Many articles and blogs, including our own, have shown how graph databases can be used to look at financial transaction data to see if particular individuals or organizations are involved in money laundering or other kinds of fraud. In this blog I will expand on this issue and explain how institutions can use Objectivity’s ThingSpan and GraphX, running on Apache Spark, to tackle detect financial fraud more quickly and efficiently.
In a typical scenario, investigators are trying to determine the money trail initiated by the perpetrator(s). This is a very simple navigational query using a graph database, along the lines of “Starting at the Person_X vertex, perform a transitive closure using Financial_Transaction edges and any kind of vertex.”
That is great if we already know that Person_X is of interest, but what if all we have is a huge graph of recent and historic financial transactions garnered from multiple sources, such as banks, exchanges, real estate transfers, etc.? The problem evolves from being a simple query to being a Big Data analytics one. We are now interested in pattern-finding, not path-following.
This is the third and final installment in a blog series examining exchange markets and how they are a good use case for graph databases.
In college, I was in a student group that wrote a research paper about redesigning election systems, which were traditionally dominated by two parties, to include viable third-party candidates. This was inspired by the two consecutive failed presidential bids by third-party candidate Ross Perot in 1992 and 1996. We found that the simplest way to reasonably include third-party candidates was to allow voters to rank two or more candidates, instead of voting for a single candidate.
The problem though was that if most voters preferred the same second-rank candidate, then that second-rank candidate would likely be elected. You can imagine in this scenario that candidates might purposely pursue becoming the second-rank candidate in order to win. In a market design like this, most candidates and political strategists would attempt to exploit the weaknesses of the system to engineer the outcome.
This is the second installment in a blog series examining exchange markets and how they are a good use case for using a graph database.
Civilization has designed exchange markets since the first early humans made stone tools and others recognized their value. In fact, non-monetary exchange markets predate all other markets by such a great deal of time that you might say that we are inherently predisposed towards them. Of course, transacting in exchange markets can be extremely difficult, because value is subjective, and we have a tendency to overvalue what we have. Successfully making a trade often involves tedious work to first find potential participants and then convince them to close the deal.
This challenge is well illustrated in an episode of the animated show, “My Little Pony: Friendship is Magic™,” titled “Trade Ya!” where one of the ponies named Rainbow Dash wants to trade for a rare book held by a book seller. In the Rainbow Falls Traders Exchange, no money is used. Instead, the ponies must trade only for goods or services. Rainbow Dash brings her most valuable possession, her lucky horseshoe, to trade for a first edition copy of the Daring Do book. Unfortunately, the book seller does not find her horseshoe valuable, so the rest of the episode involves Rainbow Dash going from vendor to vendor to enact a series of trades starting with her “valuable” horseshoe and ending with the rare book.
This is the first installment in a series of blogs examining exchange markets and how they are a good use case for using a graph database.
Bartering may seem like an ancient practice, but we all grew up exchanging non-monetary goods in a marketplace. For me, it was baseball cards, but for many it was records or clothes. We all intuitively know how to transact in markets when we want or need something, and the supply is limited.
An exchange is a market in which non-monetary goods are traded. When you trade in exchange markets, the matching is typically very rare and the value of the goods is generally very subjective. For example, my “Wade Boggs” rookie card may not be valuable to me but may be very valuable to my cousin’s friend. Networking connections and attracting people who want to transact is key to what keeps exchanges alive. Finding matches to ensure transactions are happening is what attracts users to an exchange.