Dec 5, 2013 · 20m · mad
Panel Discussion // NYC Data Business Meetup // Feb 2013
gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
Hosted by Matt Turck at the NYC Data Business Meetup, this panel discussion features founders from Plaid, Quantopian, and ZestFinance sharing their entrepreneurial journeys and discussing data-driven innovation in financial technology. The speakers address technical architecture, regulatory compliance, machine learning validation, and the challenges of disrupting legacy financial institutions.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 7.2% of the talking time here. How this is scored →
speaking balance: gold is Matt, purple is the guest (3 minute bins)
Shawn Budde explicitly pushes back against an audience member's assertion, stating 'I'm gonna disagree with the assertion that everybody knows how to do this' before detailing how major banks lag in machine learning techniques.
Hardest push from Matt ▶ 4:18 Matt Turck banter on VC value addIn a panel dominated by audience Q&A, the host's primary direct engagement is playfully challenging the guest's origin story narrative by asking if it is a rare case of VCs actually adding value.
Biggest teaching moment ▶ 13:01 Shawn refutes model validation tautology claimWhen an audience data scientist accuses ZestFinance of circular reasoning, Shawn educates her on their strict 70/15/15 split validation process and demonstrates real-world empirical proof with a 98% overnight drop in fraud.
Matt holds his own ▶ 4:18 Matt Turck's witty VC value framingMatt Turck steps in at the end of the founding stories to reframe the guest's anecdote with a sharp, humorous takeaway about VC contributions.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Matt as informed peer | Guest teaching | Guest disagreement | Matt pushing back | Why |
|---|---|---|---|---|---|---|
| Founding Stories of Quantopian and ZestFinance | 1 | 1 | 1 | 1 | The panel opens with Zach, John, and Shawn sharing their respective company founding stories. Host Matt Turck acts primarily as a warm moderator, making a lighthearted banter comment at the end about VCs adding value. | |
| Q&A: ZestFinance on Model Compliance and Risk | 0 | 6 | 2 | 0 | An audience member asks Shawn Budde about model compliance and handling 100,000 variables under lending regulations like redlining. Budde provides an educational technical answer detailing holdout sets, jitter techniques, and adverse action compliance without any host intervention. | |
| Q&A: Quantopian on Backtesting Data and Algorithmic Trading | 0 | 5 | 1 | 0 | John Fawcett answers back-to-back technical audience questions regarding Quantopian's historical data depth, intraday trading capabilities, and integration with complex event processing engines like Streambase. The dynamic is collaborative and informational. | |
| Q&A: Plaid on Use Cases, Security, and Privacy | 0 | 5 | 1 | 0 | Zach Perret from Plaid explains customer use cases and responds to an audience question about privacy and security by describing Plaid's read-only, encrypted database architecture. Host Matt Turck only intervenes to ask the questioner to state her name and company. | |
| Q&A: Skepticism on Predictive Modeling and Validation | 0 | 7 | 4 | 0 | An audience data scientist challenges ZestFinance's model validation logic as circular tautology. Shawn Budde responds firmly, breaking down their strict 70/15/15 cross-validation protocol and sharing real-world performance metrics showing fraud dropping 98% overnight. | |
| Q&A: Scaling Big Data Underwriting to Institutional Finance | 1 | 7 | 6 | 1 | Audience members ask why legacy financial institutions haven't adopted big data underwriting and suggest ZestFinance is more of a legal firm than a tech firm. Shawn Budde explicitly rejects the premise, pointing out that major banks only use basic decision trees while ZestFinance ensembles advanced ML algorithms like SVMs and random forests. | |
| Q&A: Computational Latency and Model Deployment Speed | 0 | 5 | 1 | 0 | In response to a final question about computational latency, Shawn Budde explains how ZestFinance deploys R models using Rserve on AWS clusters to score loans in three seconds, contrasting this with traditional bank 18-month deployment cycles. |