Monday, February 23, 2015

Weighted trust graph for authentication

During the hackathon (discussed in earlier post), I met with Gary.  He had come in to be play mentor, but since he couldn't devote enough time, ended up being a guest.

After engaging Neo4J in few meetups and understanding the database a bit, i was contemplating using Graph databases for authentication.  It might have consequent applications in fraud analytics too, where graph databases are used already[1]

During the discussion on the idea, Gary suggested to mould the idea differently and possibly using Trust networks/graphs, wherein each node (entities i.e. people, organisations etc) are related to each other through weighted directed relationships.  The weight of this relationship can be deduced in multiple ways, e.g. by periodic algorithms similar to search engine ranking algorithms or by asking people their trust level of others on a scale of 1 to x. x being a hypothetical standard scale that can be used as a yardstick across the network for determining level of trust.

While researching some more, I found that similar research has been done in this space [2], though applications are few to come by.

It was also pointed out in the discussion that banks don't really have a huge interest in preventing this crime.  the view was, since banks already provision for certain amount in their balance sheets for these "potential" thefts, they don't really bother so much.

I believe that the financial institutions as a single unit need to attack these fraud crimes by joining hands and leveraging best of research and technology for minimising the crime.  The technology exists for providing unto the moment information on these events, some more innovation and research is needed that can bring together the whole picture and look like a "solution"





[1]http://info.neotechnology.com/rs/neotechnology/images/Fraud%20Detection%20Using%20GraphDB%20-%202014.pdf?_ga=1.182367911.1656585956.1417700858
[2]http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.212.9978&rep=rep1&type=pdf

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