SAMAGRA VEDIKA -- TELANGANAS INTEGRATED PLATFORM Using Big data, - - PowerPoint PPT Presentation

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SAMAGRA VEDIKA -- TELANGANAS INTEGRATED PLATFORM Using Big data, - - PowerPoint PPT Presentation

SAMAGRA VEDIKA -- TELANGANAS INTEGRATED PLATFORM Using Big data, ML, Graph data base FOR BETTER CITIZEN SERVICE DELIVERY AND TRANSPARENCY, ACCOUNTABLE AND EFFICIENT GOVERNANCE ITE&C DEPARTMENT GOVERNMENT OF TELANGANA Few of ew of the


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SAMAGRA VEDIKA --TELANGANA’S INTEGRATED PLATFORM Using Big data, ML, Graph data base FOR BETTER CITIZEN SERVICE DELIVERY AND TRANSPARENCY, ACCOUNTABLE AND EFFICIENT GOVERNANCE

ITE&C DEPARTMENT GOVERNMENT OF TELANGANA

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SLIDE 2

Student scholarships

Few of ew of the w the welf elfar are e sc schemes hemes of

  • f Telangana

elangana Total

  • tal budget

budget is is mor more e than than 35,000 C 35,000 Cr r

Ration Cards Aasara Pensions Most of the welfare schemes have eligibility in terms of Income Raithu Bandhu

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Identity Fraud Quantity Fraud Eligibility Fraud These leakages have been controlled through use of Aadhar & ePOS Right Beneficiary Limited means to establish identify of right beneficiaries. Research on mitigation is currently

  • ngoing

No solution in the country as on date as it requires data of other departments Bogus Beneficiary Non-existent real beneficiary Duplicate Beneficiary Multiple registrations by same beneficiary. 1 2 3 Illegitimate Claims Claiming bills in MNREGA without work Disproportionate Quantity Availing more quantity of PDS Food grains than eligibility Is the person truly eligible? Limited means to correctly establish the eligibility

possible possible leaka leakages ges in w in welf elfar are e pr prog

  • grams

ams and and some still some still unp unplug lugged ged.

Resolution through Types of Fraud Leakage on account of

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SLIDE 4

This is probably as big as a budget of a minor department!

Quantum Quantum Of Of Leaka Leakages D ges Due ue To Wr

  • Wrong Inc
  • ng Inclusion

lusion

Total Budget For 2019-20 For Pensions ₹ 10,000 Cr Value of just 1% Leakage… ₹ 100 Crores

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SLIDE 5

El Eligibil igibility ty for

  • r man

many ben y benefits efits is is pr presc escribe ribed. d.

5

People fulfilling one or more of the following conditions listed below shall not be eligible for Aasara Pension: Classification Aspect Self-Economic Indicators Having land more than 3.0 acres wet/ irrigated dry or 7 5 acres dry. Having large business Enterprise (oil/rice mills, pumps, shop owners etc.). Owners of light and/or heavy automobiles (four wheelers and big vehicles) Family Based Parameters Having children who are Government/Public sector/ Private sector employment / Out-sourced/Contract. Having children who are Doctors, Contractors, Professionals and Self employed. Government Pensioner Already receiving Government pensions or freedom fighter pensions. Others Any other criterion in which the verification officer may asses by the manner of lifestyle, occupation and possession of assets rendering the household as ineligible

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SLIDE 6

Trad aditi ition

  • nal

al pr proc

  • cess

ess → inef ineffecti ective e implemen implementa tation tion

6

Manual Process No Digital Trail High Human Discretion Reliance on Aadhar alone Almost no accountability of

  • fficials in either error

Inclusion Error ( Benefit given to ineligible persons) Exclusion errors (Denying eligible persons)

Effects of ignoring these challenges

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SLIDE 7

Opp Oppor

  • rtunit

tunity y & Challenges & Challenges to get to get Consolida Consolidated ted view view

1. Data is in Silos 2. No Common ID 3. Integrated view – (SSOT) Single Source of Truth is not available 1. Most of the Data is in electronic form

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Sama Samagra Vedika edika One One View iew - Objec Objectiv tives es

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What is the alternative approach Without using Aadhar or any other ID But getting the same efficacy In view of Legal restrictions on use of Aadhar

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Meta da Meta data ta attr ttributes ibutes of

  • f an entit

an entity y pr present esent in in all all da data sour ta sources ces

  • Following meta data information is available in every data source

Unique Personal Details

– Name – DOB – Fathers Name ( In Some)

Unique ID* Number

– PAN or – Passport no or – Voter ID or – Driving License

Contact Details

– Mobile No. ( In some) – Address (Res) – Address (Off)

Photograph ( Some data sets) *Any one ID is present

  • All records in all data

sources have Name, Address.

  • Some records also have

DoB, Phone Number, Fathers Name, Photo

  • Can a combination of these

attributes which are already available in every record be used to identify an entity

  • With an Accuracy nearer to

Aadhar based linkage

  • With no manual intervention
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SLIDE 11

3 “V” CHALLENGES

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Examples of variations in Name & Father’s Name

Spelling Abbreviations Sequence Variation Addition/ Deletion Splitting

  • N Radha Murali Krishna
  • N Radha Muralee Krishna
  • N R Murali Krishna
  • N R M Krishna
  • Murali Krishna N Radha
  • N M Radha Krishna
  • N Murali Krishna
  • Murali Krishna
  • Murali Krishna N Radha
  • Radhakrishna N M
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13

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14

Key ey Per erfor

  • rman

mance ce Metr Metrics ics

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FOL FOLLOWIN WING G TEC TECHN HNOL OLOGI GIES S AR ARE E US USED ED

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Economi Economic c Sur Survey ey 2019 2019 of

  • f GOI

GOI has has pr praised aised Sama Samagra Vedika edika

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SLIDE 17

Accurate targeting of subsidies Beneficiaries for Old age pensions

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Using Using Sama Samagra Ved edika ika for

  • r ne

new sanc sanction tions s of

  • f Aasa

Aasara Pen ensions sions

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Total new applications received Total eligible applications (as per Samagra Vedika) Total ineligible applications (as per Samagra Vedika) 65,693 59,068 6,625 (10.1%) Value Rs 16 Cr per anum

Eligibility for Aasara Pensions is now 57 years New applications are being received (expected about 7 to 8 lakh new pensioners ) 65,693 new applications approved by the Districts officials after verification Are sent to SERP for sanction Aasara Pensions In Aug 2019 SERP requested ITEC to check the eligibility Through Samagra Vedika platform

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Accurate targeting of subsidies Predictive analytics based identification of beneficiaries for 2 BHK scheme using Big data

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Govt vt of

  • f Telan

elangan gana has a pr has a prog

  • gram

am to p to provide vide 2B 2BHK hou houses ses to to economicall economically y weak eaker sections er sections

  • Started in 2015 by the Govt. Of Telangana to provide 100% subsidized housing to the poor.
  • No beneficiary contribution needed – one of its kind in India.
  • Construction cost = 7-8 lakhs/house and total cost including land is 15-20 lakhs/house

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Total Houses constructed under the scheme Total applications received

2000

11,681

In one district

Aligned with the objective of implementation of scheme in the entire state, Govt of Telangana is looking to distribute 2BHK houses to eligible persons. The significant expenditure by Govt, and high mismatch in number of applicants and available houses has necessitated a very careful approach towards allotting the houses to applicants.

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Key observations

Earlier Beneficiaries

  • f Housing Schemes

Already owing Houses Financially well-off applicants

It is difficult to correctly identify the right beneficiaries

  • basis the information collected in the

application form.

  • basis any other information available with

district adminstration

  • Manual system

Some of the applicants have received subsidized housing earlier, but are reapplying using a family members name or their name Certain applicants have submitted low incomes certificates even though they are financially well off Some applicants or their family members already own a house.

Sid Siddipe dipet Dist Dist. . Ad Administ ministration tion had had f follo

  • llowing k

wing key ey obser

  • bserva

vation tions. s.

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Pr Predic edictiv tive e ana analytics ytics using big using big da data ta used f used follo

  • llowing

wing da data se ta sets ts availa vailable ble with with Go Govt. vt.

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❑ Name ❑ Fathers name ❑ Address ❑ Aadhar number ❑ Phone number ❑ Photo of the applicant ❑ Minor info. Electricity connection Water connection House and land database Old age pension schemes Vehicles database Ration card database

datasets available Information provided by applicant

Information about family members not provided in the application

Common databases are matched with the provided info. and they are further analyzed to bring out valuable insights in the form of applicant categories

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The a he anal nalysis ysis using Sama using Samagra a Vedik edika a ca cate tegor goriz ized t ed the he applic pplicant ants s in in four

  • ur

ca cate tegories gories as f as follo

  • llows:

ws:

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Categories -> Category 1 Category 2 Category 3 Category 4

Classification

Qualify Qualify with verification Consider as low priority Don’t consider Not financially well off No housing benefits previously accepted No other welfare schemes prior From Siddipet - SKS

Count (% of Total)

2363 (20.2%) 2678 (22.9%) 2181 (18.7%) 4459 (38.2%)

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Pilot at Hyderabad in Aug 2016

In Hyderabad about 1,00,000 cards were removed in Aug 2016 There was some public resistance due to which people were asked to apply again. About 19,000 applied as on Dec 16, 14,000 cards were activated again after verification that the property is very small or the four wheeler is taken out of loan etc. Net about 86,000 cards are removed from August 16. Total subsidy saved is Rs 4.6 Cr every month from Aug 16 onwards. The mistake of tagging the vehicle/house to a wrong person is less than 5% which shows the efficiency

  • f the application
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TH THANK ANK YOU OU