Technological Thoughts by Jerome Kehrli

I am presenting in this video NetGuardians' Big Data approach, technologies and its advantages for the banking institutions willing to deploy big data technologies for Fraud Prevention.

The speech is reported in textual form hereafter.

It keeps puzzling me to see how deploying Big Data Technologies in banking institutions for fraud prevention and other use cases seems to be so difficult.
A large number of such projects have simply failed over the past.
By failure I mean projects that, led to poor results, or exceeded the budget significantly, or even that have been simply cancelled.

When looking at why these projects failed, it always boils down to the two same major issues.

The first major issue is that extracting the required data to build the analytics use cases is a challenge on its own. Let's say the bank managed to extract the required data, which is only a technical problem, but cleaning, enriching, normalizing and re-modeling it for banking fraud use cases is a whole new project.

The second major issue is that technological mastery alone is not sufficient for Big Data projects to succeed.
Implementing data analytics use cases requires a strong involvement from business experts.
It always amazes me to see so many projects had the illusion that putting a dozen of gifted Data Scientists in a room for a few years would be sufficient. Without a clear business understanding, Data Scientists are blind and can go nowhere.

And then even with a clear understanding of these both challenges, deploying big data technologies for fraud prevention is a 10 months to 2 years project. At NetGuardians we typically deploy our technology at our new customers within a few weeks.

So how do we do that ?

First, we are using technology on the bleeding edge of the state of the art, not today's state of the art but tomorrow's, benefiting from the right data extraction approach and the right use cases.

In terms of technology, our NG|Screener platform is using key big data components underneath: ElasticSearch, Mesos and Spark.

Regarding the Data Ingestion System, we have developed at NetGuardians our Data Collection Framework that is simple, efficient and configurable.
Typical data extraction tools are either simple, or efficient or configurable. Our framework is all of that together, without any compromises.

Then, working with numerous financial institutions worldwide over the years made us understand the indispensable role of not only technology but also business expertise when it comes to developing Big Data analytics use cases.

Business experts in banking institutions are only hardly available, right ?
Not a problem for us, we have hired our own.
Today, we have our own business and risk experts with an impressive trackrecord in risk or other banking business departments.

At NetGuardians, we have this multi-competencies team that so many project struggle to build and together, we have designed and implemented the right use cases to make Big Data deployment projects happen smoothly at our customers, and bring them actual added value.

As a result, our customers are able to make sense of their available Big Data, save enormous amount of time, and implement the Big Data technology to proactively prevent growing fraud challenges.

From a personal perspective, I am utmost proud of what we have built, both in terms of technology and approach, as well as the privilege I have to work in a team with such brilliant minds and wonderful persons.