Vector ML Analytics
AI Financial Modeling for Banks: Harnessing a $70B SAM within $133T Global Bank Assets
Highlights
Featured Investors
2000 Market risk manager | 2004 Head of market risk and new products | 2006 Fixed income strategies, ABCP conduits, muni bonds | 2014 Fixed income fund construction and optimization | 2015-23 Enhanced Analytics - Global Structured Finance @ Fitch Ratings
âVector ML Analytics had the vision and foresight to build the core technology underpinning its financial planning and optimisation suite back in the old ânew normalâ of steady and low interest rates. At the same time Vector managed to build a substantial client book â despite the lack of urgency implied by rates and default risk compression. Vector are well positioned to serve the sizeable proportion of non-bank lenders and regulated entities that are now waking up to the need for robust int...â
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Our Team


AI-Powered Financial Modeling for Banks
Vector ML Analytics is poised to revolutionize the financial sector with its AI-powered Scenario Optimizer and Recommendation Engine. These cutting-edge tools are particularly transformative for small to mid-sized banks, traditionally facing resource constraints in risk management and profitability analytics. With our cutting-edge platform, banks swiftly generate in-depth 5-year projections for balance sheets and income statements. This game-changer equips financial institutions to make bold, informed decisions, optimizing profitability, capital distribution, and liquidity.
Problem: Banks' Financial Reporting Methods Suffer from Limitations
Broken reporting systems, can cause CFOs to miss key insights, potentially leading to poor decisions, financial losses, or even bankruptcy.
Traditional financial models and Excel-based methods are showing their limitations in today's complex financial environment. They fall short in providing the crucial insights and simulations essential to identify potential risks and navigate market volatility effectively.
This deficiency severely inhibits financial institutions' ability to maintain stability and make informed decisions amidst the growing uncertainties.
Why Now: The Financial Crisis A Wake-up Call
The failures of Silicon Valley Bank, First Republic, and Signature Bank have been caused by persistent issues in the finance sector, as seen in the 1980âs and 2008. Years of low and stable interest rates and financial stability cultivate complacency.
"Economists foresee 'new era' of higher inflation post-SVB rescue" - CNBC
âSilicon Valley Bank because of âpoor management,â FDIC report findsâ - CNN
This leads to underinvestment in critical areas like risk analysis, financial planning, and simulation tools. Now, more than ever, this vast financial landscape demands a transformative approach. To ensure the stability of our financial future, there is an urgent need for innovative tools that assist financial institutions in navigating the ever-changing market conditions.
Solution: AI-Powered Financial Modeling
Scenario Optimizer: A strategic tool enabling banks to proactively manage their financial planning and asset liability with advanced AI-driven simulations. This optimizer provides CFOs at small to mid-sized banks with predictive insights that help them navigate through complex financial landscapes and make decisions that align with their risk appetite and profitability goals.
Recommendation Engine: A sophisticated analytics platform that aids CFOs in making informed decisions on financial planning and asset liability management. The engine processes vast amounts of data to furnish CFOs with actionable advice, optimizing balance sheets and enhancing financial performance without the need for large, specialized data teams.
These offerings from Vector ML Analytics are reshaping the capabilities of CFOs in smaller banks, allowing them to operate with the foresight and strategic acumen that was once the domain of larger institutions with extensive resources.
How It Works
Vector ML Analytics' platform elevates financial reporting and decision-making to new heights by harnessing cutting-edge AI and advanced simulations. It simplifies data collection and analysis to deliver CFO reports swiftly and incorporates over 20 sophisticated FP&A and loan performance models for quicker strategic decision-making.
The platform's crown jewel, the AI-driven Scenario Optimizer, accelerates financial projections by 90%, offering a more cost-effective solution than conventional methods. This suite of tools not only enables smaller banks to robustly compete by marrying regulatory compliance with profitability but also equips them with unparalleled insights into market dynamics and risks. Financial institutions, therefore, are empowered to proactively manage risks, capitalize on opportunities, and adeptly navigate through market volatility, thereby redefining their standing in the financial industry.
Enhanced Efficiency
Vector ML Analytics' platform is engineered to turbocharge ROI for banks through its suite of AI-driven tools, delivering results that are quantitatively superior to traditional financial modeling and analysis methods.
In terms of ROI, these enhancements mean banks can achieve a significant reduction in labor hoursâup to 50% for complex analytical tasksâwhile simultaneously increasing the potential for revenue generation through more accurate and timely financial advice. The end result is a dramatic upswing in operational efficiency, culminating in an optimized balance sheet and an enhanced competitive position in the market.
- 90% Time Efficiency
- 20X Output Increase
- 500% Faster Insights
- 80% Cost Reduction
Vector ML Analytics thus offers a transformative solution that propels financial institutions toward achieving heightened profitability with smarter, leaner, and more agile operations.
US Banking Assets Hit $25T With a $12B SAM
Revenue Model
Subscription-based ARR Model: As a Software-as-a-Service (SaaS) platform, we offer direct/API portal access to our advanced financial forecasting platform. Our clients subscribe to annual plans, tailored to their specific asset size, with pricing ranging from $50K to $250K ARR.
Our Team: A Foundation for Success
Sadeq Safarini: CEO & Founder and Jade Clarke: COO & CO-Founder
Vector ML Analytics, founded in by industry veteran Sadeq Safarini, who has worked at 7 financial institutions, notably growing Dealnet Capital, a publicly traded firms loan portfolio from $50M to $200M.
Alongside him is Jade Clarke, our COO, with a background in compliance and marketing, responsible for expanding our customer base and raising significant capital. Rounding out our leadership is CTO Phillip Morgia, who guides our technological direction, ensuring the delivery of industry-compliant and scalable products. Together, our team is united in its mission to provide banks with revolutionary tools that prioritize profitability and sustainable growth.
Why Invest in Vector ML Analytics?
With a proven track record and recognizing the vast potential of the current banking crisis and the demand for advanced financial simulation tools, we are seeking funding to expand our reach. By doing so, we aim for a staggering 500% increase in our revenue and client base. This will position our company for a subsequent investment round at a significantly higher valuation, ensuring we are well-prepared for future growth demands.
Product Demo
Media and Press
Vector ML Analytics - Bankers Fintech Council
Vector ML Analytics - Wisconsin Bankers Association
The Lean Startup Method and LTSE Vision
Our long-term aspiration is to explore a listing on the Long-Term Stock Exchange (LTSE), founded by Eric Ries, the author of "The Lean Startup." We're honored to have Eric Ries as a member of our advisory board, which further solidifies our alignment with the principles of building a durable, community-centric enterprise. The LTSE's emphasis on sustained value and corporate responsibility. It highlights our aim to prioritize our mission over immediate gains, align compensation with long-term performance. By doing this, we hope to make a positive impact in the realm of financial analytics in banking and lending.