GoVertical presents

Vertical ML/AI Startup Creation Weekend

Hosted by Madrona Venture Labs & TiE Seattle

As a free benefit for participants, we would like to extend an invitation to the Amazon SageMaker workshop on Feb 14 from 1p-4p.


Fintech resources

Welcome to the Fintech vertical page! In order to make the most of the time the weekend of the event, please review our key educational materials and data sets. 

Be Prepared! Start thinking through what types of data could power your business and product ideas. Often times a combination of multiple, disparate data sets can yield the most ingenious ideas and solutions!

Panel videos

The following videos were recording during the April 19 Panel event. You may wish to reference them in preparation of the weekend ML event.

ML Panel moderated by Dan Weld. Panelists: Xin Luna Dong, Yejin Choi & Kevin Jamieson


VC Panel moderated by Jay Bartot. Panelists: Tim Porter, Mike Miller, Pradeep Rathinam & Ankur Teredesai


Sector analysis

Vertical description

FinTech or financial technology is used describe new tech that seeks to improve and automate the delivery and use of financial services. ​​​At its core, fintech is utilized to help companies, business owners and consumers better manage their financial operations, processes and lives by utilizing specialized software and algorithms that are used on computers and, increasingly, smartphones.When fintech emerged in the 21st Century, the term was initially applied to technology employed at the back-end systems of established financial institutions. ​Since then, however, there has been a shift to more consumer-oriented services and therefore a more consumer-oriented definition. Fintech has expanded to include any technological innovation in — and automation of — the financial sector, including advances in financial literacy, advice and education, as well as streamlining of wealth management, lending and borrowing, retail banking, fundraising, money transfers/payments, investment management and more.

How big an opportunity space is this, how is it growing, and what’s driving that growth?  

The global banking industry is worth $8 trillion. Americans have $5.5T saved in preparation for retirement. US financials services grouped with insurance makes $4.6T in revenue each year with a 3% growth rate. Wealth management platforms are worth $1.9B and growing at 13%. The financial advisory market is worth $55B and growing at 5%. The personal finance software industry is expected to be worth $895M this year, growing at 6% annually. The P2P lending market will be $460B in 2022 and is growing at 51% each year. The financial app market is growing 13% each year and will be worth just over $100B in 2019. Growth is spurred by technology adoption, economic cycles, and consumer demand.

What are the segments/pockets?

The most useful way to segment the focus area is by financial service provided, working your way through the banking industry. Budgeting, banking, insurance, mortgages, retirement, tax planning, estate, investing, etc.

What is the technology spend and trend in this category, or the revenue growth rate of companies in the category (whichever is applicable)?

Banking and securities are the highest spenders on IT as a % of revenue at 7.16%

Certain segments within FInancial services are spending less that others, capital markets is spending much less  than average and growing slower. Consumers have shown they’re willing to spend on new technology solutions and finance businesses have begun capitalizing on this trend.

What are the proof points that success may be rewarded?

At a high level, what problems are there to be solved using technology?  

What current trends are driving change in this category?  

How specifically can ML/AI change the game in this category?  

Investment hypothesis / rationale

The average  consumer is becoming more cognisant of their finances and in turn will continue to seek new services to help them. This focus area is historically rich with right tailed opportunities and likely has many more to come. Everything man does is governed in some way by money.

What adverse conditions / headwinds are there for a play in this space? What makes it difficult?

Data sets

Your novel business idea should be grounded in real-world data with plausible machine-learning/analytics on top. We've compiled a collection of datasets from which to gain inspiration. Note that you are not restricted to basing your idea on the data sets below. You may discover other open source data sets that inspire your creativity or you may bring your own proprietary data sets if you wish.

Many of the datasets below are from Kaggle, Figure-Eight (Crowdflower), Data.World, etc. The advantage of these datasets is that many have been cleaned and normalized and are ready to be explored with ML and data science tools. Note that the use of these datasets is often intended for research purposes only. Be sure to read any associated license agreements to understand if there are commercial restrictions if you plan to continuing using the data after the workshop is over.

Sample Data Sets

The FDIC is often appointed as receiver for failed banks. This list includes banks which have failed since October 1, 2000

Idea: Build a model that identifies key markers and points to banks that are likely to fail over the next 3 months, thus allowing regulators to better target their resources.

Using 8 years daily news headlines to predict stock market movement

All Ethereum data from the start to May 2017

AssetMacro offers Free Historical Data for Leading Indicators of Economies and Market Data for Stocks, Bonds, Commodities and Currencies

Tweets from verified users concerning stocks traded on the NYSE, NASDAQ, & SNP

Using 8 years daily news headlines to predict stock market movement

Historical daily prices and volumes of all U.S. stocks and ETFs