This weekend I participated in the National Day of Civic Hacking.
The project I decided to work on was working with the CFPB data (and also used some census data).
The CFPB released a large complaints database that contained information about what type of financial products people are complaining about. It also gave information about where the complaints came from, what they were complaining about and resolution information. Some of the data was released literally a day earlier. So I was given a chance to take a look at, analyze and visualize information that nobody has really seen yet.
It was an exciting and interesting opportunity. Since it was very fresh data with little to no previous work, much of what I got to do was more general analysis. I created a handful of graphics (click them to see full size) and maps (click to use the map) which I have included below:
What products are people complaining about the most?
The biggest product people are complaining about is mortgage related products. There is a category for other mortgages that people can choose and it seems most people seem to select that. I wasn't sure why until I looked at the issues people were having.
What are the most common issues people have?
Foreclosure, Loan Modification and Collection. Intuitively, this makes a lot of sense. I probably don't care what type of mortgage it is when they are trying to take my house away. This particular issue dwarfed everything else, so I had to use a log scale to even see what the other issues people were facing.
What are the most common issues people have? (log scaled)
This gives a more in-depth picture of the issues, but the first graphic really shows you the most common and/or pressing financial issue for people.
Which companies had the most complaints?
Then I explored which companies were receiving the most complaints. This data is NOT normalized. That means that just because a company has more complaints doesn't make it worse than one with less. For example, if company A had 10 complaints and 100 customers and company B had 5 complaints and 20 customers, company B would be worse (if we measured complaints as a % of customers). I didn't have easy access to a database with any dataset that would normalize these banks, so this is for curiosity more than any meaningful insight. It probably is a proxy for the largest players in consumer finance though.
[MAP] Where are the complaints coming from in the US? (Normalized for state population)
This map shows where people are complaining the most. DC won that dubious honor. Maryland, Delaware, New Hampshire, California and Florida were also high on the list. All the numbers were adjusted to reflect complaints relative to population of a state. So we can clearly see there are differences and we can probably make educated guesses for some of them. DC for example is probably the highest because of the highest awareness of CFPB (since it's based in DC). Maryland probably has a high awareness too. Florida and California had big real estate bubbles and perhaps were hit especially hard. New Hampshire and Delaware are a mystery to me, although a woman from New Hampshire at my event told me that she complained to the CFPB and told all her friends as well. Perhaps an above-average awareness of the CFPB caused the higher complaint rate.
Top 10 Companies by Complaints and their Disputed Resolution Rates
Next I explored disputed resolutions. Companies alert the CFPB when the matter is resolved and consumers are allowed to tell the CFPB they were not satisfied with the resolution. I graphed the top 10 companies by complaint volume and what their disputed resolution rates looked like. It's interesting to see such big differences between companies but without further information about how they handle disputes, it's impossible to say anything confidently comparing one company to another.
[MAP] Disputed Resolutions by State
Finally, I created a map graphing disputed resolutions by state. There was a surprisingly large variation between states. Alaska, for instance, had a disputed resolution rate of over 26% while Wyoming had a measly 16%. I have no idea why, but it's interesting and worth looking into further.
I had a lot of fun exploring this fresh set of data and there is a lot more to be learned from it. I want to give a big thanks to Ana from the CFPB and Logan from the Census Bureau who attended the event and helped participants navigate the data provided their respective organizations.