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    Date submitted
  • 05-Oct-2017

Keel- Solution for Investment Data


Keel provides investment data and analytics at a superior quality. Using users’ banking credentials, Keel aggregates investment data from multiple sources and handles a wide variety of errors: stock splits, stock ticker changes, incorrect quantities, duplicate transactions, just to name a few. In addition to cleaning and manipulating investment data, Keel provides this in a secure yet easy-to-digest format for fintech applications.

Our applications include:

1) B2C marketplace that allows users to follow credible investors' live portfolios

2) B2B account aggregator plugin

3) B2B trade data analytics


Additional Questions

Who is your customer?

The B2C business is currently generating revenue by selling subscription to retail users. In the near future, we are looking to start selling our B2B solutions, which will bring revenue from two products: 1) Investment data solution: this includes Account Aggregator plug-in for regional banks or data solution for fintech startups. 2) Alternative data: Keel generates anonymized data for research companies who are looking to understand retail investing behaviors. For the B2C market alone, we are looking at a $12 billion addressable market size. That market estimate is based on 50 million self-directed investors and $240/year/subscriber Keel charges each self-directed investor.

What problem does this idea/product solve or what market need does it serve?

In the beginning, we wanted to build a platform to allow users to find credible investors and their live trades. We thought that we could easily build the platform , by working with existing financial data aggregators including Yodlee, Intuit and Quovo. We were wrong. The level of inaccuracy for financial data is surprisingly high. We have spent the past one year building error-checking features, handling various stock events, and normalizing data sets from multiple brokerages. Since we work with real users' data to generate historical returns, even one incorrect data point will cause an inaccurate historical return drop or spike on a user's performance chart. Because our business requires a high level of accuracy, we have really dug into each data point and developed a better data solution with superior quality. The longer we work on the data and the more companies we speak with who struggle with incorrect financial data, the more confidence we have for the market opportunity. Having gone through the data-cleaning process ourselves, we know we would be happy to pay for such data service if the solution exists. One investment banker once told us,"I've got lots of requests from different buyers, looking to acquire data aggregate solution." This demonstrates the demand for the data solutions in the fintech space.

What attributes will make this idea/product successful? Why do you believe that those features will create success?

Financial data aggregate exists in the market, however, the quality of the retrieved data is low. All of the aggregate services we tried fail to deliver correct data to a level Keel can build business applications that require return calculations and trade analysis. Right now, financial services and fintech startups use the three most established companies, Yodlee, Quovo and ByAllAccount, to aggregate data from multiple brokerages. From our conversations with many companies which use the aggregate services, most of them either try to clean data by themselves, or just ignore the incorrect data. By solving the data quality issue, we will be helping financial companies and startups to focus on their core business objectives by saving time and money cleaning data by themselves.

Explain how you (your team) will execute to make this idea/product successful? What gives you (your team) an advantage over others already in the market or new to this market?

Many people might think it's easy to aggregate financial data, but dealing with a large set of financial data and filling in the blanks is extremely challenging. The CEO of one of the aggregators we're using provided us with free access due to his increasing interest in the high-level analysis and data scrubbing we're doing. When calculating the financial returns, we had to build algorithms to calculate what an account balance was each day over the last few years. This involved handling multiple asset types, stock splits, stock ticker changes, and calculating investment returns for assets where historical data is often incorrect. Our data cleaning service checks for situations where there are missing trades, failed account syncs, missing prices, wrong quantity, wrong stock tickers, and more. All of these require technical skills, relevant domain knowledge, and a good amount of trial and error. Our team has a combined background in finance and technology. This helps us navigate through the complexity of investment data. Our deep understanding of both finance and technology also helps us identify various business opportunities and opens up the conversations with various parties.