, on those issues. Let's say in this case, this research analyst has produced a very compact comprehensive research on turbotention and its impact on gold. So not only that, the most differentiated part about our products, we can actually show you how this analyst has performed over the history of his all his work. We can show his rationale, accuracy, sizing capabilities, and, average return on all his trade ideas. So you can also see, you know, his his performance and all the past trade he has recommended. In this case, let's say, hey. This guy has not only recommended the gold trade, he also recommended the copper trade early this year. As we all know, early this year, the copper, you know, tariffs has created a greater opportunity, and he identified the copper price will go up in early April and very accurately, you know, timing the market, you know, show when copper will go down. So this gives the full full conviction and idea for PM or someone, you know, reading research. Not only that, we can show the exact everyone, of your team member build the ultimate moneyball scorecard for everyone in your organization and the external research analyst. So with that, let's go back to our slide. Stoner is live today. We have paying customers, managing trillions of dollars, watched with a renowned hedge funds using it today, generating alpha. We're in we're using institution, data, again, thanks to fintech sandbox partners. We're enterprise ready, SOC two compliance, role business access control. We're looking for introductions. If you you know a portfolio manager working on AI transformation, please, you know, send them to our way. We're looking for data partners, continue to enrich our ecosystem, power our AI research, and then we're also looking for advisors who can help us, guide us through this innovative and transformative, journey. My name is William. Please come find me. Thank you. I'm Nicole Edwards, and I lead marketing and community here at Fintech Sandbox. Let me start off with a question. What does it take to turn a great Fintech idea into a company that actually changes the system? System. At fintech sandbox, we've seen the answer again and again. It's not just capital. It's not just code. It's connection. Over the past decade, we've supported over 450 startups across 20 countries. And one thing is clear, innovation doesn't happen in isolation. It happens in ecosystems when founders, data providers, financial institutions, and community organizations come together around a shared goal. That's where our partners come in. Our sponsors, data partners, and community partners aren't just supporters of our mission. They are active participants in shaping the future of financial services. They bring critical data, real world challenges, and deep domain expertise into the room. And in return, they get something incredibly powerful, a front row seat to what's next. Through fintech sandbox, partners engage directly with early stage founders building solutions in areas like AI driven risk modeling, embedded finance, and financial inclusion. They see emerging trends before they hit the mainstream. They help refine products before they scale, and they build relationships that often turn into pilots, partnerships, and long term innovation pipelines. This is how real change happens. To our sponsors, data partners, and community partners, Thank you. Your support fuels everything we do from our data access residency to moments like today. And to those of you watching, if you're looking to engage more deeply with the fintech ecosystem, to connect with founders solving real problems, and to help shape what comes next in finance, we'd love to be part of that journey with you. Make sure to head to our website, fintechsandbox.org, after today's event to learn more about how to get involved. Now it's my pleasure to introduce our next founder, Adej Janadu, cofounder of Sometime AI. His team is tackling one of the most complex challenges in financial systems today, how we understand and respond to change. Hi. I'm Ade, and I'm a cofounder at SunTime. We are a research lab building the next generation of machine intelligence. We developed the first intelligence layer for the financial markets that identifies risk at the point of inception, moving the markets from reactive panic to proactive decision making. The reason this matters is the global financial system is currently built on the foundation of reaction. In March 2020, the Federal Reserve was forced to commit 2,200,000,000,000 in just seventy two hours, a reactive move to help prevent the system from collapsing. By the time the Fed acted, the damage was already done. The market declined 30%, and Global Equity had lost 24,000,000,000,000 in just forty days. These are the massive costs of having financial blind spots. To eliminate the blind spots, we developed the first true intelligence layer, a microscope for systemic risk that monitors the global financial system as a single interconnected information network. While the current system relies on lagging indicators and reactive interventions after the fact, we provide intelligence that identify structural change at the point of inception for any publicly traded asset. Our goal is simple, to move the entire industry from reactive panic to proactive management. We serve three primary engines of the financial world. For central banks, we provide an autonomous monitor for monetary policy. For commercial banks, we act as a systemic health monitor. And for hedge funds, we deliver true information advantage in constantly moving markets. But building a tangent line for the global markets requires more than just a theory. It requires a rigorous, large scale validation. To that end, we'd like to then call sandbox partner, MASV, who provide the historical and live market data necessary to validate our framework. As you can see from the the visual here, the result of this collaboration is transformative. A massive 99% reduction in drawdowns across multiple market periods. To prove these results aren't random chance, we stress tested our framework against the most catastrophic shocks of this century, the two thousand eight financial crisis and the twenty twenty pandemic. We specifically targeted worst case scenarios across diverse sectors, including home building and banking in 2008 to energy and hospitality in 2020. The Jordan State column tells story. We effectively eliminate the tell risk that collapse portfolios. What makes these results remarkable is the methodology. Unlike traditional machine learning, we achieve this with no reliance on training data, no retraining, no sentiment analysis, and no context graphs. This isn't a model that needs to learn what a crash looks like. It's a new form of intelligence validated through our partnership with Massive. This evidence proves that even the most severe financial clients can be managed. With the right intelligence, a 90% loss is not a surprise. It's avoidable. We spoke to a fund with hospitality exposure due in early twenty twenty. While the broader market peaked on February 19, our framework generated a signal on January 17, providing a full month of lead time before the turn. Using high frequency data, we identified the inception of risk at the one second level, tracking its evolution as it scaled into a systemic collapse. The client's existing model failed, despite only after MGM had already collapsed 60%. This delay forced a reactive liquid liquidation and triggered massive investment draws. We achieved this without knowledge of the pandemic. We simply identified the structural breakdown in the data as it happened. The industry dismisses market crashes as black swans and predicts for disasters no one could have seen coming. Our framework proves these are actually white swans at inception. They only become black when the industry ignores the early signals. By monitoring the global system in real time, we identified the exact bridge where microfluctuation scales into a systemic threat. While the broader market panics, our intelligence has already been tracking the events evolution for weeks. A black one is simply a white one that was missed. Having validated our frame with MASV in q one, we are now conducting private technical demos of institutional risk teams. These deep dives demonstrate how we provide an effective challenge against model failures that crippled portfolios during 2008 and COVID nineteen. In the second half of this year, we moved to parallel runs with our partners, benchmarking our inception signals directly against the gold standard for market risk being expected shortfall. This path scales to a twenty twenty seven global deployment across London, Tokyo, and US. We are seeking partners to join this network. We wanna speak to firms ready to move beyond reactive management and identify the next white swan at its point of inception. We've reached a turning point. For decades, the industry has accepted massive crashes as unpredictable. But our insights prove these events have an inception point and they can be managed. Our framework meets the auditors trifecta. It is explainable, auditable, and traceable. So I'll leave you with this. If a black swan is now a manageable event via a real time API, what is the cost of remaining blind to it? We provide the evidence needed to stop trillion dollar collapses before they start. If you're ready to move from reactive panic to proactive decisions, let's talk. Thank you. Last but certainly not least, we have Charlie Ko, founder and CEO of Jemisin. Charlie, please take it away. Hi. My name is Charlie Coe. I'm CEO and cofounder of JEMISON. We have a problem. It comes from bad actors doing their best to hide in the data that represents $55,000,000,000,000 in financial flows every day. In simpler terms, the folks take advantage of us through fraud, money laundering, and market manipulation. The daily cost is $41,000,000,000. I can't think of a single person that hasn't been impacted by this, either stolen money, identity, or sense of safety. If you work within a financial institution, you are doubly impacted since it hits your org's profits and credibility at the same time. The pain is frustrating because it's a daily scorecard, and it feels like we're losing. How does this still happen when we have access to such sophisticated technologies? Yes. There are powerful machines that can find telltale patterns of wrongdoing within the data, but they can miss signals because, one, they have a huge resource appetite. Two, they are slow to adapt. Three, they don't always play well together. To fix things, we use math to make these pattern detection models way more efficient, enabling them to evolve and adapt instantly while offering easy integration. This is in complete contrast to more common solutions that throw bigger data farms, more GPUs, and suck more energy to counter the data volume. So how does our technology help fight the dark forces that try to hide in the data? The tools we are talking about are machine learning models. By the way, they drive 99% of pattern detection in finance. In essence, every one of these models is built from a set of equations. These equations can be large scale, complex, and slow to solve. We created a new paradigm where the vast majority of the equations to be solved are pre computed, embedded into an extremely compact and secure abstraction that we name the global elements matrix or gem. It's like taking a 10 step problem and immediately jumping to step nine. This is the difference between building a car before every trip versus simply getting in and driving. The gem is powerful because, one, it's extremely compact, Two, it's secure and unbreakable. Three, it's lossless, meaning it contains all those needed by the ML model. To use the gem, just follow some simple steps. First, grab your data. Second, create the gem. Third, feed your models. As part of our testing, we partner with FinTech sandbox's data partner, massive.com. They have an incredibly rich dataset spanning a decade and a half market transactions with billions of rows of equity flow data each day. Think of their data as a source of fuel for Jensen's machine learning engine. Throughout our testing, we confirm several outcomes. One, we're extremely resource efficient, taking up less than 1% of the compute and storage compared to standard ML platforms. Two, we cut down the model development life cycle from months to days. Three, we run seamlessly next to your current tech stack and existing ML technologies. Here's the net effect. You get to run your models a thousand times or 10,000 times in the space it takes to run once on more standard platforms. The outcome is that you have a powerfully efficient tool to stay ahead of the ballooning data volume and harder to find patterns. Now let's jump into the demo. Imagine you're the CTO targeting a shorter model development life cycle. You want your data science team to accomplish a few things. One, test their ideas quickly. Two, deploy into production seamlessly. Three, access monitoring tools. Four, retrain automatically. So what does this look like? Start by loading data from any data source. Once the data is loaded, the gem is created. The gem is placed into a library for easy examination. From there, you can create a wide variety of models, including classification, regression, clustering, anomaly detection, and sequencing models. After selecting the appropriate models, you can directly deploy into our streaming workflows, where you can look at the live stats and see how the streaming data is changing in real time. One of the most useful features is performing automatic drift analysis to see if there is performance decay. If there is performance decay, you can select automatic or manual retrain procedures. The retrain process is extremely fast. After retraining, you can seamlessly deploy. This is real time machine learning. I'll end this demo with the following note. Our platform is designed to help data scientists to test, deploy, monitor, and adapt with minimal friction. Let's recap what we've just seen. Think of us as a discovery tool that is also production ready. What does this actually mean? It means we're installed in Fortune 100 enterprises and that we offer easy integration via API or a user dashboard. We also understand that security is priority to enable you to run our technology in the cloud, private instance, on premise, or any combination of the above. Finally, we are extremely resource efficient. We're looking for testing partners to push the edge of what's possible in terms of data volume and speed along with model evolution capabilities. The best part is that you can run us in parallel with the current systems at very low resource cost, and we can be installed in an afternoon. With that, you have a powerful tool. Much thanks for your time. Please reach out if you're interested in seeing what is hiding in your data. Well, that's a wrap. Thanks for attending today. I hope that you enjoyed as much as I did learning from just a sampling of the over 450 startups that we've been fortunate enough to work with since we were founded in 2014. I thought the founders today really highlighted not only the diversity of the types of fintechs that the sandbox was founded to work with, But also, it touch they touched on some pertinent themes within the world today such as AI and climate among many other areas as well. Please join me in congratulating the startups and their founders for presenting today. I know it's a huge undertaking to do so and congratulate them on all that they've achieved. I'd also like to thank the entire Fintech sandbox team with a special call out to Ali, our operations intern, who has worked tirelessly behind the scenes to make this demo day a huge success. Also, we wouldn't be able to do this without our sponsors, some of whom you heard from today, And we wouldn't be able to offer a data residency if we didn't have the participation of over 40 different financial technology companies offering their data for free to our startups. And this equates to over $200,000 per startup in value that we're able to offer through the program. Finally, if you'd like to get involved, there's a few different ways as well. First, if you're a startup founder, there's the data residency and you just heard from some of the startups that have participated in that program. We'd also like to talk to you if you wanna become part of the community as a mentor or as a sponsor or as another data partner. And there's gonna be other ways coming up this spring to get involved in terms of events that we're hosting, so please stay tuned for that. And we're also the host of Boston Fintech Week, which will be coming up in September. And in coming weeks and months, we'll be announcing some interesting details about speakers and some of the initial events that we have planned. Have a great day, and we'll talk to you soon. Thanks.