In the first article in a
four-page report on IT systems in asset finance, Leasing Life
and Invigors examine whether lessors are reacting to growing
levels of bad debt with improved automated decisioning and single
platforms
Go deeper with GlobalData
As a result of the financial crisis,
lessors’ IT budgets are under pressure as never before. In last
month’s Leasing Life/Invigors Business Confidence survey,
38 percent of lessors expected expenditure on IT to be reduced in
the next six months with most predicting cuts of up to 25
percent.
So what are IT departments doing to respond to
these budget pressures, and how has the financial crisis reshaped
what IT investments are being made?
Interviews with bank-owned and independent
lessors, pan-European and UK-based institutions, have showed that
in the aftermath of the financial crisis, the back-to-basics
approach of most lessors has meant that existing leasing systems
are relatively well suited to the needs of the business since
nearly all of these systems are mature and have evolved over a
period of time.
However, away from the operational aspects of
leasing there is much more emphasis on portfolio management, which
is leading to a greater requirement for more, and better,
information management (IM). This, in turn, is leading Chief
Information Officers (CIOs) to invest in better analytic tools as
well as technologies that reduce overall infrastructure costs.
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By GlobalDataManaging bad debt
The sharp increase in bad debt
across the industry has brought a renewed focus on arrears
management. Some lessors believe this is better managed at a
customer level, rather than agreement or contract level.
After all, a leasing company will treat a
customer with one out of four agreements in arrears differently to
one where all four agreements are behind.
This highlights the importance of having a
single view of customer. Previously a holy grail for customer
relationship management (CRM), existing leasing systems provide
this reasonably well, so is this a non-issue?
Not according to the CIO of one pan-European
bank-owned lessor who cites two issues preventing lessors
capitalising on a single view of customer – the number of product
platforms and data quality.
For institutions with a mixed product base,
for example small ticket, middle ticket, commercial mortgages with
a mixture of loans, leases and instalment credit, there is always
the temptation for the business lines to want a system that is a
perfect match for each – the so called ‘best of breed’
approach.
The problem with this is incompatibility
between the platforms resulting in islands of information.
To obtain a single view of the customer, this
information has to be consolidated, and this requires costly
integration tools and increases the complexity of an already
complex IT infrastructure.
Single platform
By contrast, adopting a
single-platform approach enables a single view of customer to be
built in from the start. Some also believe it also provides a
better means of measuring (and hence understanding) the business,
as there is a “single version of the truth”.
With multiple platforms there is always the
risk of similar information being measured in slightly different
ways, which makes consolidation more complex, whereas a single
platform ensures there is no need for debate.
There are some downsides to a single-platform
approach – it may mean that some
products cannot be run on the system resulting in a market
opportunity being missed and, across the product base, it will
provide only a partial fit to the needs of the business.
However, the CIO of a UK-based lessor
considers that its systems have coped with all the products that it
needs to, that the fit to the business is “good enough”, and that
any constraints imposed by keeping the IT infrastructure lean this
way are more than outweighed by the benefits.
Data quality
The challenge in achieving a single
view of customer also involves data quality. While the old adage of
‘garbage in, garbage out’ is well known, ensuring that clean data
is captured by the system in the first place is easier said than
done.
Instilling the discipline of creating the
single view requires systems and processes that minimise the risk
of entering duplicate records.
Name and address checking at point of capture
is commonplace using systems such as Experian’s QAS, but most
lessors still rely on operator judgement in matching new to
existing records.
Some lessors are taking this a stage further
in removing the decision from the operator by using a new
generation of software based on natural language processing.
To obtain a single view of the
customer, this information has to be consolidated, and this
requires costly integration tools and increases the complexity of
an already complex IT infrastructure
These include specialist products such as
HiQuality Suite and Data Improver from Human Inference, which also
improve the standardisation and overall quality of customer
data.
Automated decisioning
With underwriting policies being
tightened across the board, automated decisioning is coming under
the spotlight.
Again, it is not necessarily the processes
adopted that are the issue, rather the data on which they are
based.
Data from credit reference agencies is
inevitably retrospective and can at best offer limited prediction
of future customer behaviour.
This was acceptable in relatively stable
economic circumstances. But the impact of the recession and the
associated increase in uncertainty have made some lessors question
the validity of historical financial data in the credit decisioning
process.
Recently, Patrick Gouin, head of hi-tech at SG
Equipment Finance, commented that in some instances lessors were
“switching off” their automated decisioning systems in favour of
reverting to manual underwriting.
While this may be practicable where volumes of
new business are very low, in the longer term it is unlikely to be
cost-effective for lessors operating in the high-volume,
small-ticket business.
Analysis: Benefits of
analytics
In these recessionary times
lessors are investing in tools that help to highlight over-
and
under-performing parts of portfolios
Richard Ryan and Nic
Evans
