DAVID STEWART: The question I'm asked most frequently
is how can we use analytics to make our transaction monitoring
processes more efficient and more effective?
The financial industry has invested billions
of dollars over the last 10 years
to implement systems to automate the monitoring
of financial transactions for the likelihood
of potential suspicious activity,
yet the industry is reeling from continuously escalating costs
largely associated with high false positive rates
and the additional staffing required
to work the widgets that these transaction-monitoring systems
generate.
And given the rapidly changing regulatory environment, how can
financial institutions use these systems
to keep up and keep pace and satisfy
the current state of 100% coverage
of all potential risks?
So what I'd like to talk about today
is how can analytics be used to help make these systems more
effective and more efficient.
So it's kind of a three-pronged strategy from where we sit.
It's aggregate, segment, and visualize.
What do I mean by that?
Well, if you think about the current state of aggregation,
most financial institutions have transaction-monitoring systems
that generate work items or the dreaded word
"alert" at an account level, and if they're
lucky at a party level.
So most of these strategies are designed to generate work items
or alerts for known behaviors.
And this is commonly referred to as the "prescriptive approach."
Prescribed by the good doctors at various regulatory agencies
and that have been widely published
throughout the industry.
Financial institutions feel they have
to monitor for these known red flags
as if it's the cost of doing business.
However, if we look at from an efficiency standpoint
these strategies generate high false positive rates.
We believe a best practice is to take
more of a behavioral approach.
Of course there are certain red flags
that you'll always have to monitor for, but also
balance with more behavior-based monitoring,
not just at an account or party level,
but at groups of parties, or even at network entities
so that your analysts and investigators are
working a subject rather than multiple work items.
In order to do good, effective behavioral analytics,
you need to be able to properly segment your customers.
Historically, financial institutions
didn't have the analytic or statistical talent on hand
to create these peer groups.
They look to vendors to provide out of the box or black box
solutions.
These were largely based on business attributes,
risk attributes, or channels or line of business.
The problem with that is you tend
to monitor large groups of accounts or entities
that really aren't that similar.
SAS believes you should apply behavioral analytics
to build your peer groups based on proven statistical
techniques, clustering algorithms that have been used
throughout the industry for 30 years,
build your peer groups based on the actual manner in which
the customer or entity is doing business
through your institution.
Once you have that good view into proper segments,
then it's more of a spear-fishing approach,
and in the field we've seen a northeastern bank improve
their SAR conversion rates by 2 and 1/2 times
simply by creating their commercial monitoring
strategies up into 10 different peer groups,
and we've seen tremendous efficiency gains.
So aggregation and segmentation will help you with efficiency,
but how can we be more effective?
One of the biggest problems with transaction-monitoring systems,
especially with the OCC's emphasis
on model-risk governance, is there's
so much rigor and thought and process that
goes into monitoring for a new risk or strategy.
Based on rapidly changing geopolitical events,
geographic targeting orders, different regulatory actions,
financial institutions need to be more agile.
In most shops today, if I'm the AML officer
and I go to my IT department or my head of AML operations
and ask them what's our exposure to money remittances in Yemen?
How do they answer that?
That might be a week or two-week exercise.
And if they're using the data that's
provided by my transaction-monitoring system,
that's at least 30 days in arrears,
Today there are Big Data analytics technologies
available to us that allow us to be more proactive.
That's why we see the leading financial institutions creating
the role of a Financial Intelligence Unit, whose job it
is every day to come into work, scan the media,
look at adverse media events and other sanctions
and other targeting events and ask the question, what's
our exposure?
Do we bank that customer?
Do we bank people that do business
with that person that's in a negative a media event?
So this more proactive, agile approach
is necessary if we're going to be much more effective
at managing a reputation risk.
So when people ask me how can we use
analytics to help our transaction-monitoring systems
be better?
It's aggregate, segment, and visualize.