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RPA FS Leadership Presentation - PwC · automation (RPA) play for us? CFO. PwC’s Digital Services...

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Finance Forum Prangins, 26 th September 2017 How digital shapes the future of finance
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Page 1: RPA FS Leadership Presentation - PwC · automation (RPA) play for us? CFO. PwC’s Digital Services September 2017 Finance function –a snapshot in time Confidential information

Finance Forum

Prangins, 26th September 2017

How digital shapes the future of finance

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PwC’s Digital Services September 2017

Programme

Confidential information for the sole benefit and use of PwC’s client. 2

16.45 Welcome

17.00 The Future of Finance

Darioush Zirakzadeh, Director, PwCEric Abrial, Senior Manager, PwC

17.30 Case studies in Robotics Process Automation (RPA)

Zack Tian, Director, Technologist, PwC Digital Services Michal Targiel, Senior Manager, RPA Lead, PwC Poland

18.15 Q&A

18.30 Apero

Page 3: RPA FS Leadership Presentation - PwC · automation (RPA) play for us? CFO. PwC’s Digital Services September 2017 Finance function –a snapshot in time Confidential information

A Digital PerspectiveThe Future of Finance

Darioush ZirakzadehDirector, PwC Switzerland

Eric Abrial Senior Manager, PwC Switzerland

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PwC’s Digital Services September 2017

Table of Contents

• Setting the scene - Future of Finance in a nutshell

• Process Intelligence

• Data analytics

• Artificial intelligence

Confidential information for the sole benefit and use of PwC’s client. 4

Page 5: RPA FS Leadership Presentation - PwC · automation (RPA) play for us? CFO. PwC’s Digital Services September 2017 Finance function –a snapshot in time Confidential information

PwC’s Digital Services

Future of Finance in a nutshell

Confidential information for the sole benefit and use of PwC’s client.

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PwC’s Digital Services September 2017

The role of Finance must change – again

Confidential information for the sole benefit and use of PwC’s client. 6

What will Corporate Finance function

ideally look like in 2025+?

How will digitalization help

to fulfil compliance requirements?

What people and skillsets do we need

for the transformation

required?

What’s the specific situation of our clients?

• Most of our clients currently rethink the role of Finance towards a performance driver for the business

• Finance is confronted with new requirements, e.g. providing deep insights and operationally supporting new business models

• Digitalization provides a broad range of new opportunities (e.g. predictive analytics, big data, real-time analyses)

• Uncertainty about these new requirements and opportunities results in the start of “Future of Finance” vision and roadmap projects

What are the crucial digital technologies ?

What role does robotics process

automation (RPA) play for us?

CFO

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PwC’s Digital Services September 2017

Finance function – a snapshot in time

Confidential information for the sole benefit and use of PwC’s client. 7

Time

Scorekeeper

• Post factum, silo based • Statistical reports generated based on

past data• Year end closing manual intense

activities taking weeks

Performance driver

• Business partnering• Real time analytics beyond static

reporting• Predictive analysis to drive cross

functional decision making• Next level process standardization• Transactional finance fully

automated including compliance and controls

• Continuous accounting

• Automation and integration between functions due to ERP systems

• Active decision-making support based on present data

• Challenger of the business due to integration transparency

• Shared services and outsourcing

Business Co Pilot

CFO

Data Provider

CFO

Efficiency Provider

CFO

Digital Business Partner

Client server

1990s

Mainframe computing

1980s

Manual ledgers

1970s

Integrated ERP systems

2000s 2010s

Cloud IT

2015+

Digital Sensors

2015+

Real Time Digital ERP

2015+

IOT

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PwC’s Digital Services September 2017

PwC | CFO Survey 2017

Confidential information for the sole benefit and use of PwC’s client. 8

Current state2016

Target Model2020

Transformation levers for the coming years*

+52pts

+31pts

+46pts

+13pts

+16pts #2

#3

#4

#6

Finance community mgmt.& collaboration

Robotics

Data science

Digital Dashboard

#5 E-process+26pts

#1 Cloud

400 + Finance professionals

* Ranking based on the gap between current and future state

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PwC’s Digital Services

Process Intelligence

Achievingfact-based insights

CONCEPTS AND CASE STUDIES

Confidential information for the sole benefit and use of PwC’s client.

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PwC’s Digital Services September 2017

What is process intelligence, and how is it different?

Confidential information for the sole benefit and use of PwC’s client.

Traditional Process Understanding• By interviews

Capture the assumed process flow by the interviewees.

• By process documentationCapture the expected process flow.

• By analyzing process KPIsHigh-level measurements of process performance.

16001400

1200

1000

800

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400

200

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1 2 4 8 16 32 64

Throughout Response Time

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10

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PwC’s Digital Services September 2017

A new approach to process optimisation through generation of actionable, fact-based insights

Confidential information for the sole benefit and use of PwC’s client.

Data driven approachNo more time-consuming employee interviews ingestion of all actual transaction data

Accurate visualisationNo more manual process documentation visualisation of all actual process variations

Actionable insights• Understand differences between actual and

expected process execution, quantify impact of deviations based on transaction data

• Visualise team structures based on user activity analytics

• Simulate the effects of improvement measures by combining process visualisation with known process parameters

11

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PwC’s Digital Services September 2017

Discovery of the actual P2P business process

Confidential information for the sole benefit and use of PwC’s client. 12

CLIENT EXAMPLE

Complete process:

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PwC’s Digital Services September 2017

Compare the business processes on different complexity levels

Confidential information for the sole benefit and use of PwC’s client.

CLIENT EXAMPLE

Number of cases and the average duration of each activity

Exception

75% most frequent paths 85% most frequent paths13

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PwC’s Digital Services September 2017

Benchmark the processes across territories

Confidential information for the sole benefit and use of PwC’s client.

SwedenSwitzerland Portugal UK

14

CLIENT EXAMPLE

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PwC’s Digital Services September 2017

Identify bottlenecks and process inefficiencies (e.g. Sweden)

• The procurement process follows a linear structure

• Intercompany transactions only• But “Invoice updated – Blocking: quantity”• Manual invoice release (more touchpoints)• Potential bottleneck Three-way match ill-configured?

Confidential information for the sole benefit and use of PwC’s client. 15

CLIENT EXAMPLE

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PwC’s Digital Services September 2017

Assess the process dynamics (e.g. Sweden)

• Good separation of duties: people placing orders are not involved in payment, goods and invoice receipt.

• Document item updates performed by “Unknown” persons

• Backlog due to “Invoice updated – Blocking quantity” slows down the process.

Confidential information for the sole benefit and use of PwC’s client.

Note: Dots stand for users. The speed they flow indicates the time it takes to complete the activity.

Backlog

16

CLIENT EXAMPLE

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PwC’s Digital Services September 2017

Analyse people involvement using social maps

Confidential information for the sole benefit and use of PwC’s client. 17

CLIENT EXAMPLE

Staff Sweden (4)

Batch Users

75% levelStaff Portugal (23)

• Sweden: 123’387 purchases done with 4 people

• Portugal: 61’187 transactions done with 23 people

• Portugal: several people responsible for invoice receipt

• In both countries: final payment is done by one person only

• Instances of re-work in Portugal: subsequent updates on purchase orders

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PwC’s Digital Services

Data Analytics

Finance as a Performance Driver

Confidential information for the sole benefit and use of PwC’s client.

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PwC’s Digital Services September 2017

CFO as a Performance Driver

Confidential information for the sole benefit and use of PwC’s client.

Insight vs. InstinctDecision-making based on relevant

real-time information

Real-timeFrom period-oriented reportingto “breaking news”

Driver focus and integrationConsistent information in all views

(internal/ external, customer, market, product)

Predictive AnalyticsFuture-oriented analyses replacing forecasting

Granular Steering Focused steering on all levels

with flexible scenario analyses

Consistent Data“Single-Source-of-Truth” and data integrity for all views

Business Enabling / Modular Steering

19

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From Descriptive Analytics to Prescriptive Analytics

What happened?

Why did it happen?

Prescriptive Analytics

Establish and automate interlinkages between big data, statistical analysis, machine learning and company data

Derive measures to optimize business on the basis of forecasts

e.g.: Forecast models can be integrated to optimize ‘Poncho’ production and its logistics distribution

Predictive Analytics

Apply statistical methods and econometric models in order to gain insights from digital data sources and forecast future developments

Conversion of raw data into useful and usable information

Use of data to determine possible future scenarios through forecasts in confidence levels

e.g.: Based on a forecast of the sugar price (among others), the sales figures (‘Poncho’) for the coming period can be predicted

Diagnostic Analytics

Gaining a deeper insight, e.g. correlations in a complex system

e.g.: An analysis reveals dependencies between sugar price and ‘Poncho’ sales

What will happen?

What should be done?

Head of Controlling – German Blue Chip

Why do we employ hundreds of controllers who generate forecasts which are either just a gut feeling or out of date when consolidated or, at worst, both? For this reason, we now launched various D&A test pilots from predictive analyticsto autonomous planning.

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Sales Intelligence 2.0 – The power of Predictive Analytics

Pilot Scope with an bottom-up approach

Tractor

Selected Country

Retail customers

Driver model based on the entire Value Chain

Prediction Objective

Retail customers

Value Chain

CLIENT EXAMPLE

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Sales Intelligence 2.0 – The power of Predictive AnalyticsCLIENT EXAMPLE

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PwC’s Digital Services

Artificial Intelligence

Machine learning

Confidential information for the sole benefit and use of PwC’s client.

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PwC’s Digital Services September 2017

There are 3 levels of automation for a process depending on its complexity and the type of data

Confidential information for the sole benefit and use of PwC’s client. 24

Robotic DESKTOP Automation (RDA)

Robotic PROCESS Automation (RPA)

ArtificialIntelligence

• Require human supervision• Semi- structured data• Manually initiated• One robot assist one FTE

• No human supervision• Structured data• Low complexity• Automatically initiated• One robot counts as several FTEs

• Simulate and replace human workforce with automated robots monitored through consoles

• Require large learning data sets• Complex cognitive learning systems

(Machine learning)

Easy ComplexFast Time consuming

Nature of the task

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PwC’s Digital Services September 2017

Teaching a machine to perform VAT coding

Confidential information for the sole benefit and use of PwC’s client. 25

Minimum data set sizesThis will vary based on the type of ML, the number and complexity of the classifiers, and the signal to noise ratio, amongst other features. This can be offset by the use of SME feedback into the system to improve the classifier recognition. This could be as few as a few hundred example transactions, but we prefer to use between 5k & 10k for highest target accuracy.

Case Study: ML for ITX Determination

Achieving 99.95% reproducibility

Using 2 data sets (1 EU, 1 non EU), we applied a full ML approach to learn the Tax Coding (determination) for datasets of 4m & 300k transactions respectively.The data covers complex transactions, cross-border selling, jurisdictional specific rules, chains & various different incoterms.Human SME feedback on exceptions, combined with refined ML algorithms, allows us to increase accuracy from 99.7% to 99.95% target accuracy.We can therefore support both diagnostic and predictive outcomes, allowing us to increase confidence and lower costs simultaneously.

Determination of business rulesBased on ML techniques, the key drivers for VAT coding were identified in the ITX transactions. Through advanced techniques, considering the individual significance of the key drivers, applied tax business rules were determined. This allowed an independent assessment of the tax rules against corporate policies.

CLIENT EXAMPLE

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Indirect Tax Analytics

ContractItem

Country / Sales

Country ofDestination

Material Price / unit Division Tax Code

1004 CH UK ST005 450 CHEK WE30

1007 DE NZL ST003 310 DEEK IN07

1009 DE SE PE440 25 DEEK BD79

1011 CH PT ST005 790 CHTRE WE30

1013 DE JP PE440 25 DEEK IN70

11015 DE SE PE440 107 FREK IN07

11017 DE PT PE017 320 DETRE BD79

11023 FR JP TT055 107 FREK IN07

approx. 10’000 transactions

CLIENT EXAMPLE

Questions from the client- Is code correct?- Is coverage full?

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Indirect Tax Analytics

Machine Learning

«features» «label»

The machine learns by itself the relationbetween the «label» (Tax Code) and the«features» (all other columns).

Learning example:

Country / Sales = CH

and

Material = ST005

then

Tax Code = WE30

The result of this learning is an algorithmthat is called the «classifier».

CLIENT EXAMPLE

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Case studies in Robotics Process Automation (RPA)

Zack TianDirector, Technologist, PwC Digital Services

Michal TargielSenior Manager, RPA Lead PwC Poland

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Non Intrusive Technology• RPA connects to business software applications through existing user interfaces, hence it is not technically integrated• Without complex integration, RPA programs can be launched in a matter of days or weeks, resulting in lower cost of implementation

and faster pay back

Scalability and Audit• After some initial training staff can easily learn to program and deploy their own robots• Robots can easily be scaled to match demand and can be fully audited, recording all access and changes

performed.

Repeatability & Reproducibility• RPA systems use rules and perform tasks exactly as programmed improving accuracy levels in error prone repetitive tasks• Productivity improvements free up employees for value added cognitive processes

Technology Agnostic• RPA can work across legacy ERPs, mainframes, custom applications and any other type of IT platform• It can be deployed in a mixed setting, where some of the steps are performed by humans while others are completely

automated.

Key features:

Robotic process automation (RPA) is an integration software that can integrate with existing systems via user interfaces to perform tasks normally performed by a human

What is RPA and what does it offer?

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Potential RPA Usage

Original report Set of

rulesFinal report

Data manipulation / calculation / formatting• Perform data clean up based on

pre-defined rules which include getting input from various systems

• Calculate and format final financial report

Databases APIs

Report

Access databases via APIs• At pre-set time, the program is

kicked off to generate reports based on pre-defined rules by accessing APIs

Data entry with input from multiple systems• Access multiple programs (e.g.

Excel and Oracle)• Find record information from Excel

based on a set of criteria• Copy and paste information into

Oracle

Excel

Oracle

Data entry within same system• Complete data entry with navigation

through a series of screens

Oracle

Email notification• Send email notification with the

proper attachment after an activity is completed based on pre-defined rules and time

Data validation between multiple systems / OCR• Identify fields in multiple systems

and conduct data validation• Leverage Optical Character

Recognition (OCR) technology to extract fields from PDF

SFDC

XSM

Email notification

PDF

There are many areas where RPA can be applied to quickly realize value

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The main business case behind using RPA

… the immense reduction in operating costs

Benefits of RPA

Improved quality with 100%

accuracy on automated

cases

The cost reduction due to process automation outperforms the cost reduction from off-shoring.

This is not only because of more efficient processes, but also because of lower operating costs.

Onsite costs(Switzerland)

Nearshore costs

RPA costs

Overall cost reduction due to RPA

implementationCHF 69k – CHF 92k

CHF 24k – CHF 33k

CHF 6k – CHF 9k

-90%

Below is the actual operating costcomparison of an organization we supportedwith RPA feasibility study:

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Human vs. Bot – business lifecycle comparison

Test & audit logs, Configuration files

BOT Identity & Access Management

BOT Infrastructure, Environment setup

Work Queues, Scheduling

Maintenance, DR / BC planning

BOT Operational & Security compliance

Change Request, Bug Fixing

BOT Upgrades, Scalability

Benefit Realisation

BOT Decommissioning

Process Identification and Book of Work Creation

Business Case, Cost Benefit Analysis

BOT Pipeline Management

BOT Development

BOT Testing, UAT

BOT Deployment

BOT Utilisation

Lessons Learnt

RPA Competence

Centre

Business Unit

Risk & Compliance

BPR

HR

IT

Position Request

Talent Acquisition

On Boarding

Business As Usual

Exit

Float Requirement

Shortlist Profiles

Interview and Selection

Make Offer

Background Checks

Employee Enrolment

Laptop, Desk Allocation

Budget Approval

Attendance

Planned and Ad Hoc Leaves

Compliance Adherence

Performance Appraisal & Feedback

Training and Development

Increments and Promotions

Salary

Relieving & Settlement

Exit Interview

Employee Lifecycle Governance

Business Team

IT and Support Functions

Risk & Compliance Team

Training & Development Team

HR Team

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Retail Company GBSCase Study

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Client Situation OverviewApplying robots to HR Processes

The client

The SSC client is a global company with multiple offices across Europe in retail. Within its GBS it provides HR, FA and IT services for majority of EMEA locations.

• 4 basic HR Processes

• Painstaking & time consuming

• Manual work – 4 FTE workload

Volume: approx. 2,400 cases per month (registration of new employees, initial contract creation, personal and contract data changes, certificates of employment, etc.)

Accuracy: 96% (4% human error)

Backlog:200 cases

The challenge

4 Scenario Types: • New Employee• Personal Data Change• Contract Change• Employment Certificate Preparation

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RPA Pilot Overview

Stage 1Process Assessment

and Project preparation

Stage 2RPA Implementation & CoE Creation

(3 weeks) (12 weeks)

Process Assessment & business case creation

IT infrastructure preparation

RPA platform selection

Staff training preparation

4 processes selected for Pilot Phase

IT Intrastrcuture prepared

RPA Tool purchased, installed and configured

Client’s Staff selected for training

Robot 1 „KAZIK” (Design, Development, Testing and Implementation) – PwC Developer

Robot 3 (Design, Development, Testing and Implementation) – Client’s Developer supported by PwC Developer

Robot 1 & 2 GO LIVE

RPA Training

Robot 1 & 2 Optimisation & Stabilisation

Robot 3 & 4 Optimisation &

StabilisationRobot 3 & 4 GO LIVE

09.01 30.01 31.03

Robot 2 (Design, Development, Testing and Implementation) – PwC Developer

Robot 4 (Design, Development, Testing and Implementation) – Client’s Developer supported by PwC Developer

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HR automated process – Kazik

Robot (Kazik) automates HR related requests like registration of new employees, personal and contract data changes, certificates ofemployment, etc. have been sent to the special e-mail alias.

Depends on the e-mail subject and attached documents, robot decides what is the nature of the requests and acts accordingly (creates required documentsor/and fill the data in the HR database. At the end sends e-mail back to the requestor with the information that has completed the work/or that moreinformation is required.

Robot actions visible on screen:

Background robot actions:

Request is being sent to the special e-mail address

Analysis of the information in the e-

mail

Scenario #1: Changing terms of employment condition in the HR system, drafting Contract Addendum

Scenario#2: Creating new employee in the HR database and drafting Contract of the Employment

Scenario #3: Changing Personal data in the HR system

Scenario #4: Drafting Certification of Employment after collecting required data from the HR system

Send e-mail back to

requestor

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What has Kazik delivered?

Staff comments“Robots make our lives easier”

The project of HR process reengineering and automation resulted in

Saving of approx. 120 k EUR per annum1

Re-allocation of resources to higher value activities2

Management comments“This automation is a true success and we’ll continue to look for further RPA opportunities”

90%Processing time reduction

3.5Saving (h/m)

571Project time

8 weeks1 Robot Capacity (FTE)

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RPA lessons learnt

Stakeholder buy-inTo minimise roadblocks, key automation stakeholders should be brought into alignment to agree on roles, responsibilities,benefit prioritisation and impact on existing delivery policies, procedures and processes.

Process selection drives everything

Process identification is the key to the success of the automation. Ensuring that processes are fully understood meet bothautomation and commercial criteria to deliver solid Return on Investment.

Delivery capabilityWe have proven methodology and experience that ensures successful and smooth RPA implementation. Deliver fast andmaximise value through a combination of an agile approach, process optimisation, customer centered design and qualityautomation development.

IT supportTechnology is a key enabler for automation. By including technology in every aspect of the automation process unexpectedtechnical surprises can be avoided.

Business supportOngoing support from operational staff needs to be provided throughout every stage of automation, e.g., detailed scopedefinition, testing data and scenario preparation, UAT session participation.

Change managementWhereas RPA can bring substantial benefits enhancing operational performance, it requires effective management of processand required organisational change. Proper planning and proactive communication ensure smooth integration and optimalbenefits achievements.

Operational capabilityThe monitoring, management, reporting, support and maintenance of the virtual workers needs to be considered. Stability ofsystems and application used in automated process has to be ensured

Early consideration of governance, risk and assurance

Compliance and regulatory topics are one of the most complex and therefore also one on the most important areas to getright, especially robots will be processing across different regions

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Key elements of successful RPA journey

Process assessment methodology

Best practice on process selection

Right selection of RPA platform

Framework for single process

automation

Support and Maintenance model

Building internal RPA capabilities

Proven suite of tools RPA lessons learnt

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Large Bank Case study

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Robotics Process AutomationOn-going project at a large Swiss Bank

• Thousands of systems / applications are used by the bank for everyday tasks

• Some of the “mission-critical” systems were developed a few decades ago lacking of integration options

• Manual processes have been created to act almost like manual interfaces between the systems

• Due to the speed of change in regulatory and competitive landscapes, the organization needs to have more flexible and high- or full-volume processing power, which cannot be satisfied by manual processes

• Robotics Process Automation (RPA) was used as the solution to rapidly automate and digitize tasks that were manual, time-consuming and rule-based. Common tasks including: data collection, reconciliation, compare records, and regular reporting

• Talents are realigned to focus-on more value-adding tasks

• The results of RPA in those low-value tasks:

• 50% to 80% operating cost reduction

• 100% standardization

• 30% improvement in cycle time

Encouraged by these results, the bank is expanding the use of RPA technologies from 5 to 20 RPA BOTs in the current department, and hundreds of BOTs bank-wide by end of 2017.

The automation technologies are also expanding to ORC, Machine Learning and Cognitive AI.

RPA software is a powerful tool to rapidly automate manual, time-consuming, and rule-based tasks.

It is also a foundation for machine learning, artificial intelligence and a more autonomic enterprise.

Client situation How did we help

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The actual design of a Machine Learning and RPA solution in the project

Navigate to the View

Check results after download

Download results

Intraday dynamic & immediate alert

Access the Systems

EoD Report generation

Process AS-IS

AutomationRPA robot:

automatically log into the intraday systems using a read-only user type.

RPA robot: navigates to the right view of the systems and preparing to download the risk file containing e.g. sensitivity on intraday price shocks or volatility changes

RPA robot: download and save the risk files for archiving and further processing

RPA robot: execute a list of pre-defined data cleansing and normalisation steps

Machine learning: 1. identify most probable type of errors, provide possible solutions to the user (correct data, ignore data, etc.)

2. tracking user behaviour during pre-processing steps, e.g. error handling

3. prompt user with the most frequent actions based on learning, and suggesting user to add the most frequent action into RPA robot logic for automated error handling

Machine learning:1. identify existence of

unusual intraday risk patterns

2. Suggest potential reasons for such deviation form the expected risk development

RPA robot: An aggregated report will be compiled for summary reporting

Comments will be integrated into the report and sent for verification and finally to selected receivers

RPA robot to automate any manual repetitive and rule-based tasks, alert & support user.

Machine learning to translate patterns in user behavior and historical data into rules for RPA robot to execute the process more effectively.

RPA robot: Arrange the data in order, and map with the GBM data to identify business grouping

Check risk / P&L numbers against predefined limits and classify severity of limit breach

Machine Learning Machine learning:

1. Significant changes in (risk) exposure will be identified and marked to be commented

2. Suggestion of comments based on among others time series analysis of exposure and/or market prices

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Lessons and experience to share from current RPA project

All our RPA BOTs come with a configuration file. Each user can configure the RPA BOT using the configuration file before executing the robot.

As a result, each time the RPA BOT can run with different settings, e.g.: date range changes, amount thresholds, different download folders, etc.

When designing a RPA BOT, sometimes a few additional ‘smart’ logic can make a huge difference in the user experience.

For example, using a RPA BOT to adjust data extraction thresholds can save user time to go through excessive number of transactions produced by RPA.

RPA software stores various information during the process. It is important to ensure sensitive information such as user passwords, customer information are secured during the BOT design.

The logging functionality of a RPA software can also be used for audit evidence and user accountability.

The stability of RPA BOTs equal trusts from the process users. It is important to implement and enforce stability and exception handling KPIs during the development and testing phase

Stability is the key to success

Configurable RPA BOTs allow flexibility without re-development

Use RPA BOT also to do something ‘smart’

Environmental and utility BOTs

To facilitate the main process BOTs, and make the automated solutions robust and smart, some utility BOTs are necessary to do simple things such as write a summary email of the actives, or create a IT service tickets for the user

Security needs to be considered as part of the design

Complete and comprehensive Solution Design Documents signed-off by process SMEs/owners are crucial:- to understand the

impact on each RPA BOT during process changes- requirements have

been gathered and agreed by process SMEs before each RPA BOT development

High quality BOT documentation is key for future maintenance

Process owner and SME support is the most important aspect of the project:- Process owners and SMEs should be involved in the solution design- Process owners and SMEs should be involved in the acceptance testing

Above all

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Questions?

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Darioush ZirakzadehDirector PricewaterhouseCoopers SA Phone: + 41 58 792 8322Avenue Giuseppe Motta, 50 Mobile: + 41 79 671 73 701211 Geneva 2 Email: [email protected]

Zack TianDirectorPricewaterhouseCoopers AG Phone: +41 58 792 9303Birchstrasse 160 Mobile: +41 79 742 8681 8050 Zurich Email: [email protected]

Michal TargielSenior ManagerPwC Polska Sp. z o.o. Phone: +48 (0) 12 433 3500Lubicz 23a, 31-501 Kraków Mobile: +48 519 507 138Poland Email: [email protected]

Eric AbrialSenior Manager PricewaterhouseCoopers SA Phone: + 41 58 792 9183Avenue Giuseppe Motta, 50 Mobile: + 41 79 150 75601211 Geneva 2 Email: [email protected]

Contacts


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