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Meet Daniel Newman, CEO Of Dandy: The Tech Startup Spearheading The “Live” Movement In Social Networking

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Just three short years ago, Newman began his journey as a full-time CEO of his very own tech startup named Dandy. He created the company with his partner and co-founder, Leor Massachi, while the two were seniors in college. We’ve got the full scoop on how Newman went from a Real Estate Development student to a full-time entrepreneur, all before earning his undergraduate degree.

Newman was born and raised in Beverley Hills, CA. Although he’s mainly American, he takes pride in inheriting a Persian background from both his mother and father. Early on in his younger years, he became interested in the various aspects of business and how they were created. He also enjoyed learning about the Israeli economy and the country’s positive outlook on young people developing their own startup companies.

When Newman got to high school, he became heavily involved in extracurricular activities and always did well in class. Not only was he named Senior Class President, but he was also involved in several sports and school clubs. As if that weren’t enough on his plate at 17, he also had the opportunity to get a taste of what it was like to build a business when he founded his own tutoring company during his junior year. He saw an opportunity arise when the younger kids in grades K-8 were complaining about their tutors being too old and not up-to-date with the material. Brilliantly, Newman asked some of his friends if they wanted to earn some money tutoring the students, and the rest was history. The company took off instantaneously, and Newman kept it running until he graduated in 2015.

Once he reached college, the grind continued. Newman decided to pursue a degree in Real Estate Development at the University of Southern California. Although he was indeed partially interested in the real estate portion of the program, he was far more captivated by the school’s innovative take on technology and its multifaceted ability to influence new businesses. At that point, he began to understand the building blocks of a tech startup, and he fell in love. Along the way, he met several friends, mentors, and executives that taught him the dos and don’ts about the complicated world of Silicon Valley. But regardless of the dire risks he was advised of, he knew his ultimate goal would be to someday establish a startup company of his own.

In the meantime, Newman founded his second small business with his then-roommate and best friend, Leor Massachi. The two college students created a design agency that helped businesses market toward the Gen Z demographic via custom-made interactive Geofilters on Snapchat. At the time, the social networking app had just begun allowing users to publicly submit Geofilters for a fee, but it had not provided any tools or instructions on how to create them. Due to the high design skillset and intricate strategy required for the process, Newman and Massachi saw it as a business opportunity and proceeded to create a company named Geocasion. Although the business only lasted a few months, the experience proved essential for what followed for these two college students. In addition to founding Geocasion, Newman also founded USC’s TAMID Tank event during his sophomore year, which is the school’s equivalent to the popular television show, Shark Tank. The competition was created to provide students with a real-life experience of pitching their startup concepts to big-name investors and venture capitalists. Their first event filled an auditorium of 500, and since then, TAMID Tank has held the event annually. The organization also named Newman their Vice President of Operations.

But things changed in 2018 when Newman’s roommate suggested the idea of creating a dating app for millennials and gen Z’s unlike the existing ones on the market. After sitting and brainstorming for hours in their dorm, they came up with a concept that was far too tangible to pass up. They wanted to create a version of a dating app that would mimic two people meeting in person for the first time. Users would log onto the app once it went “live”, and they would have an allotted time to attempt to find their match and start a conversation. Once two users established they were interested, they’d be transferred into a three-minute video call where they could formally introduce themselves and decide whether or not to move forward with communication off the application. They called the app Dandy and instantly began searching for the perfect engineers to develop the product. 3 months later, the app launched its beta testing.

Dandy blew up all over USC, and eventually, all over Los Angeles. People were excited to try this new version of virtual dating and claimed that it was a “magical” app since it cut around the BS and got straight to the point of building new relationships. At this point, Newman and his Massachi began to pitch Dandy to investors in hopes to raise funds for the app’s future development. After hearing 117 no’s, they received their first yes, as well as their first check from an investor. Once the first came, many others followed, and soon enough Dandy has fundraised over $3.3 million in a matter of months from investors involved in companies such as Uber, Airbnb, Snapchat, and Facebook. Newman took over all finance and logistic aspects of the company while Massachi handled the marketing strategy and creative.

Things were running smoothly until word of a pandemic began to consume the news in February 2020. The two business owners called an emergency meeting and decided it was the perfect time to rebrand Dandy into something more applicable to the possible consequences of a national pandemic. In just a few hours, they came up with the idea for Zoom University– a virtual dating app with the same “live” concept of Dandy, but with two-on-two video calls resembling that of a double date. Since some users had commented that Dandy could become stressful and awkward during the short video calls, the founders hoped that having a user bring a friend would help turn the tension into fun. The next day after the meeting, the team had a web MVP of Zoom University uploaded and a rough draft of the app immediately went live. In honor of their first creation, they decided to keep the name of their now product-based startup company as Dandy.

Since then, Zoom University has gained traction all over the internet; including Tiktok, which had a video about the app hit impressions of over 2.5 million views. Users were scrambling to get their hands on this new dating app. In just a matter of weeks, a waitlist of thousands of users began to accumulate while the Dandy teamed continued to finalize the details behind the app that was only originally meant to stay live for a week. Positive feedback came pouring in from users, and eventually, the application broke records as it made it through the Top 10 Best Social Networking Apps on the Apple Store, coming in at #9.

Four businesses and two successful startups later, 23-year-old Newman says his success has come from knowing how to take high-level concepts and applying them to a realistic, practical lens. Although his achievements have skyrocketed over the years, he shares that the work has only just begun. He and his partner are currently working with investors on their next top-secret product that is reckoned to top all their prior inventions and take the market by storm once again. Details cannot yet be disclosed, but we wait eagerly to see how a few college seniors will continue to dominate the startup world with their commitment and dedication to changing the world through the use of advanced technology.

The idea of Bigtime Daily landed this engineer cum journalist from a multi-national company to the digital avenue. Matthew brought life to this idea and rendered all that was necessary to create an interactive and attractive platform for the readers. Apart from managing the platform, he also contributes his expertise in business niche.

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Business

AI in Asset Management Explained: How Leading Firms Apply It

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AI in asset management explained at its most basic level is this: using machine learning, data modeling, and automation to make faster and more accurate investment decisions. The applications vary widely across asset classes, fund strategies, and operational functions. Understanding where AI creates real value separates productive adoption from expensive experimentation.

Asset managers now face a data environment far larger than any human team can process manually. Market signals, company filings, macroeconomic indicators, alternative data sources, and portfolio monitoring all generate information continuously. AI tools process that information at scale. They surface patterns that traditional analysis would miss or find too late.

AI in Asset Management Explained Across Core Investment Functions

AI delivers the most measurable results when applied to specific investment functions rather than deployed as a general capability. The clearest applications sit in portfolio construction, risk management, and credit analysis.

Portfolio Construction and Factor Modeling With AI

Traditional portfolio construction relies on return and correlation assumptions built from historical data. AI-driven portfolio tools go further. They process real-time market data, alternative signals, and macroeconomic inputs simultaneously. This surfaces factor exposures that static models miss.

Machine learning models in portfolio construction can:

  • Identify non-linear relationships between asset classes that correlation matrices do not capture
  • Adjust factor weightings dynamically as market conditions shift rather than on a quarterly rebalancing schedule
  • Flag concentration risks before they appear in standard risk reports
  • Model tail scenarios using a broader range of historical stress periods than traditional value-at-risk models allow

James Zenni, founder and CEO of ZCG with over 30 years of capital markets experience, has built the platform’s investment approach around the principle that better data and faster analysis produce better outcomes. That view shapes how AI capabilities get deployed across ZCG’s private equity, credit, and direct lending strategies.

Credit Analysis and Private Markets AI Applications

Credit analysis in private markets has historically depended on periodic financial reporting and relationship-based deal intelligence. AI changes that model. Lenders using machine learning tools now monitor borrower health continuously rather than waiting for quarterly covenant tests.

Specific credit applications include:

  • Cash flow pattern analysis that identifies revenue deterioration weeks before it shows up in reported financials
  • Supplier and customer relationship mapping that flags single-source dependencies and concentration risks
  • Covenant monitoring automation that tracks hundreds of credit agreements simultaneously and alerts teams to early warning signs
  • Loan pricing models that incorporate current market spread data and comparable transaction history

These capabilities compress the time between identifying a problem and taking action. In credit, that time advantage directly affects loss rates and recovery outcomes.

AI in Asset Management Explained Through Risk and Compliance Applications

Risk management and regulatory compliance represent two of the highest-value AI applications in asset management. Both functions involve processing large volumes of structured and unstructured data under time pressure.

How AI Transforms Risk Monitoring in Asset Management

Traditional risk monitoring produces reports at set intervals. AI-powered risk systems run continuously. They flag anomalies in position data and monitor correlated exposures across a portfolio. Alerts fire when market conditions shift beyond defined thresholds.

The practical risk management applications include:

  • Real-time portfolio stress testing against live market inputs rather than end-of-day snapshots
  • Liquidity modeling that accounts for position size relative to market depth across multiple scenarios
  • Counterparty exposure monitoring that aggregates risk across instruments, custodians, and trading relationships
  • Regulatory reporting automation that reduces manual preparation time and lowers the risk of filing errors

ZCG applies these capabilities across its approximately $8 billion in AUM. The platform was founded 20 years ago. It built its investment infrastructure around systematic data analysis and operational discipline.

AI for Operational Efficiency in Asset Management Firms

Beyond investment decisions, AI delivers significant value in fund operations. Back-office functions like reconciliation, reporting, and compliance documentation consume substantial resources at most asset management firms.

AI tools applied to fund operations include document processing systems. These extract and verify data from offering documents, side letters, and subscription agreements automatically. Reconciliation tools flag breaks between custodian records and internal systems automatically. Investor reporting platforms generate customized materials from structured data inputs, reducing the manual production time significantly.

ZCG Consulting (“ZCGC”) advises operating companies across more than a dozen sectors on operational improvement programs, including technology-driven process redesign. Those operational efficiency principles translate directly to asset management back-office functions.

Applying AI to Asset Management: Limitations Firms Must Address

AI in asset management explained fully must include the limitations. Models trained on historical data perform poorly when market regimes change. Overfitting produces tools that work in backtests but fail in live environments. And AI outputs require experienced interpretation to avoid acting on statistically significant but economically meaningless signals.

The ZCG Team approaches AI adoption with the same discipline it applies to investment underwriting. Every tool requires a defined use case and a measurable success metric. A review process keeps experienced judgment in the decision chain. That framework prevents the common failure mode where AI adoption generates activity without improving outcomes.

Firms that treat AI as a capability layer on top of sound investment processes generate sustainable advantages. Those that treat AI as a replacement for process discipline find the technology amplifies existing weaknesses. It rarely corrects them.

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