https://www.helpnetsecurity.com/2018/05/15/graph-analytics/
Dirty money and money laundering
have been around since the existence of currency itself. On a global level, as
much as $2 trillion is washed annually, estimates the United Nations. Today’s
criminals are sophisticated, using ever-adapting tactics to bypass traditional
anti-fraud solutions. Even in cases where enterprises do have enough data to
reveal illicit activity, more often than not they are unable to conduct
analysis to uncover it.
As the fight against money
laundering continues, AML (anti money laundering) compliance has become big
business. Global spending in AML alone weighs in at more than $8 trillion, says
WealthInsight. This figure will continue to grow, considering how any
organization facilitating financial transactions also falls within the scope of
AML legislation.
But combating crime is never
easy. Especially when organizations face pressing needs for cost reduction and
faster time to AML compliance in order to avoid regulatory fees. Legacy
monitoring systems have proven burdensome and expensive to tune, validate and
maintain. Often involving manual processes, they are generally incapable of analyzing
massive volumes of customer, institution and transaction data. Yet it is this
type of data analysis that is so critical to AML success.
New ideas have emerged to tackle
the AML challenge. These include: semi-supervised learning methods, deep
learning based approaches and network/graph based solutions. Such approaches
must be able to work in real time and handle large data volumes – especially as
new data is generated 24/7. That’s why a holistic data strategy is best for
combating financial crime, particularly with machine learning (ML) and AI to
help link and analyze data connections.
Graph analytics for AML
Graph analytics has emerged at
the forefront as an ideal technology to support AML. Graphs overcome the
challenge of uncovering the relationships in massive, complex and interconnect
data. The graph model is designed from the ground up to treat relationships as
first-class citizens. This provides a structure that natively embraces and maps
data relationships, even in high volumes of highly connected data. Conducted
over such interconnected data, graph analytics provides maximum insight into
data connections and relationships.
For example, “Degree Centrality”
provides the number of links going in or out of each entity. This metric gives
a count of how many direct connections each entity has to other entities within
the network. This is particularly helpful for finding the most connected
accounts or entities which are likely acting as a hub, and connecting to a
wider network.
Another is “Betweenness,” which
gives the number of times an entity falls on the shortest path between other
entities. This metric shows which entity acts as a bridge between other
entities. Betweenness can be the starting point to detect any money laundering
or suspicious activities.
Today’s organizations need
real-time graph analytic capabilities that can explore, discover and predict very
complex relationships. This represents Real-Time Deep Link Analytics, achieved
utilizing three to 10+ hops of traversal across a big graph, along with fast
graph traversal speed and data updates.
Let’s take a look at how
Real-Time Deep Link Analytics combats financial crime by identifying high-risk
transactions. We’ll start with an incoming credit card transaction, and
demonstrate how this transaction is related to other entities can be
identified:
New Transaction → Credit Card → Cardholder → (other) Credit Cards → (other) Bad Transactions
This query uses four hops to find
connections only one card away from the incoming transaction. Today’s
fraudsters try to disguise their activity by having circuitous connections
between themselves and known bad activity or bad actors. Any individual
connecting the path can appear innocent, but if multiple paths from A to B can
be found, the likelihood of fraud increases.
Given this, more hops are needed
to find connections two or more transactions away. This traversal pattern
applies to many other use cases – where you can simply replace the transaction
with a web click event, a phone call record or a money transfer. With Real-Time
Deep Link Analytics, multiple, hidden connections are uncovered and fraud is
minimized.
By linking data together,
Real-Time Deep Link Analytics can support rules-based ML methods in real time
to automate AML processes and reduce false positives. Using a graph engine to
incorporate sophisticated data science techniques such as automated data flow
analysis, social network analysis, and ML in their AML process, enterprises can
improve money laundering detection rates with better data, faster. They can
also move away from cumbersome transactional processes, and towards a more
strategic and efficient AML approach.
Example: E-payment company
For one example of graph
analytics powering AML, we can look towards the #1 e-payment company in the
world. Currently this organization has more than 100 million daily active
users, and uses graph analytics to modernize its investigation methods.
Previously, the company’s AML
practice was a very manual effort, as investigators were involved with
everything from examining data to identifying suspicious money movement
behavior. Operating expenses were high and the process was highly error prone.
Implementing a graph analytics
platform, the company was able to automate development of intelligent AML
queries, using a real-time response feed leveraging ML. Results included a high
economic return using a more effective AML process, reducing false positives
and translating into higher detection rates.
Example: Credit card company
Similarly, a top five payment
provider sought to improve its AML capabilities. Key pain points include high
cost and inability to comply with federal AML regulations – resulting in
penalties. The organization relied on a manual investigative process performed
by a ML team comprised of hundreds of investigators, resulting in a slow,
costly and inefficient process with more than 90 percent false positives.
The company currently is
leveraging a graph engine to modernize its investigative process. It has moved
from having its ML team cobble processes together towards combining the power
of graph analytics with ML to provide insight into connections between
individuals, accounts, companies and locations.
By uniting more dimensions of its
data, and integrating additional points – such as external information about
customers – it is able to automatically monitor for potential money laundering
in real time, freeing up investigators to make more strategic use of their
now-richer data. The result is a holistic and insightful look at its colossal
amounts of data, producing fewer false positive alerts.
As we continue into an era of
data explosion, it is more and more important for organizations to make the
most in analyzing their colossal amounts of data in real time for AML. Graph
analytics offers overwhelming potential for organizations in terms of cost
reduction, in faster time to AML compliance and most importantly, in their
ability to stop money laundering fraudsters in their tracks.