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The creative CIO’s agenda: Getting started with digital laborAshraf W. Shehata, Principal, KPMG

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DISCUSSION TOPICS

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The Macro Trends in the Digital Labor Marketplace

Digital Labor Use Cases in IT and Healthcare

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14.9billion

$

1.7billion

$

DIGITAL LABOR, ENABLED BY COGNITIVE AUTOMATION, WILL DRAMATICALLY IMPACT THE WAY WORK IS DONE TODAY

The global market for robots and artificial intelligence is expected to reach $152.7 billion by 2020. The adoption of these technologies could improve productivity by 30 percent. Bank of America Merrill Lynch

A recent study by HfS Research and KPMG LLP reports that 55 percent of North American enterprises are looking at new opportunities available with RPA systems.

MarketsandMarkets estimates that the AI, or cognitive computing marketplace, will generate revenue of

According to Quid, from 2010 to 2014, private investment in AI has grown from $1.7 billion to $14.9 billion, and was on track to grow nearly 50 percent year-on-year in 2015 alone.

Gartner predicts that by 2020, smart machines will be a top five investment priority for more than 30% of CIOs.

McKinsey research suggests that smart robots will replace more than 120

million knowledge workers by 2025.

billion152.7

Recent research fromLondon School of Economics suggests a return on investment in robotic technologies of between 600% and 800% for specific tasks.

ROI800and

600%%

billion by 201912.5$

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55%

Top

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DIGITAL LABOR: COGNITIVE TECHNOLOGIES CONVERGING WITH ROBOTIC TECHNOLOGIES

— Sometimes referred to as “smart bots”

— Algorithms powering applications which execute judgement oriented tasks that require evidence and reasoning

— Interaction with humans is more natural

Adva

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Advancements in machine intelligence

Digitallabor

Robotic processautomation

Human labor

CognitiveTechnologies

— Machine learning and adaptive technologies, which typically evaluate unstructured data, text, video and images

— Designed to perceive context and infer probabilistic answers

— Uses data and analytics technologies

— Automation of transactional, rule-based, and repeatable processes

— Technologies include OCR, rules engine, macros

— Benefits include FTE reduction, cycle time

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COGNITIVE TECHNOLOGIES HAVE EVOLVED TO ADDRESS THE CHALLENGES OF TODAY’S DIGITAL WORLD

Cognitive systems mimic human brain functions

Intelligent augmentation

A new partnership between humans and machines

Physician Nurse WealthAdvisor

AcademicAdvisor

SalesAdvisor

LegalAdvisor

TaxAdvisor

Auditor ServiceAdvisor

InsuranceAdvisor

Machine learning

Deep learning

Artificialintelligence

Natural languageprocessing

Predictive analytics

Text analytics

Imagerecognition

Voicerecognition

Have the potential to reshape the workforce of the future

Perceive(interpret sensory input beyond traditional data)

1 Reason(hypothesize, weigh supporting evidence)

2 Learn(improve confidence levels with experience)

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Cognitive automation

— Artificial intelligence— Natural language recognition and

processing— Self-learning (sometimes self optimizing)— Processing of super data sets— Predictive analytics/hypothesis

generation— Evidence-based learning

— Built-in knowledge repository— Learning capabilities— Ability to work with

unstructured data— Pattern recognition— Reading source data manuals— Natural language processing

— Macro-based applets— Screen level and OCR

data collection— Workflow automation— Process mapping— Self executing

Basic process automation

Enhanced process automation

MachineLearning

Large-scaleprocessing

Adaptivealteration

Artificialintelligence

Big dataanalytics

Naturallanguageprocessing

Processing of unstructured data

and base knowledgeRules engine

Visual Data Collection

Work flow

THREE TYPES OF DIGITAL LABOR

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DIGITAL LABOR: WHERE ARE WE NOW?

Adoption is rapidly occurring across the enterprise

Virtual chat / chat bots are the most hyped technology of 2017, but they still work best for discrete interactions

Large BPOs are building proprietary platforms, but they are not aggressively disrupting their existing client base

Organizations see the promise of cognitive automation & AI, but they are struggling with where to start

Very few companies are looking at both the operating and business model disruption that the next wave of automation will drive

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IT BACK OFFICE FRONT OFFICE

Application Support “Production Control”

Activities

IT Health Check Activities (App/Infra.)

Provisioning & Capacity Management

Virtual Service Desk Agent

Record to Report (e.g. GL close, month end reporting)

Source to Pay (e.g. Supplier Mgmt, Requisition)

Acquire to Retire(e.g. Asset Depreciation)

HR Transaction Processing & Forms

Provider Enrollment

Contact Center & Customer Services

Small Claims Balance Processing

Patient Scheduling

Digital Labor Opportunities in Healthcare

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HIGH IMPACT DIGITAL LABOR APPLICATIONS TO HEALTHCARE PROVIDERS

Cross Industry (Horizontal) RPA solutions utilizing enhanced capabilities will have the most impact in the short term in the following:

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Degr

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Enhanced

Cognitive

Digital Labor Maturity

Degr

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Moderate

High

Scalability

AR P

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Industry specific (Provider) RPA solutions utilizing enhanced capabilities will have the most impact in the short term in the following:

Many organizations are taking a risk based approach to digital labor and starting with opportunities in the back and middle office.

As organizations evaluate process opportunities and technologies to deploy; its important not to forget the governance, security and operating model shifts necessary to make digital labor initiatives successful

Quick wins in the back office utilizing basic or enhanced process automation can generate early ROI to enable more advanced digital labor programs or expansion into the front office

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INSURANCE/HEALTH PLAN USE CASES ARE RAPIDLY MATURING

Business Challenge

■ Medical Bill Review (MBR) is necessary component of the workers’ compensation, auto PIP and other medical claims processes. With medical expenses accounting for up to 70% of claims costs1, carriers rely on third party providers (in addition to outsourced claims staff) to review and reduce bill costs as a service.

■ For workers’ compensation alone, MBR costs may represent up to 50%2 of per claim loss adjustment expenses. While service providers tout savings to offset, fees represent a significant cost to claims operations.

Solution

■ The MBR process is a defined workflow process built on well established rules for reasonableness and databases of fees schedules (state and PPO networks).

■ Taking ownership of the bill review process can reduce cycle times, costs and reliance on third party providers.

■ When combined with other automated capabilities, including case management and fraud, insurers have the opportunity to build robust claims management infrastructures to better enable adjuster handling and improving customer service.

The Case

■ MBR is rules-based and data-driven, which are the core components of Class 1 automation

■ Rather than paying per claim or as a percentage of savings, carriers now have a fixed cost solution to manage claims expenses

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SECURITY & RISK – TRENDS• Avoiding the common risk pitfalls – a few illustrative examples to consider for your RPA program:

Why Risk Matters: Inconsistency in policies allows for poor

design and execution of the program Lack of proper authentication controls for

Bots creates accountability and compliance issues

Inconsistencies in Bot development can result in incomplete and inaccurate processing of data

Weak change management practices can result in unauthorized programs implemented to production

Lack of policies procedures over ownership between the business and Center of Excellence.

Policies in place do not always reflect the current processes in practice.

General lack of oversight of risk acceptance process by the business and IT.

Programs lack controls for proper ownership of Bot ID and effective integration of the Bot IDs with applications.

Programs often lack design and enforcement of Bot ID accountability relating to data elements the bot should have access to in light of security, privacy, and compliance requirements.

Proper bot access provisioning and password management.

Varying skill levels and inconsistent developer training drives ineffective logging, monitoring, and analytics capabilities.

Programs often lack automated alerting tools for error handling and resolution and lack trend analysis capabilities.

General lack of controls around “is the bot doing what it is supposed to be doing” (completeness/accuracy/integrity of data).

There is often a lack of formal process for assessing how source application changes affect Bots that access them.

Some RPA programs lack formal and consistent process for requesting and implementing changes to Bots.

Segregation of RPA development and production environments is not consistently enforced.

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KEY LESSONS LEARNED BASED ON EXPERIENCE# Event Resulting Experience Key Takeaways

1 Setting the Business Expectations

High expectations from the business on what could be achieved through DL needed to be managed - They did not realize that their respective organization would likely need to be reorganized.

COE must educate the client business it will take time to reap the full benefits of automation. Basic RPA does not lead to full transformational benefits.

2 Business Engagement Process owners must be committed and engaged for the project to be successful.

Implement communication and change management plan at the beginning of the program.

3Ineffective and Inconsistent Business Processes

Lack of a coordinated approach for BOT development led to inconsistent implementation and increases support complexity.

Define a single strategy for an RPA program in order to minimize duplicative work and inconsistencies.

4Transformation of the Customer Experience Beyond Basic Automation

Identify opportunities to utilize more advanced RPA solutions that expand beyond Class 1 Rules Based Automation.

Establish an RPA Center of Excellence to facilitate the implementation of more advanced RPA solutions.

5 Establishment of Script Standards and Guidelines

Working with the client to set up standards and guidelines up front was effective to get the developers from the Center of Excellence and the client on the same page.

Maintain a consistent, elastic infrastructure throughout the process that aligns with the long term strategy of the IT division.

6 Initial Lack of a Detailed Repository

Streamlined implementation process eliminating detailed process documentation simplified initial implementation however, it became problematic when issues arose / adjustments required.

Detailed repository should be retained at all times and presented to the maintenance team. This documentation conveys what has been produced and also facilitates change should the team need to go back and make adjustments.

7 Involvement of Necessary Systems

Team did not know what systems would be involved and the necessary processes to gain access, it became difficult to work through various roadblocks.

Establish what systems are involved, the current testing environments and engage with App teams to understand impact of systems changes on BOTS.

8 Core Teams’ Inability to Match Demand

As demand grew core RPA unable to keep up with demand for automation.

Enablement of business units to build and implement RPA solutions to address and meet their respective goals within CoE framework.

KPMG Digital Labor Case Studies

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DIGITAL LABOR - KPMG CLIENT CASE STUDIESCognitive Automation strategy and roadmapLarge healthcare provider

Context/client challenge

— Client was interested in applying advanced cognitive technologies in a shared services environment; needed help in identifying opportunities and conducting a market study of available solutions.

— Client was piloting basic automation for a limited number of P2P, H2R, and R2R processes.

Approach

KPMG assisted the client to:

— Conduct a detailed assessment to identify candidates for cognitive automation; prioritized the opportunities based on value proposition, complexity of solution, and organization readiness.

— Conduct a market scan to develop a vendor/capability matrix for 100+ vendors and service providers and identify high potential cognitive automation solutions.

— Create an implementation roadmap to develop and sustain cognitive automation capabilities, including the supporting CoEorg structure.

Benefits

Client evaluating KPMG’s recommendations, which are expected to deliver:

— Significant reduction in existing and future human labor needs.

— Improved service levels and user experience with evidenced-based outcomes.

— Integration with existing enterprise systems and basic automation technologies.

— Ability to expand into new markets and rapidly add new customers.

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CLIENT CASE HEALTHCARE PROVIDER...

# Resolutions types

1 Missing authorization

2 Lack of medical necessity

3 Cannot identify subscriber

4 CPT code missing OR charges

5 Primary denied due to other insurance identified

6 Coordination of Benefit (COB) information needed from patient

7 Present on Admission information needed

8 Missing NPI for ER/Admit Physician

9 Duplicate claim on file

10 Charges exceed allowable amount

For example, resolution steps can be broadly classified into the following 10 types. Further analysis can identify key words within the resolutions performed by follow up staff…

Word count analysis indicate Missing and Claim Not on File are the most common issues across all Small Dollar Claims which are mostly resolved via Rebills

Top 6 words

Missing 7,412

Claim 5,701

COB 2,329

CPT 2,316

Primary 1,987

Duplicate 1,289

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DIGITAL LABOR - KPMG CLIENT CASE STUDIESRPA proof of conceptMajor financial services provider

Context/client challenge

— Client was interested in implementing RPA solutions across its organization as a means to reduce operational costs, and re-invest the savings into broader platform re-engineering and cognitive automation.

— Manual execution of processes results in inefficient use of time and labor, and a high probability of errors and rework, and reduced employee moral.

— The client wanted to conduct proof of concepts with multiple vendors to understand and validate the delivered capabilities.

— The client needed assistance with vendor selection and conceptualizing the foundation steps required within the organization.

Approach

KPMG assisted the client to:

— Conduct education sessions and workshops and to share KPMG RPA thought leadership, benefits and impacts of RPA.

— Identify candidate processes for Proof of Concepts (POC).

— Configure the POC process on multiple RPA tools to understand respective software features, functions, and general ease to operations.

— Demonstrate the value and benefits of RPA to key business and IT stakeholders.

Benefits

— Conducted executive leadership workshops increased internal awareness of the benefits and impacts of RPA across the organization.

— Delivered Proof of Concepts to demonstrate the ease of configuration, potential reduction in errors, reworks and augmenting existing associates with automation.

— Finalized vendor for the program and presented to business stakeholders and broader leadership

— Set the path to implement RPA across the organization with the following immediate activities:- CoE Set-up and operationalization

- Pilot implementations

- Solution roll-out and management.

Thank you!Ashraf W. Shehata, Principal, KPMG

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