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The risk of money laundering spans the entire financial services ecosystem – banks, payment providers and newer cryptocurrencies, such as Bitcoin and Ripple and more. Given how much financial activity occurs every second, everyday, it’s important for banks and financial organizations to develop a robust AML strategy that is effective in stopping fraudsters in their tracks.
However, few people outside the AML compliance profession fully appreciate how hard it can be to get it right. Thankfully, there are new technologies such as graph analytics that can help. As we dive into this topic, let’s first consider key challenges contributing to this exceedingly difficult task.
Graph databases have gotten much attention due to their advantages over relational models (see discussions here). However, while different technical companies rush into this area, Amazon, Microsoft, Oracle, IBM, etc., it is getting more challenging to evaluate different vendor’s product when a project wants to embark a graph database. It is recommended that purchasers or practitioners take a grain of salt before embarking on the adventure of the graph database world! That does not mean it’s hard to find the right database. The criteria in this article contain some simple and achievable steps for readers to try and get the truth.
A traditional AML solution would not flag an alert for this new customer. However, graph-based features dig deeper to find that new account shares a phone number with several customers with SARs. The graph-based solution creates a new AML alert missed by the traditional AML solution, marking it high risk for further monitoring and investigation. In this way, our graph-based solution finds the false negatives, or hard-to-detect money laundering cases, that would be missed using traditional solutions.
IoT is “another type of network within which we can create applications that blend what people do within that network,” said Jeff Morris, head of product marketing at Neo4j. At the same time, “it’s not enough for businesses to just accumulate data—they also have to be able to act on it,” Yu Xu, CEO of TigerGraph, pointed out. “Today, most IoT users—businesses, governments—are collecting the data but have challenges making sense of it to drive value. Imagine a city that is better able to program traffic lights to improve traffic flow based on in-the-moment feedback after an accident. Saving 30 minutes in traffic is quite meaningful.”
Using linkage and network analysis enabled by the graph model, this web can be dissected with speeds and accuracy that were impossible using a relational database. Combined with conventional AML compliance tools, we can incorporate graph analytics to uncover key insights. This involves looking deep and hard at data from all types of sources — much like how our brains learn and recognize patterns of suspicious behaviors, activities and relationships — through intelligent analytics and advanced algorithms.
Fraud detection, in many ways, resembles finding needles in a haystack. You must sort and make sense of massive amounts of data in order to find your “needles” or in this case, your fraudsters.
Let’s use the example of a phone company with billions of calls occurring in its network, all on a weekly basis. How can it identify signs of fraudulent activity from its mountain — or haystack — of call logs?
The selected companies come from our massive data set of vendors and industry metrics. Yes, we use machine learning to analyze the industry in a detailed manner to determine a ranking for this list. We’re using a custom RankBoost algorithm adapted specifically for the big data community along with a plethora of propriety data sources.
When making a decision on which graph DB in which to invest, admins must take into account the development language the enterprise is going to use. There are different nuances among the database vendors for different use cases (some are more scale-out than others, for example), so organizations must scope this out ahead of time to make sure the most effective language and DB is selected for the corporate purpose.
Recently, we have seen the next-phase in the graph database evolution, with technology fulfilling the needs of e-Commerce by providing Deep Link Analytics. This enables customer intelligence in real time, along with powerful relationship analysis. With these real-time capabilities, e-Commerce sites can quickly synthesize and make sense of customer behavior. The result is the capture of key Business Moments, transient opportunities where people, businesses, data and “things” work together dynamically to create value used to personalize the customer experience, which leads to more transactions.
Today, enterprises use graph technology as a competitive edge for customer analytics, fraud detection, risk assessment and other complex data challenges. The technology offers the ability to quickly and efficiently explore, discover and predict relationships to reveal critical patterns and insights to support business goals.