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organisation. However,
because of
the difficulty
of calculating non-financial risks, this study will only look
at expectations of financial impact and the motion and time study in the event of down time in the BI-RTGS system.
This information
on down
time risk
is expected
to provide a tangible picture for the Bank Indonesia management
in calculating the operational risk in the BI-RTGS system, which
can then
be put
to use
in pricing
policy. The
determination of
RTOs will
also provide
a basis
for Bank
Indonesia to
maintain transparency
toward BI-RTGS
system participants concerning risks and fulfilment of the BI-RTGS
system service level agreement.
1.2. Study Objectives
As explained above, down time risk and determination of RTOs
is fundamental
to development
of a
BCP Concept.
Accordingly, the objectives of this study are:
1.
To provide an overview of the development and operational risks of the BI-RTGS system.
2.
To examine risks in the event of down time in the BI- RTGS system and possible impact that may result.
3. To study the Recovery Time Objectives from the angle of
expected financial
impact and
technical aspects
concerning the
time required
for the
system recovery
process.
1.3. Methodology
August 2004
In broad
terms, this
study applies
the descriptive
method. This method is used to help explain and describe the operational
characteristics of
the BI-RTGS
system, ranging
from transaction growth and operational risks to financial impact in the event of downtime.
The data
used is
secondary data
obtained from
the payment
system database,
the BI-RTGS
system operational
logbook and
literature studies.
The scope
of the
data analysis
begins with
the system
operational data
for the
2003 period.
Data from
2003 was
selected with
the expectation that it would explain the cyclical or seasonal
nature of transaction data in the BI-RTGS system over a one year
period. To
ascertain the
time required
for system
recovery, primary data was obtained from observations during trial runs of the Disaster Recovery Plan in 2004.
To obtain a more detailed picture of the possible impact of
downtime at
member banks,
information was
gathered through
in-depth interviews
at a
sampling of
four leading
banks: Bank Central Asia, Bank Mandiri, Lippo Bank and ABN Amro
Bank. This
sampling was
determined on
the basis
of number
and diversity
of transactions
conducted by
those banks using the BI-RTGS system. This selection of banks was
expected to be representative of the pattern and diversity of transactions conducted in the BI-RTGS system.
The RTO
analysis from
the financial
side uses
the
August 2004
Business Impact Analysis approach, which studies the degree of financial impact from down time risk. Down time risk is
calculated using the following formula:
Determining Down Time Risk Tom Pisselo, 2002
1
Potential Down Time Cost = average transactionsday x average nominal transaction value or
Potential Down Time Cost = number of peak hour transactions x average nominal transaction value
To calculate
potential down
time risk,
two different
transaction periods will be used: monthly and daily. These periods
are analysed
separately because
the monthly
data contains
cyclical or
seasonal data
for December
while the
daily period was chosen because a one-week period contains one day that is cyclical or seasonal, i.e. Thursday.
To determine down time cost, it is also possible to assess the impact of down time on Bank Indonesia’s internal
systems.
Down Time Risk for Bank Indonesia’s Internal Systems
Down Time Cost = Bank Indonesia revenues from RTGS system transactions
From a technical angle, the RTO analysis will apply the Motion and Time Study method by calculating the time taken
for each recovery process in the existing BI-RTGS system. This analysis is used as an added reality check for the Time
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Recovery Objectives
that need
to be
determined. The
time calculation
method is
based on
the results
of a
trial conducted in conjunction with this RTO study in 2004 using
various test
scenarios for
the BI-RTGS
system Disaster
Recovery Plan.
1.4. Scope of the Paper
