One of the company's key initiatives was using data analytics to
effectively increase customer retention rates in the Australian market. By
comparing historical transactions to 115 predictive churn variables, AMEX
can determine if an Australian customer will close an account within the
next four months. This allows the company to take immediate remedial
action to maintain these accounts.
Regulatory Compliance
Credit Suisse made compliance a top priority when it deployed its
advanced analytics and technology platform back in 2017. This platform
comprises a huge amount of data -- over 4,000,000,000 records.
More importantly, the data is constantly available and can be processed
in real time -- providing users with the ability to identify potential risks
before they can do any damage. This includes the ability to monitor
suspicious transactions or identify patterns of money laundering.
Lara Warner, Head of CCRO and Member of the Executive Board of Credit
Suisse, summed it up best. "Over the past two years we have gone from a
human-led approach to compliance, where we were carrying out periodic
checks, to a technology-led approach in which we are continuously
monitoring activities across the bank to enable earlier prevention and
detection."
For instance, the company has quicker -- and better -- insight into the
different relationships an international client has with Credit Suisse. In
fact, international client assessments are now made 80% faster than the
prior year.
"Having a single tool also enables the compliance organization to quickly
and efficiently respond to changes in the global regulatory landscape,"
she adds. "Both quantitative and qualitative compliance risk factors are
taken into account, allowing the bank to channel resources at short
notice towards high-risk areas.
Reducing Fraud
For most insurance companies, fraud continues to be a consistently
evolving, constantly changing threat that directly impacts their bottom
line.
THE NEED FOR SPEED | 7
Analytics 1.0
Traditional Analytics -
Rudimentary analytics
can be traced back to the
early 1950s. Driven
primarily by the larger
corporations, these
analytic methods were
inherently slow, manually
laden and constrained by
limited data sources. The
value of the analysis was
limited since any insights
derived from the data
came much later in the
process.
Analytics 2.0:
The Rise of Big Data -
Big data analytics began
to appear in the early
2000's. Driven by
computers, connectivity
and new data application
programs, this phase
witnessed an influx of
new -- and larger --
datasets. It also ushered
in the role of the data
analyst, an individual who
was responsible for
understanding and
reporting on data
activities and trends. This
period also began to
uncover inefficiencies
between the growing
volume of data and the
ability of traditional IT
infrastructures to handle
that myriad of data.
