SlideShare a Scribd company logo
1 of 1
Download to read offline
| |December 2014
1CIOReview
CIOREVIEW.COMDECEMBER - 30 2014
T h e N a v i g a t o r f o r E n t e r p r i s e S o l u t i o n s
ENERGY TECHNOLOGY SPECIAL
Nag Ramachandran,
CEO
iSIGMA, Inc: Trusted
Customer Care & Billing
Solutions for Energy
Providers
iSIGMA, Inc: Trusted
Customer Care & Billing
Solutions for Energy
Providers
Company of the Month
FrankLi,
President&CEO,PantonIncorporated
| |December 2014
60CIOReview
T
o address energy conservation, generation
companies and commercial organizations
which deal in services like rating, programs
and measure implementations take a multi-
prong approach. The nation is cautious about its carbon
emissions and aims to earn carbon credits by curbing
on these carbon emissions. Thermal energy generation
companies, seen today, play important role in this process.
The challenge for the company is to meet the energy
demand of consumers and controlling emission. One
of the approaches taken by the company is to perform
an energy audit of the dwelling before and/ or after
construction. The audit captures data related to heating,
cooling, hot water, lighting, and appliance energy loads
and consumptions. The article scope is limited to single
and multi-family homes.
The company implements an energy conservation
objective through the program. This program is mostly
implemented by a non-profit organization which focuses
on green earth/energy
conservation ob-
jective. The
c o m p a n y
allocates a
Fraud detection need in the
energy program implementation
for Utility companies
By Atul Gunjal, CEO and Ameet Shedge, CTO, iWorkTech
CXO INSIGHT
budget and sets a target for implementation. The budget covers oper-
ational expenses plus rebate or incentives offered to consumers. The
objective of the program gets implemented by the staff and corrective
measures are implemented by its auditor or rater.
The challenges faced by the CIO is to see that each dollar spend,
is well spend and to detect instances of fraud and incompetency. The
proactive steps taken towards these challenges help in ensuring that
the objective of the program is met.
Program Implementation Platform (PIP)
PIP consists of the following modules
It is not hard-and-fast rule that all modules need to
present. Some can be dropped or some other can be
added as per the program needs. These modules are
implemented as portals which can be accessed through a
browser or desktop solution. Some modules are needed to
be accessed by the field executives; hence it needs to be
available as a mobile solution.
Data Flow, approach and algorithm
The data flow can be depicted as follows:
First, the home data is gathered by the file executive. It is important
that this data complies with the energy model. This helps to get
This approach ensures that
continuous Quality Assurance
is performed to detect and
report any anomalous result
in the submitted data
| |December 2014
61CIOReview
consistency in the rating. To achieve this, the following is
necessary:
• Clear and consistent information for the rater
• Training needs to cover assessment methods, measures, and
data points.
• Independent third-party assessment needs to be done on the
field reviews and submitted data files. This is done to detect any
fraud or incompetency.
o Establish limit on input variables
o Determine bounds check
o Detect and warn users for input values beyond reasonable
limits
o Generate XML data which is stored in the repository for
pattern detection, future use, data exchange with other systems.
This approach ensures that continuous Quality Assurance
is performed to detect and report any anomalous result in the
submitted data. This continuous approach absolves staff from
routine manual checking. The automated nature of the job helps
to handle any increase in the volume of homes. This process can
also detect existing patterns or establish a new pattern. This
pattern is stored in the repository. This rule-based approach
helps to build a flexible system.
Some of this system’s silent features are:
• Backend system
• Sanitize data received
• Analyze data based on several data validations
• Possess multiple methods for
o data analysis
o pattern recognition
• Flag files with issues
• Deliver report on analysis
Risks
• Unknown nature of data needs to be flagged and further
analyzed. This analysis helps determine whether new data needs
to be consumed by the system for fraud detection or not.
• Efforts involved to sanitize and manipulate data
• Diverse data models
Constraints
• Project efforts
• Support needs
Dependency
• Close communication is needed between involved stake holders
Conclusion
PIP helps CIO to keep focus on the project objective. PIP’s
Fraud module helps to get consistent rating and helps detect
fraud and incompetency. The fraud detection helps to keep
tab on the unfair practices. By taking proactive measures, like
training, helps to address issues like incompetency.
Atul Gunjal

More Related Content

What's hot

GSMA Final Project by Denson Ngumo
GSMA Final Project by Denson NgumoGSMA Final Project by Denson Ngumo
GSMA Final Project by Denson NgumoDenson Ngumo
 
Adaptive Six Sigma Case Study - MACH Teledata - 01
Adaptive Six Sigma Case Study - MACH Teledata - 01Adaptive Six Sigma Case Study - MACH Teledata - 01
Adaptive Six Sigma Case Study - MACH Teledata - 01LN Mishra CBAP
 
ACEEE Data Access Presentation at MES 2017
ACEEE Data Access Presentation at MES 2017ACEEE Data Access Presentation at MES 2017
ACEEE Data Access Presentation at MES 2017Annie Gilleo
 
RISK: When What Can Never Happen — Does
RISK: When What Can Never Happen — DoesRISK: When What Can Never Happen — Does
RISK: When What Can Never Happen — DoesTechPoint
 
626 slidedeck
626 slidedeck626 slidedeck
626 slidedeckLantorel
 
Data remediation article2
Data remediation article2Data remediation article2
Data remediation article2David Pedreno
 
Equipment Finance Data Remediation
Equipment Finance Data RemediationEquipment Finance Data Remediation
Equipment Finance Data RemediationDavid Pedreno
 
Economic assessment for improving eaccessibility services and products
Economic assessment for improving eaccessibility services and productsEconomic assessment for improving eaccessibility services and products
Economic assessment for improving eaccessibility services and productsJose Angel Martinez Usero
 
knowledge Byte -IT change management
knowledge Byte -IT change managementknowledge Byte -IT change management
knowledge Byte -IT change managementmohitnkm
 
Accenture HeathTech Innovation Challenge: Ayasdi
Accenture HeathTech Innovation Challenge: AyasdiAccenture HeathTech Innovation Challenge: Ayasdi
Accenture HeathTech Innovation Challenge: AyasdiJonathan Symonds
 
Electronic Batch Records
Electronic Batch RecordsElectronic Batch Records
Electronic Batch RecordsJason Corder
 

What's hot (15)

It change management
It change managementIt change management
It change management
 
GSMA Final Project by Denson Ngumo
GSMA Final Project by Denson NgumoGSMA Final Project by Denson Ngumo
GSMA Final Project by Denson Ngumo
 
Adaptive Six Sigma Case Study - MACH Teledata - 01
Adaptive Six Sigma Case Study - MACH Teledata - 01Adaptive Six Sigma Case Study - MACH Teledata - 01
Adaptive Six Sigma Case Study - MACH Teledata - 01
 
ACEEE Data Access Presentation at MES 2017
ACEEE Data Access Presentation at MES 2017ACEEE Data Access Presentation at MES 2017
ACEEE Data Access Presentation at MES 2017
 
Linckers
LinckersLinckers
Linckers
 
Lessons Learned from AMI Deployments and Asset Management Readiness
Lessons Learned from AMI Deployments and Asset Management ReadinessLessons Learned from AMI Deployments and Asset Management Readiness
Lessons Learned from AMI Deployments and Asset Management Readiness
 
RISK: When What Can Never Happen — Does
RISK: When What Can Never Happen — DoesRISK: When What Can Never Happen — Does
RISK: When What Can Never Happen — Does
 
626 slidedeck
626 slidedeck626 slidedeck
626 slidedeck
 
Data remediation article2
Data remediation article2Data remediation article2
Data remediation article2
 
Equipment Finance Data Remediation
Equipment Finance Data RemediationEquipment Finance Data Remediation
Equipment Finance Data Remediation
 
AIA Industries Brochure
AIA Industries BrochureAIA Industries Brochure
AIA Industries Brochure
 
Economic assessment for improving eaccessibility services and products
Economic assessment for improving eaccessibility services and productsEconomic assessment for improving eaccessibility services and products
Economic assessment for improving eaccessibility services and products
 
knowledge Byte -IT change management
knowledge Byte -IT change managementknowledge Byte -IT change management
knowledge Byte -IT change management
 
Accenture HeathTech Innovation Challenge: Ayasdi
Accenture HeathTech Innovation Challenge: AyasdiAccenture HeathTech Innovation Challenge: Ayasdi
Accenture HeathTech Innovation Challenge: Ayasdi
 
Electronic Batch Records
Electronic Batch RecordsElectronic Batch Records
Electronic Batch Records
 

Viewers also liked

Miriam Mc Govern CV 2.6.16
Miriam Mc Govern CV 2.6.16Miriam Mc Govern CV 2.6.16
Miriam Mc Govern CV 2.6.16Miriam Govern
 
Istruzioni presentazione
Istruzioni presentazioneIstruzioni presentazione
Istruzioni presentazioneAnna Chiozzi
 
अल्लाह ने ज़मीन को नहीं बनाया विधैना और पहाड़ो को मिखे
अल्लाह ने ज़मीन को नहीं बनाया विधैना और पहाड़ो को मिखेअल्लाह ने ज़मीन को नहीं बनाया विधैना और पहाड़ो को मिखे
अल्लाह ने ज़मीन को नहीं बनाया विधैना और पहाड़ो को मिखेFAHIM AKTHAR ULLAL
 
Kyle Hicks Resume
Kyle Hicks ResumeKyle Hicks Resume
Kyle Hicks ResumeKyle Hicks
 
Electronics Technician A Strand School
Electronics Technician A Strand SchoolElectronics Technician A Strand School
Electronics Technician A Strand SchoolMichael Cook
 
Family Office Elite Magazine Special Promotional Edition
Family Office Elite Magazine Special Promotional EditionFamily Office Elite Magazine Special Promotional Edition
Family Office Elite Magazine Special Promotional EditionTy Murphy
 
Grow Greek Tourism Online Certification
Grow Greek Tourism Online CertificationGrow Greek Tourism Online Certification
Grow Greek Tourism Online CertificationShistohilis Dimitrios
 
Trastornos alimenticios (Anorexia, Obesidad, Bulimia)
Trastornos alimenticios (Anorexia, Obesidad, Bulimia)Trastornos alimenticios (Anorexia, Obesidad, Bulimia)
Trastornos alimenticios (Anorexia, Obesidad, Bulimia)Karen Carriel
 

Viewers also liked (13)

Miriam Mc Govern CV 2.6.16
Miriam Mc Govern CV 2.6.16Miriam Mc Govern CV 2.6.16
Miriam Mc Govern CV 2.6.16
 
Istruzioni presentazione
Istruzioni presentazioneIstruzioni presentazione
Istruzioni presentazione
 
अल्लाह ने ज़मीन को नहीं बनाया विधैना और पहाड़ो को मिखे
अल्लाह ने ज़मीन को नहीं बनाया विधैना और पहाड़ो को मिखेअल्लाह ने ज़मीन को नहीं बनाया विधैना और पहाड़ो को मिखे
अल्लाह ने ज़मीन को नहीं बनाया विधैना और पहाड़ो को मिखे
 
Kyle Hicks Resume
Kyle Hicks ResumeKyle Hicks Resume
Kyle Hicks Resume
 
Electronics Technician A Strand School
Electronics Technician A Strand SchoolElectronics Technician A Strand School
Electronics Technician A Strand School
 
Radar Tech Cert
Radar Tech CertRadar Tech Cert
Radar Tech Cert
 
Graduation2012
Graduation2012Graduation2012
Graduation2012
 
Family Office Elite Magazine Special Promotional Edition
Family Office Elite Magazine Special Promotional EditionFamily Office Elite Magazine Special Promotional Edition
Family Office Elite Magazine Special Promotional Edition
 
Grow Greek Tourism Online Certification
Grow Greek Tourism Online CertificationGrow Greek Tourism Online Certification
Grow Greek Tourism Online Certification
 
Trastornos alimenticios (Anorexia, Obesidad, Bulimia)
Trastornos alimenticios (Anorexia, Obesidad, Bulimia)Trastornos alimenticios (Anorexia, Obesidad, Bulimia)
Trastornos alimenticios (Anorexia, Obesidad, Bulimia)
 
Resume
ResumeResume
Resume
 
Parco San Giuliano - Il Percorso didattico degli alberi
Parco San Giuliano - Il Percorso didattico degli alberiParco San Giuliano - Il Percorso didattico degli alberi
Parco San Giuliano - Il Percorso didattico degli alberi
 
Dossiers 0008
Dossiers 0008Dossiers 0008
Dossiers 0008
 

Similar to iworktech - CIO Review

Requirements document for big data use cases
Requirements document for big data use casesRequirements document for big data use cases
Requirements document for big data use casesAllied Consultants
 
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...Continuous Transaction Monitoring Detect and analyze anomalous transactions t...
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...Genpact Ltd
 
Mastering Data Conversion A Comprehensive Guide to Avoid Common Pitfalls
Mastering Data Conversion A Comprehensive Guide to Avoid Common PitfallsMastering Data Conversion A Comprehensive Guide to Avoid Common Pitfalls
Mastering Data Conversion A Comprehensive Guide to Avoid Common PitfallsAndrew Leo
 
Data Integrity webinar - Essentials & Solutions
Data Integrity webinar - Essentials & SolutionsData Integrity webinar - Essentials & Solutions
Data Integrity webinar - Essentials & Solutionspi
 
Myrmex short version
Myrmex short versionMyrmex short version
Myrmex short versionFactor-X
 
FDA News Webinar - Inspection Intelligence
FDA News Webinar - Inspection IntelligenceFDA News Webinar - Inspection Intelligence
FDA News Webinar - Inspection IntelligenceArmin Torres
 
FDA News Webinar - Inspection Intelligence
FDA News Webinar - Inspection IntelligenceFDA News Webinar - Inspection Intelligence
FDA News Webinar - Inspection IntelligenceArmin Torres
 
20cs2024 Ethics in Information Technology
20cs2024 Ethics in Information Technology20cs2024 Ethics in Information Technology
20cs2024 Ethics in Information TechnologyKathirvel Ayyaswamy
 
Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...
Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...
Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...Cognizant
 
Qlik wp 2021_q3_data_governance_in_the_modern_data_analytics_pipeline
Qlik wp 2021_q3_data_governance_in_the_modern_data_analytics_pipelineQlik wp 2021_q3_data_governance_in_the_modern_data_analytics_pipeline
Qlik wp 2021_q3_data_governance_in_the_modern_data_analytics_pipelineSrikanth Sharma Boddupalli
 
Stop the fraudster! Pennsylvania Treasury, Industry Expert Chris Doxey and Fu...
Stop the fraudster! Pennsylvania Treasury, Industry Expert Chris Doxey and Fu...Stop the fraudster! Pennsylvania Treasury, Industry Expert Chris Doxey and Fu...
Stop the fraudster! Pennsylvania Treasury, Industry Expert Chris Doxey and Fu...Oracle
 
Capgemini ses - smart grid operational services - supply chain fact sheet (gr)
Capgemini   ses - smart grid operational services - supply chain fact sheet (gr)Capgemini   ses - smart grid operational services - supply chain fact sheet (gr)
Capgemini ses - smart grid operational services - supply chain fact sheet (gr)Gord Reynolds
 
Con8154 controlling for multiple erp systems with oracle advanced controls
Con8154 controlling for multiple erp systems with oracle advanced controlsCon8154 controlling for multiple erp systems with oracle advanced controls
Con8154 controlling for multiple erp systems with oracle advanced controlsOracle
 
Customers talk about controlling access for multiple erp systems with oracle ...
Customers talk about controlling access for multiple erp systems with oracle ...Customers talk about controlling access for multiple erp systems with oracle ...
Customers talk about controlling access for multiple erp systems with oracle ...Oracle
 
Data Analyst Interview Questions & Answers
Data Analyst Interview Questions & AnswersData Analyst Interview Questions & Answers
Data Analyst Interview Questions & AnswersSatyam Jaiswal
 
State of application performance management in the Indian BFSI sector
State of application performance management in the Indian BFSI sector State of application performance management in the Indian BFSI sector
State of application performance management in the Indian BFSI sector ValueNotes
 
Dw19 t1+ +dq+fundamentals-cvs+template
Dw19 t1+ +dq+fundamentals-cvs+templateDw19 t1+ +dq+fundamentals-cvs+template
Dw19 t1+ +dq+fundamentals-cvs+templateMILLER A. ZAMBRANO T.
 

Similar to iworktech - CIO Review (20)

Requirements document for big data use cases
Requirements document for big data use casesRequirements document for big data use cases
Requirements document for big data use cases
 
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...Continuous Transaction Monitoring Detect and analyze anomalous transactions t...
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...
 
Mastering Data Conversion A Comprehensive Guide to Avoid Common Pitfalls
Mastering Data Conversion A Comprehensive Guide to Avoid Common PitfallsMastering Data Conversion A Comprehensive Guide to Avoid Common Pitfalls
Mastering Data Conversion A Comprehensive Guide to Avoid Common Pitfalls
 
Data Integrity webinar - Essentials & Solutions
Data Integrity webinar - Essentials & SolutionsData Integrity webinar - Essentials & Solutions
Data Integrity webinar - Essentials & Solutions
 
Myrmex short version
Myrmex short versionMyrmex short version
Myrmex short version
 
FDA News Webinar - Inspection Intelligence
FDA News Webinar - Inspection IntelligenceFDA News Webinar - Inspection Intelligence
FDA News Webinar - Inspection Intelligence
 
FDA News Webinar - Inspection Intelligence
FDA News Webinar - Inspection IntelligenceFDA News Webinar - Inspection Intelligence
FDA News Webinar - Inspection Intelligence
 
20cs2024 Ethics in Information Technology
20cs2024 Ethics in Information Technology20cs2024 Ethics in Information Technology
20cs2024 Ethics in Information Technology
 
NZS-4555 - IT Analytics Keynote - IT Analytics for the Enterprise
NZS-4555 - IT Analytics Keynote - IT Analytics for the EnterpriseNZS-4555 - IT Analytics Keynote - IT Analytics for the Enterprise
NZS-4555 - IT Analytics Keynote - IT Analytics for the Enterprise
 
Data Governance
Data GovernanceData Governance
Data Governance
 
Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...
Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...
Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...
 
Qlik wp 2021_q3_data_governance_in_the_modern_data_analytics_pipeline
Qlik wp 2021_q3_data_governance_in_the_modern_data_analytics_pipelineQlik wp 2021_q3_data_governance_in_the_modern_data_analytics_pipeline
Qlik wp 2021_q3_data_governance_in_the_modern_data_analytics_pipeline
 
Stop the fraudster! Pennsylvania Treasury, Industry Expert Chris Doxey and Fu...
Stop the fraudster! Pennsylvania Treasury, Industry Expert Chris Doxey and Fu...Stop the fraudster! Pennsylvania Treasury, Industry Expert Chris Doxey and Fu...
Stop the fraudster! Pennsylvania Treasury, Industry Expert Chris Doxey and Fu...
 
Capgemini ses - smart grid operational services - supply chain fact sheet (gr)
Capgemini   ses - smart grid operational services - supply chain fact sheet (gr)Capgemini   ses - smart grid operational services - supply chain fact sheet (gr)
Capgemini ses - smart grid operational services - supply chain fact sheet (gr)
 
evaluation
evaluationevaluation
evaluation
 
Con8154 controlling for multiple erp systems with oracle advanced controls
Con8154 controlling for multiple erp systems with oracle advanced controlsCon8154 controlling for multiple erp systems with oracle advanced controls
Con8154 controlling for multiple erp systems with oracle advanced controls
 
Customers talk about controlling access for multiple erp systems with oracle ...
Customers talk about controlling access for multiple erp systems with oracle ...Customers talk about controlling access for multiple erp systems with oracle ...
Customers talk about controlling access for multiple erp systems with oracle ...
 
Data Analyst Interview Questions & Answers
Data Analyst Interview Questions & AnswersData Analyst Interview Questions & Answers
Data Analyst Interview Questions & Answers
 
State of application performance management in the Indian BFSI sector
State of application performance management in the Indian BFSI sector State of application performance management in the Indian BFSI sector
State of application performance management in the Indian BFSI sector
 
Dw19 t1+ +dq+fundamentals-cvs+template
Dw19 t1+ +dq+fundamentals-cvs+templateDw19 t1+ +dq+fundamentals-cvs+template
Dw19 t1+ +dq+fundamentals-cvs+template
 

iworktech - CIO Review

  • 1. | |December 2014 1CIOReview CIOREVIEW.COMDECEMBER - 30 2014 T h e N a v i g a t o r f o r E n t e r p r i s e S o l u t i o n s ENERGY TECHNOLOGY SPECIAL Nag Ramachandran, CEO iSIGMA, Inc: Trusted Customer Care & Billing Solutions for Energy Providers iSIGMA, Inc: Trusted Customer Care & Billing Solutions for Energy Providers Company of the Month FrankLi, President&CEO,PantonIncorporated | |December 2014 60CIOReview T o address energy conservation, generation companies and commercial organizations which deal in services like rating, programs and measure implementations take a multi- prong approach. The nation is cautious about its carbon emissions and aims to earn carbon credits by curbing on these carbon emissions. Thermal energy generation companies, seen today, play important role in this process. The challenge for the company is to meet the energy demand of consumers and controlling emission. One of the approaches taken by the company is to perform an energy audit of the dwelling before and/ or after construction. The audit captures data related to heating, cooling, hot water, lighting, and appliance energy loads and consumptions. The article scope is limited to single and multi-family homes. The company implements an energy conservation objective through the program. This program is mostly implemented by a non-profit organization which focuses on green earth/energy conservation ob- jective. The c o m p a n y allocates a Fraud detection need in the energy program implementation for Utility companies By Atul Gunjal, CEO and Ameet Shedge, CTO, iWorkTech CXO INSIGHT budget and sets a target for implementation. The budget covers oper- ational expenses plus rebate or incentives offered to consumers. The objective of the program gets implemented by the staff and corrective measures are implemented by its auditor or rater. The challenges faced by the CIO is to see that each dollar spend, is well spend and to detect instances of fraud and incompetency. The proactive steps taken towards these challenges help in ensuring that the objective of the program is met. Program Implementation Platform (PIP) PIP consists of the following modules It is not hard-and-fast rule that all modules need to present. Some can be dropped or some other can be added as per the program needs. These modules are implemented as portals which can be accessed through a browser or desktop solution. Some modules are needed to be accessed by the field executives; hence it needs to be available as a mobile solution. Data Flow, approach and algorithm The data flow can be depicted as follows: First, the home data is gathered by the file executive. It is important that this data complies with the energy model. This helps to get This approach ensures that continuous Quality Assurance is performed to detect and report any anomalous result in the submitted data | |December 2014 61CIOReview consistency in the rating. To achieve this, the following is necessary: • Clear and consistent information for the rater • Training needs to cover assessment methods, measures, and data points. • Independent third-party assessment needs to be done on the field reviews and submitted data files. This is done to detect any fraud or incompetency. o Establish limit on input variables o Determine bounds check o Detect and warn users for input values beyond reasonable limits o Generate XML data which is stored in the repository for pattern detection, future use, data exchange with other systems. This approach ensures that continuous Quality Assurance is performed to detect and report any anomalous result in the submitted data. This continuous approach absolves staff from routine manual checking. The automated nature of the job helps to handle any increase in the volume of homes. This process can also detect existing patterns or establish a new pattern. This pattern is stored in the repository. This rule-based approach helps to build a flexible system. Some of this system’s silent features are: • Backend system • Sanitize data received • Analyze data based on several data validations • Possess multiple methods for o data analysis o pattern recognition • Flag files with issues • Deliver report on analysis Risks • Unknown nature of data needs to be flagged and further analyzed. This analysis helps determine whether new data needs to be consumed by the system for fraud detection or not. • Efforts involved to sanitize and manipulate data • Diverse data models Constraints • Project efforts • Support needs Dependency • Close communication is needed between involved stake holders Conclusion PIP helps CIO to keep focus on the project objective. PIP’s Fraud module helps to get consistent rating and helps detect fraud and incompetency. The fraud detection helps to keep tab on the unfair practices. By taking proactive measures, like training, helps to address issues like incompetency. Atul Gunjal