When Milliseconds Matter:

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When Milliseconds Matter:

Architecting Real-Time Analytics Into Operational Systems

Jose Castano [email protected] April 28, 2015

Drive top-line growth via transformational new services

Dramatically “maximize yield” on investments in standard electronic business practices

Leaders know they must leverage data to

outperform, but are struggling to hit the mark

More sophisticated analytics techniques must be put in play IT must be exploited as a business strategy

80%

annual decrease in customer

lifetime value for

firms that don’t use engagement analytics of marketers send same content to all subscribers of businesses “extremely satisfied” with ability to use customer data for decisions typical banking regulation non-compliance fine estimated fraud loss to healthcare government tax revenues lost to non-compliance

6%

-4.3%

$226M

16%

$100M

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•Guide overall direction of the enterprise

•Should we expand overseas?

•Manage and control operations

•What product should we promote

today?

•Handle every customer interaction

•Is this payment request valid?

An enterprise analytics strategy must be able to support a

wide variety of decision types

Tactical Decisions Strategic Decisions D e c is io n F lo w F e e d b a c k L o o p Operational Decisions

How often is this decision made?

What is the value/risk?

How repeatable is this decision?

What type of analysis does the decision require?

How quickly must the decision be reached?

Source: James Taylor, Decision Management Solutions: Transforming Operational Systems From Transactional Systems to Decision Management Systems, 2015

But, outdated views of infrastructure can impede

progress …

Significant complexity Separated data warehouses Analytics latency

Transactional data is not readily available Lack of synchronization

Data is not easily aggregated and fresh Data duplication

Multiple copies of the same data Excessive costs

Of moving data around

Live Data Predictive Scoring Predictive Modeling

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An enterprise analytics architecture is required to

transform systems into Decision Management Systems

R ig h t-ti m e a n a ly ti c s

Consistent, accurate view of data for uniform decision results Richness of capability to support every decision type Range of responsiveness for time-to-value from days to msecs

Focus for today: injecting insights into operational

decisions

R e a l-ti m e a n a ly ti c s R ig h t-ti m e a n a ly ti c s

Operational decisions are high volume, low latency, highly repeatable and high value in aggregate

They require analytics that are closely linked to transactional systems so that decisions can be automated, in real-time Key considerations: richness of analysis and ability to maintain SLAs Operational decision management is a business discipline, supported by operational and analytics software, that enables organizations to automate, optimize and govern repeatable business decisions to improve the value of customer,

partner and internal interactions

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Focus on “Analytics in Business Applications” What is

“real-time analytics?”

This is the time during which a transactional event is still occurring Someone is shopping at a store Someone is on the phone with a customer service representative An insurance claim is being adjudicated on a processing system A purchase using a credit card is being processed

An electronic payment is being processed

…

It’s about leveraging the power of analytics “in the transactional moment” to achieve a more

favorable outcome for a transactional event, while the event is in progress The person visiting a store buys more than he or she otherwise would have

What would have been an over-payment is stopped before it gets out the door

The person on the phone, who was about to cancel a service, instead re-ups

A commission of fraud is stopped before it is effected

7 7

Use Case: enhanced “suggest-selling” at

mini-marts

A gasoline retailer operates mini-marts at its stations

The retailer has a loyalty program, and had collected a huge amount of data

about customers’ purchases, but didn’t really see how this data could be

effectively leveraged

Mini-mart employees were encouraged to “suggest-sell,” i.e., to suggest to

customers additional items they might want to buy

–Success of this program depended largely on individual employees’

knowledge of customers’ buying habits

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Enhanced “suggest-selling” at mini-marts:

Getting smarter, and getting faster

The gasoline retailer implemented SPSS to make sense of the vast amount

of purchase data – for sales in general and for sales to loyalty-program

customers

–Patterns were detected, and predictive analytics could be enabled

through models

The company also acquired a DB2 Analytics Accelerator, to get suggest-sell

queries executed in seconds – in time to enable suggestions before the

in-store checkout had completed

–Sales went up

–Customer satisfaction improved – data-driven purchase suggestions

were often what a customer actually wanted to buy but had forgotten

“Everything is done on the customer’s timetable, in seconds”

9

(CIO)

2. Drive one decision one interaction at a time

3. Maximize customer life-time value

1. Build long-term customer relationships

Real-time predictive customer intelligence leads to smart

business decisions at the point of impact

Banking Providers

Customer calls for password reminder on “self-trade” account. Pattern of usage shows limited activity and few returns; offer upgrade to managed investment offering.

Customer’s transactions show multiple trips to Asia; offer international emergency cover, and provide information about commission-free ATM withdrawals from our regional partner.

Insurance Providers

When confirming to a customer that his / her claim will be settled, direct him / her to a repair shop which, although it may take 1 – 2 days longer than other options, has higher quality ratings.

When a customer calls in with a coverage query, let them know they can save money by combining their two policies into a single multi-car one.

Telco Providers

•When a high-value, long-tenure customer calls with a bill query within six months of the end of his / her contract, offer him / her a complimentary handset upgrade if he / she renews in advance.

•Customer repeatedly called tech support with mobile Internet issues comes to the website to check process to transfer his / her number. Initiate a chat, check problems are now resolved, refund two months data charges by way of apology, and offer discount on a new handset.

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The worldwide cost of occupational fraud has risen 9% over the past

two years, to $3.7T USD annually

Sources: Association of Certified Fraud Examiners (ACFE) “Report to Nations”, 2012 and 2014

The cost of fraud is considerable – and rising, not falling – The typical organization loses 5% of its revenues to

occupational fraud each year (no change)

– The median loss caused by the occupational fraud cases in our study was $140K (up 4%)

– 20% of the cases were greater than $1M (up 2%)

Fraudsters are good at hiding their behavior

– The frauds reported to us lasted a median of 18 months before being detected (no change)

It is nearly impossible to recover fraudulent losses – 58% of the victim organizations had not recovered any of their

losses due to fraud, and only 14% had made a full recovery

Yet, most organizations still rely on “after the fact” methods

– Over 40% of all cases were detected by a tip – more than twice the rate of any other detection method

Prevent

Stop processing known fraud, or encourage fraudsters to abandon their objective by showing more is known than they think should be known about their activities and intentions

Discover

Discover fraud by retrospectively reviewing past data and identifying individuals or organizations that may be conducting fraudulent activities

Investigate

Gather data about fraudsters and/or schemes DETECTED or DISCOVERED; build cases for prosecution, recoveries, or denial of payments. Build watch lists and rules

Detect

Detect in real time if a transaction, request, application, document, etc. is potentially fraudulent by applying models and rules in real time

IBM’s fraud management point of view

Detect fraud within a business process

Take action in real time – when it matters

Find Fraud within the Data

Confirm fraud for prosecution, recovery, rules and watch lists

Prevent Detect

Investigate Discover

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Prevent

Stop processing known fraud, or encourage fraudsters to abandon their objective by showing more is known than they think should be known about their activities and intentions

Discover

Discover fraud by retrospectively reviewing past data and identifying individuals or organizations that may be conducting fraudulent activities

Investigate

Gather data about fraudsters and/or schemes DETECTED or DISCOVERED; build cases for prosecution, recoveries, or denial of payments. Build watch lists and rules

Detect

Detect in real time if a transaction, request, application, document, etc. is potentially fraudulent by applying models and rules in real time

IBM’s fraud management point of view

Detect fraud within a business process

Take action in real time – when it matters

Find Fraud within the Data

Confirm fraud for prosecution, recovery, rules and watch lists

Prevent Detect

Investigate Discover

Fraudster

z Systems optimization focus: best results based on location of operational data

Business

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Predictive Analytics O rc h e s tr a ti o n Transaction •Determine fraud risk •Continue •Pend/investigate, etc. Banking Transaction Initiated

Fraud rule engine •In place today

•Extend with additional insight from predictive analysis

IBM DB2 Analytics Accelerator scores evaluates Transaction Data Historical Transaction Data Model Account Data Historical Account Data

Business Goal: reduce loss due to card fraud, reduce card deactivation, grow revenue associated with card purchases, reduce call center costs, improve service yielding preferred card usage Approach: incorporate aggregate data from geographic location, merchant, issuer and card history into existing card authorization business flow to reduce fraud while preserving transactional SLAs

Integrated high performance query optimizations enable client to aggregate data several times a day and use this complex data as part of real-time fraud detection process

Enhance with predictive scoring integrated with fraud detection transaction for even more preventive capabilities Merchant

Use Case: Bank: incrementally enhance existing fraud

detect with predictive approach

Results of large, complex queries

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Intake

Claims Prepare Claims Adjudicate Claims

Deliver Benefit Recover Overpaid Claims Claims DB (DB2 z/OS)

Challenge 1: Complex reports for overpaid claims not

completing on time, result is monetary losses

Solution: Integrate optimized analytics of IDAA with

overpayment reporting of transactions

Benefits:

• Up to 2000x improvement in speed of overpayment reports • LOB users enabled to respond with more agility to

overpayment trends

• Informed decisions at the right time

Challenge 2: Stop improper payments prior to payment,

avoid pay & chase, meet SLAs

Solution: Integrate predictive analytics into claims

adjudication for analytics in place

Benefits:

• Very efficient scale for analytics

• Scale requirements only achievable with analytics as part of transaction flow

• Expected results of efficient in-transaction analytics can be multi-million dollars per year

Scoring adapter Descriptive Analytics leveraging IBM DB2 Analytics Accelerator System of Record

Use Case: Insurer: minimize loss from claims overpayments

1

2

Predictive Analytics leveraging IBM SPSS Modeler with Scoring Adapter for z Systems

Predictive analytics should be a key element of

your real-time decision strategy

Like the real world, predictive models are not binary

– Understanding how closely a pattern of behavior matches a known pattern of bad (or good) behavior can help uncover crimes or non-obvious opportunities

Predictive models detect patterns

– Deviation from expected behavior can isolate bad (or good) behavior, trigger additional actions or new targeted marketing and up-sell / cross-sell offers

Predictive models can have many variations

– Can be built to assess only specific transactions or more generically for all transactions

– Multiple layers of models can be invoked for increasing sophistication of analysis, triage leading to further inspection of contributing factors and weights

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Distributed data

environments

Starting point: where are the transactions and data that

support real-time operations located?

There’s a very high probability that your operations and data of record are maintained on a mainframe

– Worldwide, 92% of top banks, 84% of top insurers, 92% of top retailers, 90% of top airlines rely on the mainframe

– As do 71% of the Fortune Global 500

The mainframe processes roughly 30 billion business transactions every day 55% of ALL enterprise applications need the mainframe to complete transactions

How can we leverage the mainframe for incorporating analytics into operational decisions?

Operational Systems of Record: • Customer • Accounts • Payments • Purchases • Card • Claims • etc. © 2015 IBM Corporation 18 Customer Accounts Payments Purchases Card Claims etc. Process Orchestration Predictive Models Business Rules M o v e t h e d a ta t o t h e a n a ly ti c s network P la c e t h e a n a ly ti c s w it h t h e d a ta

The infrastructure you need: compare approaches

Can performance / throughput SLAs tolerate data movement and network traffic?

Can models integrate large volumes of historical data with incoming transactions to deliver the most accurate outcomes?

Can security for sensitive data be maintained across multiple zones? Can audit trails be maintained to satisfy regulations?

Can availability and BC/DR objectives be met?

Can 100% of transactions be richly analyzed without user impact?

Process Orchestration Predictive Models Business Rules Operational Systems of Record Customer Accounts Payments Purchases Card Claims etc. Operational Systems of Insight

Considerations:

Better approach?

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The key elements behind real-time operational

decisions

R e a l-ti m e a n a ly ti c s R ig h t-ti m e a n a ly ti c s

Predictive modeling, business rules and process flows together enable the most effective decisions

Integrating analytics with transactions transforms operational systems into Decision Management Systems

MODELS

RULES PROCESSES

INTEGRATED

Operational decisions (Real Time Analytics) Webinar

Operational decisions: high volume, low latency, highly repeatable and high value in aggregate

Require analytics that are closely linked to transactional systems so that decisions can be automated, in

real-time

Key considerations: richness of analysis and ability to maintain SLAs

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Analytics as part of the flow of business

Insights on every transaction

*z13 is the first mainframe system with embedded analytics providing real-time insights on all transactions. This capability can help guarantee the ability of the client to run real-time fraud detection on 100 percent of their business transactions.” [2]

Based on IBM internal tests comparing IBM zEnterprise Analytics System 9700 with a comparably priced, comparably tuned competitor Eighth Unit configuration (version available as of 12/31/2014), executing a materially identical 10 TB BIDAY “Fixed Execution” workload in a controlled laboratory environment. Competitor configuration: Eighth Unit including competitor recommended software options and features. IBM configuration: z13 platform with 1CP and 3 zIIPs with 128GB memory and DB2 Analytics Accelerator Full Rack (N3001-10) with 7 S-blades (140 Intel E5-2680v2 2.8GHz cores and 128 GB RAM), 2 Hosts (1 active – 1 passive) with 20 Intel E5-4650v2 2.4GHz cores each and 12 disk enclosures, each with 24 600GB SAS drives. Results may not be typical and will vary based on actual workload, configuration, applications, queries and other variables in a production environment. Users of this document should verify the applicable data for their specific environment.

“z13 is the first mainframe system with embedded analytics providing real-time insights on all transactions. This capability can help guarantee the ability of the

client to run real-time fraud detection on 100 percent of their business transactions.”*

z Systems: when analytics are business-critical

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Systems and Technology Group Route 100

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Produced in the United States of America February 2015

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