IBM Systems Magazine, Mainframe - November/December 2013 - 30

Moving down the management
and control chain are decisions
with a greater degree of repeatability. Taylor classifies decisions
such as determination of staffing
levels-when to start or stop a
promotion, etc.-as tactical. These
are decent candidates for automation because they need to be
made on a regular basis and are
driven by a repeatable process,
but their volumes aren't typically
high enough to make the business
case for investing in tactical decision automation.
The most practical decisions
for automation are those Taylor
calls operational-the ones that
typically relate to an individual
customer or a single transaction.
They answer questions like: Is
this claim valid? Should I offer
this customer free shipping
for this order? Should I raise
this customer's credit line and
approve this charge?

These are by far the most
repeatable and consistently
processed types of decisions.
Although they might be of lower
individual value than strategic
or tactical decisions, because
they're made on the order of
millions or billions of times a
year, they're extremely valuable
in the aggregate.
If you agree with Taylor and
begin your predictive analytics
journey by focusing on operational decision automation,
you're looking for business
processes that are highly transactional, repeatable, uniform
and consistent. If this is starting
to sound like familiar territory,
it should. These operational
business processes are probably being handled by your
mainframe's online transaction
processing (OLTP) and relational
database management systems
(RDBMSs).

good illustration of the value of automating
operational decisions by injecting predictive
analytics into existing systems is the identification
and prevention of claims fraud. Most healthcare and
property/casualty insurers write off up to 10 percent of
claims paid as being fraudulent. They will typically take
action after the fact, identifying which claims were likely
fraudulent and referring them to a special investigation unit
(SIU) for follow-up.
Is this a good use case for decision automation? Claims
certainly qualify as operational decisions. Each is a personal,
repeatable transaction performed against a uniform and
consistent set of policies. The goal is to train the claims
system to predict-with a high degree of accuracy-which
are likely to be fraudulent before they're paid, without
adversely affecting legitimate claims. For a large insurer
processing upward of 1 million claims a day-of which up
to 100,000 are potentially fraudulent-the aggregate value
of stopping fraudulent payments in advance can be a very
compelling business case for decision automation.

A

30 // NOVEMBER/DECEMBER 2013 ibmsystemsmag.com

Not All Data Is
Created Equal, Either

Read More
Analytics on
System z

Operational decisions, being of a
transactional nature, very often
involve customers waiting for the
decision to be made-meaning
they have to be made fast, in real
time. The first step in developing
a decision management system is
identifying the source of the realtime data being analyzed.
All decisions are driven from
various data, but one source will
often dominate. For operational
decisions, the main source is
usually transactional systems,
an assertion backed up by such
research as the January 2013
Forrester study, "Banking On
Analytics," (ibm.co/VDBje7)
conducted on behalf of IBM.
In it, executives from financial
services firms indicated they
are most focused on driving
analytics against their business
transactional systems, which

IBM maintains a comprehensive reference architecture
for fighting fraud at every point in the policy lifecycle. A key
decision point is determining whether a claim is fraudulent.
When an SIU makes that decision post-payment, it's more
tactical in nature and difficult to automate. But when it's made
pre-payment, at claims intake, the decision is automatable
as long as the analytics are closely aligned with the claims
system and data. With claims processing, the most effective
strategy involves deploying predictive analytics on the
mainframe-as close to the claims system as possible.
IBM has tailored solutions to the healthcare and insurance
industries that incorporate its analytics packages and
technologies, such as SPSS* in-transaction scoring,
combined with models that incorporate decades of industry
expertise in fighting fraud. These solutions leverage the
integrated, cost-effective, and easy to deploy infrastructure
provided by the IBM zEnterprise* Analytics System, which
includes the IBM DB2* Analytics Accelerator. For more
information, see www.ibm.com/systems/z/solutions/.
-P.D.


http://public.dhe.ibm.com/common/ssi/ecm/en/zsl03225usen/ZSL03225USEN.PDF http://ibm.co/VDBje7 http://www.ibm.com/systems/z/solutions/ http://www.ibmsystemsmag.com

IBM Systems Magazine, Mainframe - November/December 2013

Table of Contents for the Digital Edition of IBM Systems Magazine, Mainframe - November/December 2013

Table of Contents
Editor's Desk: On the road with analytics
IBM Perspective: Extending IBM's commitment to big data and analytics
Trends: IBM Capacity Management Analytics for zEnterprise cost-effectively ensures optimal performance
IT Today: IBM DB2 Analytics Accelerator V4.1 expands the value of high-performance analytics
Case Study: The State of Consolidation: Oklahoma improves service levels by consolidating its IT on zEC12
Cover Story: Big Data's Big Impact: zEnterprise hybrid technology helps redefine business analytics
Feature: The System z Crystal Ball?: Teach the mainframe to predict the future using analytics and automation
Tech Corner: Alternative metrics deliver a more complete view of hardware for server RFPs
Administrator: QMF extends its analytics capabilities to help meet the demands of big data and mobile
Solutions: TestBase; Vanguard Offline
Stop Run: Historic property has much to teach retired IBMer
Reference Point - Global Events, Education, Resources for Mainframe
2014 Mainframe Solutions Edition Product Index
IBM Systems Magazine, Mainframe - November/December 2013 - Intro
IBM Systems Magazine, Mainframe - November/December 2013 - Cover1
IBM Systems Magazine, Mainframe - November/December 2013 - Cover2
IBM Systems Magazine, Mainframe - November/December 2013 - 1
IBM Systems Magazine, Mainframe - November/December 2013 - Table of Contents
IBM Systems Magazine, Mainframe - November/December 2013 - 3
IBM Systems Magazine, Mainframe - November/December 2013 - 4
IBM Systems Magazine, Mainframe - November/December 2013 - 5
IBM Systems Magazine, Mainframe - November/December 2013 - Editor's Desk: On the road with analytics
IBM Systems Magazine, Mainframe - November/December 2013 - 7
IBM Systems Magazine, Mainframe - November/December 2013 - IBM Perspective: Extending IBM's commitment to big data and analytics
IBM Systems Magazine, Mainframe - November/December 2013 - 9
IBM Systems Magazine, Mainframe - November/December 2013 - Trends: IBM Capacity Management Analytics for zEnterprise cost-effectively ensures optimal performance
IBM Systems Magazine, Mainframe - November/December 2013 - 11
IBM Systems Magazine, Mainframe - November/December 2013 - 12
IBM Systems Magazine, Mainframe - November/December 2013 - 13
IBM Systems Magazine, Mainframe - November/December 2013 - IT Today: IBM DB2 Analytics Accelerator V4.1 expands the value of high-performance analytics
IBM Systems Magazine, Mainframe - November/December 2013 - 15
IBM Systems Magazine, Mainframe - November/December 2013 - 16
IBM Systems Magazine, Mainframe - November/December 2013 - 17
IBM Systems Magazine, Mainframe - November/December 2013 - Case Study: The State of Consolidation: Oklahoma improves service levels by consolidating its IT on zEC12
IBM Systems Magazine, Mainframe - November/December 2013 - 19
IBM Systems Magazine, Mainframe - November/December 2013 - 20
IBM Systems Magazine, Mainframe - November/December 2013 - 21
IBM Systems Magazine, Mainframe - November/December 2013 - Cover Story: Big Data's Big Impact: zEnterprise hybrid technology helps redefine business analytics
IBM Systems Magazine, Mainframe - November/December 2013 - 23
IBM Systems Magazine, Mainframe - November/December 2013 - 24
IBM Systems Magazine, Mainframe - November/December 2013 - 25
IBM Systems Magazine, Mainframe - November/December 2013 - 26
IBM Systems Magazine, Mainframe - November/December 2013 - 27
IBM Systems Magazine, Mainframe - November/December 2013 - Feature: The System z Crystal Ball?: Teach the mainframe to predict the future using analytics and automation
IBM Systems Magazine, Mainframe - November/December 2013 - 29
IBM Systems Magazine, Mainframe - November/December 2013 - 30
IBM Systems Magazine, Mainframe - November/December 2013 - 31
IBM Systems Magazine, Mainframe - November/December 2013 - Tech Corner: Alternative metrics deliver a more complete view of hardware for server RFPs
IBM Systems Magazine, Mainframe - November/December 2013 - 33
IBM Systems Magazine, Mainframe - November/December 2013 - 34
IBM Systems Magazine, Mainframe - November/December 2013 - 35
IBM Systems Magazine, Mainframe - November/December 2013 - Administrator: QMF extends its analytics capabilities to help meet the demands of big data and mobile
IBM Systems Magazine, Mainframe - November/December 2013 - 37
IBM Systems Magazine, Mainframe - November/December 2013 - 38
IBM Systems Magazine, Mainframe - November/December 2013 - Solutions: TestBase; Vanguard Offline
IBM Systems Magazine, Mainframe - November/December 2013 - Stop Run: Historic property has much to teach retired IBMer
IBM Systems Magazine, Mainframe - November/December 2013 - Cover3
IBM Systems Magazine, Mainframe - November/December 2013 - Cover4
IBM Systems Magazine, Mainframe - November/December 2013 - Reference Point - Global Events, Education, Resources for Mainframe
IBM Systems Magazine, Mainframe - November/December 2013 - 2014 Mainframe Solutions Edition Product Index
IBM Systems Magazine, Mainframe - November/December 2013 - SE2
IBM Systems Magazine, Mainframe - November/December 2013 - SE3
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