Post on 11-Sep-2018
Finance Forum
Prangins, 26th September 2017
How digital shapes the future of finance
PwC’s Digital Services September 2017
Programme
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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
A Digital PerspectiveThe Future of Finance
Darioush ZirakzadehDirector, PwC Switzerland
Eric Abrial Senior Manager, PwC Switzerland
PwC’s Digital Services September 2017
Table of Contents
• Setting the scene - Future of Finance in a nutshell
• Process Intelligence
• Data analytics
• Artificial intelligence
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PwC’s Digital Services
Future of Finance in a nutshell
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PwC’s Digital Services September 2017
The role of Finance must change – again
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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
PwC’s Digital Services September 2017
Finance function – a snapshot in time
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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
PwC’s Digital Services September 2017
PwC | CFO Survey 2017
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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
PwC’s Digital Services
Process Intelligence
Achievingfact-based insights
CONCEPTS AND CASE STUDIES
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PwC’s Digital Services September 2017
What is process intelligence, and how is it different?
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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
600
400
200
1 0.00%
10.00%
20.00%
25.00%
5.00%
15.00%
1 2 4 8 16 32 64
Throughout Response Time
Op
erat
ion
s p
er s
econ
d
% o
per
atio
ns
>50
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cs
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PwC’s Digital Services September 2017
A new approach to process optimisation through generation of actionable, fact-based insights
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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
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Discovery of the actual P2P business process
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CLIENT EXAMPLE
Complete process:
PwC’s Digital Services September 2017
Compare the business processes on different complexity levels
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CLIENT EXAMPLE
Number of cases and the average duration of each activity
Exception
75% most frequent paths 85% most frequent paths13
PwC’s Digital Services September 2017
Benchmark the processes across territories
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SwedenSwitzerland Portugal UK
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CLIENT EXAMPLE
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?
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CLIENT EXAMPLE
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.
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Note: Dots stand for users. The speed they flow indicates the time it takes to complete the activity.
Backlog
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CLIENT EXAMPLE
PwC’s Digital Services September 2017
Analyse people involvement using social maps
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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
PwC’s Digital Services
Data Analytics
Finance as a Performance Driver
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CFO as a Performance Driver
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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
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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
PwC’s Digital Services
Artificial Intelligence
Machine learning
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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
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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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Teaching a machine to perform VAT coding
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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
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
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
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-
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
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: darioush.zirakzadeh@ch.pwc.com
Zack TianDirectorPricewaterhouseCoopers AG Phone: +41 58 792 9303Birchstrasse 160 Mobile: +41 79 742 8681 8050 Zurich Email: zack.tian@ch.pwc.com
Michal TargielSenior ManagerPwC Polska Sp. z o.o. Phone: +48 (0) 12 433 3500Lubicz 23a, 31-501 Kraków Mobile: +48 519 507 138Poland Email: michal.targiel@pl.pwc.com
Eric AbrialSenior Manager PricewaterhouseCoopers SA Phone: + 41 58 792 9183Avenue Giuseppe Motta, 50 Mobile: + 41 79 150 75601211 Geneva 2 Email: eric.abrial@ch.pwc.com
Contacts