Wealth-Watcher

Background

We place a lot of trust in our financial institutions. We trust Equifax to keep our credit reporting safe. We trust Wells Fargo to keep our financial records accurate. And I personally trust US Bank to monitor my checking account for potential fraud, and if it happens, to explain why it happens.

We _mis_place a lot of trust in our financial institutions.

Of course Equifax has allowed breach after breach. Wells Fargo decided to open accounts we didn't know we needed. And a couple months ago US Bank sent me a vague message telling me I should check my recent statement for potential fraud. This message came without any explanation, or justification, or really any information at all.

The world of finance can be pretty incomprehensible to begin with--and when financial institutions are opaque or dishonest it can leave users feeling confused and concerned.

That's why we were really excited to team up with a financial company that has led the way in transparency and access, and an analytics company that has revolutionized how we make sense of seemingly incomprehensible data.

The result of all that was Wealth Watcher--Security You Can Understand.

About

Wealth Watcher allows consumers to monitor their financial accounts for potentially fraudulent behavior in a transparent and easy to understand way. I'll see a transaction is flagged, but I'll also get an explanation, and the option to tweak what kinds of behavior I want flagged.

Technology Incorporated

MicroStrategy Visualizations

We use MicroStrategy Visualizations to give users a visual so they can better understand what's going on with their finances.

CapitalOne Nessie API

Users can easily link their CapitalOne bank accounts so all their financial data and transactions are there as soon as they log in.

AWS Backend

We want to be able to scale up and never leave users without access to the security and peace of mind Wealth-Watcher provides.

The Power of Machine Learning + The Clarity of Rules

Machine learning is a powerful tool for anomaly detection, but many models are black boxes that gives us about as much transparency Lehman Brothers. We pair this power with a rules based approach to gives users real explanations as to why certain transactions might be

Python/Django

We wanted our app to be easy to maintain and extend. That's why we opted for a Python/Django stack.

Docker

Docker containers provide the perfect bridge from our django app to the AWS backend.

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