Delivering Excellent Customer Experienceswith Analytics and Automation—Session #6945

Stephane MeryIBM DE, Digital Business Automation, Decisions Chief ArchitectJames TaylorCEO, Decision Management SolutionsRyan TrollipCTO, Decision Management SolutionsThink 2019 / 6945 / February 14, 2019 / © 2019 IBM Corporation

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Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.

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Please note

IBM Cloud 2019 / © 2019 IBM Corporation

Artificial Intelligence is a Key Driver to Product Innovation

and enhanced Customer Experience

of companies believe AI is key to competitive advantage94%

companies have extensively incorporated AI in their offerings or processes

5%

IBM Cloud 2019 / © 2019 IBM Corporation

What is Artificial Intelligence?

To get the value, AI must be made

actionable through the Automation

of business operational decisions.

Rule-based

systems

Operational

Systems

Natural

Language

Processing

Image

Recognition

IBM Cloud 2019 / © 2019 IBM Corporation

It is not prescriptive by itself

In other words, you can’t make decisions with ML alone

Use ML to get insights from your

historical data.

Use Business Rules to control how insights

are turned into action.

Machine Learning is a descriptive/predictive technology

to transform your data into insights

IBM Cloud 2019 / © 2019 IBM Corporation

https://www.forrester.com/report/The+Dawn+Of+Digital+Decisioning/-/E-RES141568

https://www.forrester.com/report/The+Forrester+New+Wave+Digital+Decisioning+Platforms+Q4+2018/-/E-RES141571

IBM Cloud / © 2018 IBM Corporation

Key Takeaways

Digital Decisioning Platforms are a new generation of systems that bring together

business context, processes, ML and rules to deliver intelligent decisions.

They are the “brain” of your digital operations

IBM Cloud / © 2018 IBM Corporation

Combine analytics with business rules

to implement your operational decisions

Provide the ultimate

business agility by

managing business

decisions outside of

applications

Execute your enterprise

operational decisions at scale

Use IBM Operational Decision Manager, the market-leading Decisioning Platform,

to inject intelligence in your business operations

A major insurance company in Asia is processing claims and takes approval decisions based on potential fraud score, historical similar decisions and prescriptive decision logic.

IBM Cloud 2019 / © 2019 IBM Corporation

EXPECTED BENEFIT

Most simple cases automatically processed.

Improve customer experience with no-delay payments.

UNIQUE CHALLENGE

Need to combine several predictive models (fraud, claim complexity…) with regulation-compliant and business-led decision logic

Balance automation and human control to maximize speed and reduce errors.

What Clients are saying?

✓ Decisions are everywhere, touching every steps of the Customer journey, from Marketing, to Sales, to Claims.

✓ Best decisions leverage insights from the past and business knowledge and regulation.

✓ All operational systems should be supported by a “Decision Hub” that brings data, ML and business rules to automate decisions.

10© 2019 Decision Management Solutions

Next Best Offer

$6,000,000 in APE uplift

>98% Agent adoption

14-24%+ Acceptance

One smarter, automated decision can be worth millionsThe Dawn Of Digital Decisioning: New Software Automates Immediate Insight-To-Action Cycles Crucial For Digital Business John R. Rymer and Mike Gualtieri

• An automated decision picks the best offer to make to a customer based on

the insurance products they have already selected. Using information

about the customer, it personalizes the message and picks a suitable offer.

• The offer is always for a relevant product, never for something they have

already selected and always something easy to add to their purchase.

Think 2019 / 6945 / February 14, 2019

© 2019 Decision Management Solutions 11

A Smarter, Automated Decision:Next Best Offer

Multi-channel Agents mobile app (iPad) initially

Reused in customer portal

Under Marketing Control Analyze decision outcomes

Manage decision logic

Analytics Propensity

Product sequence

Segmentation

Business Rules Suitability

Affordability

Restrictions

Product rules

Next Up: Machine Learning

Think 2019 / 6945 / February 14, 2019

© 2018 Decision Management Solutions 12

External Data

Big Data

Decision Services Deliver Digital Decisions to Your Systems

Business

RulesAnalytics,

ML and AI

• Business Rules are quick

to change

• Good for regulations,

policies, flash updates

• Less insight-rich than

analytics

• Analytics are insight-rich

but often opaque,

especially ML and AI

• Good for patterns,

trends, categorization

• Must be fed new data

and continuously

improved

A decision service encapsulates business rules, analytics, ML and AI to deliver automated decisions to your application context.

Data about business outcomes and decisions made is integrated with external data to close the loop and improve both rules and analytics

Think 2019 / 6945 / February 14, 2019

© 2019 Decision Management Solutions 13

Three Steps to Delivering Digital Decisioning

DecisionsFirst Design Thinking

Mix and Match Technology

Continuous Improvement

• DecisionsFirst Thinking - Think about decision design first to build a decision model and drive practical innovation

• Mix and Match Technology – Business rules, analytics and AI under a decision umbrella, deployed as a decision service

• Continuous Improvement – Analyze decision-making to drive business learning and focus on gradual improvement

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© 2019 Decision Management Solutions 14

Decision Models Show What’s Involved In Digital Decisions

Knowledge

required

Structure of

decision-making

Data required

• Decision models are best

developed using the

Decision Model and

Notation (DMN) standard.

• This defines a notation

showing decisions, their

decomposition into

reusable sub-decisions,

the data each decision

needs and the knowledge

required to define

business rules or analytics

for each.

Decision to be

made

Think 2019 / 6945 / February 14, 2019

© 2019 Decision Management Solutions 15

Mix and Match Business Rules, Analytics and AI

Decision Modeling shows all the elements of a decision and enables you to mix and match the right technologies to automate enough of the decision to take advantage of your predictive analytic models and machine learning.

Automate Policies

Enforce Regulations

Encapsulate Expertise

Put Analytics, ML and AI to Work

Think 2019 / 6945 / February 14, 2019

© 2019 Decision Management Solutions 16

Continuously Improve by Capturing Decision Outcomes

Gather data What was decided

Why was that decided

How did that work out?

Change the way you decide

Good Machine Learning platforms keep models learning as new data is gathered. Add data about the decisions you made, and how they worked out in business terms, and you can understand your decision-making and turn your machine learning into business learning.

Think 2019 / 6945 / February 14, 2019

© 2019 Decision Management Solutions 17

Continuous ImprovementMedical Claims

Straight Through Processing Before: 0%

Day 1: 8%

Day 100: 28%

Business-led Continuous Improvement

The project developed a decision model, then implemented the business rules that

match that model in IBM ODM.

To manage risk the initial implementation was very cautious – just 8% auto

adjudication.

But because ODM Simulation and a decision outcomes dashboard had been developed

using the decision model, the claims team could see which claims were not being auto

paid that could have been.

Using Decision Center they were able to make the changes they wanted, simulate

them and deploy them.

This resulted in a steady increase to exceed their target and positioned them to

integrate analytic insight using ML in the coming months.

0%

10%

20%

30%

-30 -20 -10 0 10 20 30 40 50 60 70 80 90 100

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© 2019 Decision Management Solutions 18

What the Customer Says about Maximizing Impact

It’s a journey

It’s lots of small positive changes

It’s about transparency

It’s a lot about people

• It’s a journey• Start with rules/data insights, then build up

sophistication leveraging A.I./machine

learning as appropriate

• It’s lots of small positive changes• Leverage built-in experimental capabilities to

manage unexpected risk/outcome and

improve decisions overtime

• It’s about transparency• Break down logic of complex

decisions for easy understanding.

• It’s a lot about people • Provide traceability for each decision

made. Require new roles/skills to

manage decision performance. Focus

on training to build inhouse

competencies/capabilities.

Think 2019 / 6945 / February 14, 2019

© 2018 Decision Management Solutions 19

Challenges with ML Ops

Data Accessibility & performance learning

Age & quality

Prescriptive noise

Development & Deployment Very long development cycle

Reliant on technical deployment

Visibility Model performance

Result explainability

© 2018 Decision Management Solutions 20

Rule Maintenance

DecisionsFirst Modeler

Design Time - Real-Time Decision ArchitectureDecision models managed

by business SMEs

coordinate business rules

and analytic models

Watson Studio

Decision explanation and outcomes

integrated into decision dashboard

Decision Dashboard

Model

Configuration

Prescriptive

Decisions

Predictive

DecisionsSupporting

Variables /

Features

HTAP

© 2018 Decision Management Solutions 21

Rule Maintenance

DecisionsFirst Modeler

Deployment - Real-Time Decision ArchitectureDecision models managed

by business SMEs

coordinate business rules

and analytic models

Watson Studio

Model

Configuration

Prescriptive

Decisions

Predictive

Decisions

Decision Service

Explanation e.g.

LIME, AI Open

Scale

© 2018 Decision Management Solutions 22

Decision Service

Execution - Real-Time Decision Architecture

Decision-making logs

Business

outcome data

Explanation e.g.

LIME, AI Open

Scale

Dynamic query in-memory HTAP database

Decision explanation and outcomes

integrated into decision dashboard

Decision Dashboard

Transaction

Dynamic Model Exec

Execution Data

Events update

variables

© 2018 Decision Management Solutions 23

Business Results ViewExplainers Integrated in Claims Dashboard

Non-Disclosure

model Score

Which features contributed

what to the score?

Review data at a per

claim level or for a

group of claims

How was the score used

in the overall decision

Explanation e.g.

LIME, AI Open

Scale

Decision-making logs

Business

outcome

data

Enterprises waste time and money on unactionable analytics

Digital decisioning can stop this insanity

It is the highest-value next step for … a successful digital transformation

The Dawn Of Digital Decisioning: New Software Automates Immediate Insight-To-Action Cycles Crucial For Digital Business John R. Rymer and Mike Gualtieri

Think 2019 / 6945 / February 14, 2019 / © 2019 IBM Corporation

Thank you

25Think 2019 / 6945 / February 14, 2019 / © 2019 IBM Corporation

Stephane MeryIBM DE, Digital Business Automation, Decisions Chief Architect—stephane.mery@fr.ibm.comibm.com

James TaylorCEO, Decision Management Solutions—james@decisionmanagementsolutions.com+1 650 400 3029decisionmanagementsolutions.com

Ryan TrollipCTO, Decision Management Solutions—rtrollip@decisionmanagementsolutions.com+1 774 641 3666decisionmanagementsolutions.com

Notices and disclaimers

26Think 2019 / 6945 / February 14, 2019 / © 2019 IBM Corporation

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27Think 2019 / 6945 / February 14, 2019 / © 2019 IBM Corporation

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