The Problem

The financial industry is full of advisers. How do we know which firms to trust? How should regulators know which advisers are worth looking into?

There are also many publicly available resources to streamline data collection on financial firms and advisers, but most aren't used due to inherent complexity.

Goal

Find a way to simplify the public adviser data to make it easier to read for both potential investors and for regulators.

Our Creation

Advised

An iOS app that retrieves and aggregates data from SEC filings. It utilizes many publicly disclosed factors such as complaint history, violation logs, number of jobs, and years in the industry to generate one core risk coefficient.

Both potential advisers as well as regulators can use the application to find and discover potential investors they may be interested in.

Technical

We used a python script to pull and interpret the iAPD SEC data set. It filters out unnecessary information and focuses on factors that would tell us more about the adviser's record. This ultimately is put through an algorithm to generate the risk coefficient. A higher coefficient would mean that trusting this adviser is more risky.

On the iOS side, we made a local copy of all of the data using CoreData. We then began visually representing the data in iOS using UIKit and Core Graphics. Among our most difficult technical challenges was to successful import such a large amount of data, and providing meaningful ways to present it. Our iOS app focused mainly on design and user experience, to provide an intuitive way for users to interact with the adviser data set.

Extension

Our app and processed information can be used in multiple ways. We published our filtered information including the risk coefficient publicly online as well in more conventional JSON format. which allows future developers to continue to improve on it.

Using a subset of the same data, we also created a d3.js animation that finds the average risk factor of each firm using the information about the advisers from those firms. This gives us a more complete picture about not just specific advisers, but also their operating environments, whether or not certain firms are more likely to engage in or reward more risky/stable behavior.

In addition, because we are posting our filtered data set publicly, we encourage and hope future developers to continue to improve on the data.

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