Nedbank has upgraded its
data monitoring systems after external auditors found horrendous
differences in the retail unit’s analytics platforms. Nedbank’s
head of retail decision science support, Eugene Liebenberg,
discusses the challenge of transforming de-centralised data
monitoring systems into one single platform.
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With responsibility for all marketing, attrition and
prediction data models in Nedbank’s retail unit, head of retail
decision science support (DSS) Eugene Liebenberg was in charge of a
pile of de-centralised data systems.
His team wanted to improve
their analytics models and methodologies, so Monocle Solutions, a
risk assessment and optimisation firm, audited the
models.
“What they came back with
was, basically, quite horrendous,” Liebenberg told
RBI.
“They found most of the units
were de-centralised, they did not follow the same methodologies and
many of the big operational models based within Nedbank were saved
on normal desktop computers.
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By GlobalData“Nobody knew who had
developed the models. Some of them did not have owners, so if there
had been an issue, no one would have known who to go to or how to
fix the problem. And some models also did not have any
documentation on them.”
Liebenberg said that Nedbank
had been working with SAS for a decade and already deployed its
Predictive Analytics Suite to increase analytical choices for
marketing modellers and SAS Enterprise Miner for production
models.
So it “made sense to go with
SAS”.
The vendor provided Nedbank
with “a little roadmap to consolidate” their de-centralised data
models: SAS Model Manager.
According to a SAS
spokeswoman: “The system offers a range of models stored in one
place, so Nedbank’s intellectual property does not go onto peoples’
laptops and out of the front door.”
The spokeswoman said the
system helps predict customer behaviour, reduce churn, minimise
credit risk, combat fraud, reduce asset downtime, anticipate risk
and plan intervention methods.
Nedbank’s retail division began implementation of SAS Model
Manager in September 2009. It integrated the software in the unit’s
infrastructure within two weeks, deploying it for complete
lifecycle management.
Liebenberg said: “In Model
Manager, we can monitor the performance of all models over time. We
can see if a model is degrading or actually improving.
“A lot of people perceive
model management as just managing the models. It is not only about
that. Model management is managing your data, your infrastructure,
your software and your people.”
“If you don’t manage the
people that actually develop the models, you will not be able to
deploy in a short time. It would take three to four months. But
because we have the knowledge sharing and cooperation between the
whole business units within retail, everything is running quite
smoothly.”
Everybody in the retail DSS
team has access to Model Manager, so everybody can see what the
next step will be, who is signing off, and what documentation has
been done to date and what is outstanding.
Liebenberg said that this
communal access opens up “communication channels” among analysts,
while monthly cross-business discussions focus on how to improve a
current model.
“We previously couldn’t do
that,” he said.
Since the implementation of
Model Manager last year, the retail unit witnessed a “20 to 30%
improvement” in model performance.
But convincing the bank’s
analysts and executives to share knowledge and making them adapt to
the centralised model was “the most difficult thing to do”, he
said.
“Executives don’t understand
data monitoring reports or results that were developed out of
statistical models,” Liebenberg added.
“So part of the
implementation process was a mind shift, to understand what we were
doing, why and how we were changing it. It did not matter how you
did it [collect data], just as long as it worked.”
“A lot of the analysts,
including myself, never shared knowledge at some stage, because we
felt that the model we had built was ours and that people were
going to steal our work.”
“Making them realise that
sharing data and sharing modelling techniques would help them
improve their own skills was quite a challenge. It took about six
months for them to understand why we were centralising the
models.”
Liebenberg said that the
reluctance to share knowledge was not only across the bank’s
business units, but also within retail division itself.
“We had a lot of different
data departments within retail and even between them the analysts
did not want to share.”
He said that analysts
believed secrecy created job security because they were the only
ones who knew how the models worked.
But the unit did keep some
‘uniqueness’:
“What makes us [the retail DSS team] unique is that our
modellers file their own data as well,” he said.
