Cash transfers to citizens through the Direct Benefit Transfer (DBT) infrastructure are among the most prominent developments in India’s social protection policy landscape. Our field engagements and empirical work reveal the presence of some fault lines in the delivery process of DBTs, causing the exclusion of some citizens. We use a proprietary framework that characterises various barriers to accessing social protection across four stages of the delivery chain – namely, identification, targeting, payment processing, and cash withdrawal. Notably, payment failures during back-end processing emerge as a significant concern – where enrolled beneficiaries do not receive the DBT into their bank accounts for various reasons.
Understanding the landscape of payment failures that occur during the backend processing of cash benefits requires a multi-pronged approach, since citizen surveys alone are unlikely to reveal technical reasons behind the payment delays/failures. Accordingly, we complement our survey work with the analysis of data from administrative sources. The following lessons emerge from this multi-pronged approach.
Findings from the Dvara-Haqdarshak Survey on Government-to-Person Payments:
The Dvara-Haqdarshak survey on government-to-person payments was designed with the objective of validating our ‘framework’ of exclusion and also measuring its prevalence across the dominant social protection schemes for citizens. The survey sample comprised of a total 1477 beneficiaries of the following schemes: National Social Assistance Pensions (NSAP), Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), Pradhan Mantri Kisan Samman Nidhi (PM Kisan), Janani Suraksha Yojana, and Pradhan Mantri Matru Vandana Yojana. The sample was selected from six districts across the states of Assam, Chhattisgarh, and Andhra Pradesh. Approximately 80 citizens were sampled under each scheme in each of the three states, except for PM Kisan in Assam. Below are some headline findings from the survey:
- 72.85% of surveyed respondents reported experiencing some issues during the processing of their payments.
-
Of all such respondents, 51% experienced disruptions to the payment schedule. This may imply any interruption to scheduled disbursements of a welfare scheme. For instance, a month of pension may be missed, the first due instalment to the citizen may be delayed, or MGNREGA wages may not be processed as funds have not been received by the Panchayat.
-
18% experienced ‘Bank Account and Aadhaar-related issues
, indicating that citizens’ payments failed due to errors in their Aadhaar IDs, KYC procedures, or Aadhaar-bank account seeding.
-
- Of survey respondents who experienced ‘Bank Account and Aadhaar-related’ issues:
- 36% said their payment was held up due to spelling errors in Aadhaar.
- 18% reported an error in their Aadhaar-bank account seeding.
- 32% experienced a pending KYC.
Findings from analysis of payments failure data (PM Kisan):
A survey-based approach to discovering fault lines in the back-end processing of payments may be limited, as respondents are unlikely to have complete visibility over the reasons a payment does not come through. To supplement the above survey, we undertook an analysis of data scraped from the publicly available PM Kisan dashboard. PM Kisan is one of the few schemes wherein the instalment status of each beneficiary is made available as part of a village-wise dashboard in the public domain. The data scraped revealed the reasons for payment failures for farmers in the East Godavari[1] district in Andhra Pradesh whose PM Kisan payments had failed (N=39,655).
- 51.3% of beneficiaries under the PM Kisan scheme experienced payment failures due to Aadhaar-related reasons. This may imply that the individual’s ‘Aadhaar number is not seeded in NPCI’ or that their ‘Aadhaar number already exists for the same Beneficiary Type and Scheme’[2].
- For 18.5% of such records, the reason for payment failure was reflected as ‘Correction pending at state’, possibly indicating that the correction in beneficiary records was yet to be approved by the state government.
- 5.3% of beneficiaries under the PM Kisan scheme experienced payment failures due to a bank-related error.
Reflecting on these results and the more qualitative aspects of our work (such as stakeholder and citizen interviews), we make the following recommendations:
- Improving coordination between organisations:
To resolve the key issues that arise during payment processing, there is a need for increased coordination between the organisations involved in the backend processing of DBT payments (such as the National Payments Corporation of India (NPCI), Reserve Bank of India (RBI), and beneficiaries’ banks (typically commercial/postal banks), the respective scheme’s implementing government department, etc.). For instance, while notifications from the Ministry of Finance have instructed banks to eliminate 12 types of errors in DBT payments, these errors persist. We seek to understand the information flows across these entities to suggest how streamlining communication may allow them to work in tandem to improve the system.
We recommend the creation of a common Grievance Redress Cell for all DBT schemes across tiers: State, District and Block. Ideally, appointees for a state-level cell should belong to all agencies involved in the DBT system – the relevant Ministry/Department/Implementing Agency, Ministry of Finance, NPCI, UIDAI, and State Level Banker’s Committee (SLBC) Convenor Banks and Lead Banks.
-
-
- Facilitating transparency by improving channels of communication 2.1
-
a.Publication of DBT transaction failure data:
- We understand that DBT payment files already contain status responses (success/failure) and the root cause for any failure. Further, the Standard Operating Procedure for Aadhaar Payments Bridge prescribes that member banks should conduct a return analysis to analyse the root cause for DBT returns. The regular publication of such transaction failure data would facilitate greater transparency and improvements in the system over time.
A suggested template for such reports may include fields for location type (urban/rural), scheme, transaction volume, the root cause for payment failure, etc.
b.Publication of grievances related to payments:
- Typically, grievances about the payments system are collected by banks. The collation and analysis of such grievances relevant to DBT payments particularly could prove beneficial in identifying pain points in backend processing.
We are keen to explore the potential for the NPCI to aggregate such grievance data for further analysis and to also publish said data publicly. Further, we see considerable potential in creating feedback loops by leveraging grievance and failure data to improve system performance and reduce the prevalence of errors.
2.2 Communications between NPCI and Beneficiaries:
c. Communicating root causes of transaction failures:
- Our research reveals that citizens are often unaware of why their benefits are not credited to their accounts. Even for schemes such as PM Kisan, where the facility of a dashboard containing these details is available, a lack of awareness persists. Overall, beneficiaries do not have a clear picture regarding the backend of DBT payments. As a result, they cannot take necessary actions to correct issues hindering their payments.
Live tracking of the application and the specific reason for pendency/rejection must be added to the beneficiary’s online record across schemes. Beneficiary records should also include the next step the beneficiary can follow to resolve the issue.
d. Enabling citizens to check Aadhaar seeding status:
Our research reveals that citizens may be unaware of the status of their Aadhaar number being seeded in the NPCI mapper, which leads to some difficulty in resolving the issue itself. A March 2013 circular issued by NPCI clarifies the presence of an ‘Aadhaar Lookup Feature’ on the NACH system, which permits banks to know the status of an individual’s Aadhaar mapping in the NACH system.
Encourage banks to use the Aadhaar Lookup Feature to convey Aadhaar seeding status to citizens upon request. This will increase transparency in the system and facilitate easy resolution of issues.
[1] This district has been chosen for illustrative purposes only.
[2] Error categories are obtained through the data scraping exercise.
Cite this blog:
APA
Narayan, A. (2022). Payment Failures in Direct Benefit Transfers . Retrieved from Dvara Research.
MLA
Narayan, Aishwarya. Payment Failures in Direct Benefit Transfers . 2022.
Chicago
Narayan, Aishwarya. 2022. Payment Failures in Direct Benefit Transfers .
