Basic Info

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Nominee Information

Institutional Information

Member State Indonesia
Institution Name Ministry of Finance
Institution Type Ministry
Ministry Type Ministry of Finance, Budgeting and Planning
Administrative Level National
Name of initiative Directorate General of Fiscal Balance
Projects Operational Years 2
Website of Institution http://www.djpk.kemenkeu.go.id/

Question 1: About the Initiative

Is this a public sector initiative? Yes

Question 2: Categories

Is the initiative relevant to one of the UNPSA categories? Category 2: Enhancing the effectiveness of public institutions to reach the SDGs
UNPSACriteria
NoItems

Question 3: Sustainable Development Goals

Is the initiative relevant to any of the 17 SDG(s)? Yes
If you answered yes above, please specify which SDG is the most relevant to the initiative. (hold Ctrl to select multiple)
Goal 1: No Poverty
Goal 16: Peace, Justice and Strong Institutions
Goal 17: Partnerships for the goals
Which target(s) within the SDGs specified above is the initiative relevant to? (hold Ctrl to select multiple)
1.a Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensions
16.6 Develop effective, accountable and transparent institutions at all levels
16.10 Ensure public access to information and protect fundamental freedoms, in accordance with national legislation and international agreements
17.1 Strengthen domestic resource mobilization, including through international support to developing countries, to improve domestic capacity for tax and other revenue collection

Question 4: Implementation Date

Has the initiative been implemented for two or more years Yes
Please provide date of implemenation (dd/MM/yyyy) 26 Mar 2019

Question 5: Partners

Has the United Nations or any UN agencies been involved in this initiative? No
Which UN agency was involved? (hold Ctrl to select multiple)
Please provide details

Question 6: Previous Participation

1. Has the initiative submitted an application for consideration in the past 3 years (2017-2019)? No

Question 7: UNPSA Awards

Has the initiative already won a UNPS Award? No

Question 8: Other Awards

Has the initiative won other Public Service Awards? Yes
If yes, please specify name, organisation and year. Top 45 of Public Service Innovation Awards, Ministry of Administrative and Bureaucratic Reform

Question 9: How did you learn about UNPSA?

How did you learn about UNPSA? UN

Question 10: Validation Consent

I give consent to contact relevant persons and entities to inquire about the initiative for validation purpose. Yes

Nomination form

Questions/Answers

Question 1

Please briefly describe the initiative, what issue or challenge it aims to address and specify its objectives (300 words maximum)
The fulfilment of essential services in local governments (LGs) is still uneven, marked by the unequal Human Development Index between regions (Yogyakarta had 86.11 in 2018 while Nduga had 29.42). The quality of regional spending needs to be improved so that the budget for essential services, like education and health, can be optimized. In 2018, 118 LGs have not met the mandatory spending for education of at least 20% and 26 LGs have not met the mandatory spending for health of at least 10%. That low compliance is due to the lack of adequate data and monitoring tools. Moreover, the LGs’ fiscal independence is still low with the contribution of Local Own-Source Revenue to total revenue in 2018 of only 24.6% limiting the LGs’ fiscal space to boost essential services. Law Number 33 of 2004 mandates LGs to submit financial information to the Ministry of Finance (MoF) through a financial information system named SIKD. In its implementation, the 542 LGs have different systems and charts of accounts, causing problems in further data processing which takes a long time and the risk of human error due to manual processes. Since 2019, the MoF has begun to apply artificial intelligence for data standardization and compilation to increase the efficiency and reliability of providing data. This innovation aims to provide real-time and reliable data through the AIFA Dashboard which provides a policy framework for improving regional budgeting to fulfil essential services so that evaluations that were initially only able to be carried out in the following year can be carried out during the current year. The financial advices can accelerate LGs in responding to gaps in the fulfilment of essential services through budget adjustments. Regional budget performance data can also be easily accessed by the public through the website to increase transparency.

Question 2

Please explain how the initiative is linked to the selected category (100 words maximum)
AIFA improves accountability and transparency by providing real-time and publicly accessible regional budget performance data. AIFA can be used as a means of policy harmonization and collaboration, like analysis of regional spending priorities for budget reallocation on handling the pandemic. AIFA can improve regional financial management in achieving the SDGs indicators. The technology used is capable of interconnecting data between systems in LGs and MoF’s SIKD so that data from 542 LGs with different systems can be consolidated and evaluated automatically and in real-time. This data is also used by other institutions within the framework of One Indonesia Data.

Question 3

a. Please specify which SDGs and target(s) the initiative supports and describe concretely how the initiative has contributed to their implementation (200 words maximum)
Goal 1 The Improvements from 2018 to 2021 are: • for education budget fulfilment, increased from 424 to 458 LGs, meanwhile for health budget, increased from 516 to 524 LGs; • the share of the education budget increased from 28.0% to 28.9%, and health increased from 13.9% to 15.7%. AIFA provides features for monitoring and evaluating the fulfilment of mandatory spending so that LGs are more motivated to fulfil it because the data can also be accessed publicly. Goal 16 The deviation of budget realization is more controlled because monitoring and evaluation of budget realization are carried out in real-time. In 2020, with uncertain conditions, the deviation of realization against the adjusted budget was very small (98.5% for revenue and 95.2% for expenditure), compared to the initial budget (84.1% for revenue and 79.0% for expenditure). Regional financial data can be accessed through http://www.djpk.kemenkeu.go.id/portal/data/apbd with monthly updates while previously it was only available on a semi-annual basis. Goal 17 The contribution of PAD in 2021 increased to 26.8%, compared to 2018 at 24.6%. AIFA provides an evaluation of regional revenue performance to the detailed level, like a tax for hotels and restaurants, to monitor the types of taxes that require optimization.
b. Please describe what makes the initiative sustainable in social, economic and environmental terms (100 words maximum)
● Social: Financial advice in AIFA can be accessed by all LGs with equal treatment. The LGs are also involved in the pilot project so that the model is implemented according to the LGs’ needs and ensures the continuity of innovation. ● Economic: AIFA can improve regional financial management as a catalyst for a sustainable regional economy, such as through optimizing local taxes so that LGs are more fiscally independent and improving the quality of spending. ● Environmental: AIFA digitization supports environmental sustainability (paperless). The detailed data produced can support regional climate budget tagging and monitoring of environmental spending.

Question 4

a. Please explain how the initiative has addressed a significant shortfall in governance, public administration or public service within the context of a given country or region. (200 words maximum)
Improving the fulfilment of essential services in the regions requires improvement of spending, especially related to the fulfilment of mandatory spending in education and health. Control over that fulfilment is constrained by the limited data. Manually collected data cause them too long to be available so that the evaluation is also late. The use of machine automation in this innovation is able to speed up data processing from 16,000 minutes (manual process) to 60 minutes for 542 LGs’ data, increasing the relevance and transparency of providing data to a more detailed level. Monitoring and evaluation of the quality of regional spending can be done in a real-time so that when there is a gap in the effort to fulfil essential services, advisory can be given through the AIFA Dashboard and the LGs can respond more quickly through budget adjustments, such as adding to the education and health budget. Advisory is also given regarding the optimization of local tax revenues, which are still not performing well year on year, so that local governments can respond with the proper strategy. This automation also supports the preparation of GFS more quickly and reliably so that national decision-making can be better carried out.
b. Please describe how your initiative addresses gender inequality in the country context. (100 words maximum)
The text analysis method using artificial intelligence can easily and quickly provide regional expenditure data related to gender equality. It can support proper monitoring and evaluation to improve the quality of regional spending that is more gender responsive, for example related to the size of the women's empowerment budget and evaluating whether the use of these expenditures has been productive or is it still only for personnel expenditures (honorarium and official travel). The community can also easily participate in monitoring the budget in their area related to gender issues.
c. Please describe who the target group(s) were, and explain how the initiative improved outcomes for these target groups. (200 words maximum)
The target of this innovation is LGs throughout Indonesia by providing financial advice to LGs as public servants who are closest to local communities. Suggestions for improvement that can be accessed by the LGs in real-time and online will be a trigger that can be quickly responded by the LGs to correct deficiencies in regional financial management in the current year, so that the impact can be directly felt by the community. For example, the analysis of spending priorities during the pandemic provides input to LGs to be able to properly reallocate and refocus their budgets in the context of handling the impact of COVID-19, such as Rp30.4 trillion for the health budget, Rp22.8 trillion for social safety nets, and Rp19.2 trillion for economic recovery. The forecasting module can also optimize cash management in the regions by 8.2% (Rp102 trillion at the end of 2019 to Rp94 trillion at the end of 2020), so there is not much idle cash in the bank and can be used to accelerate spending on essential services. Communities who can easily access regional financial management performance data can also provide social control for improvements directly to the LGs.

Question 5

a. Please describe how the initiative was implemented including key developments and steps, monitoring and evaluation activities, and the chronology. (300 words)
AIFA is built using artificial intelligence which consists of two main stages, the automation of data standardization and analytic dashboards. First, data standardization is done using automatic text classification (Natural Language Processing) which replaces the manual process. Second, standardized data is used for analysis in analytical dashboard that provides a policy framework related to regional budgeting through automatic and real-time financial advice. This is supported by the already established interconnection between the LGs’ system and SIKD so that data is sent daily and automatically. The dashboard consists of a homepage containing budget realization and five modules: 1. data anomaly detection for detecting data errors using baseline analysis, box plots, and quadrant analysis. 2. evaluation of regional budget performance using year-on-year and thematic analysis of spending using word cloud that can be used to accelerate the fulfilment of essential services and increase Local Own-Source Revenue. 3. forecasting uses exponential smoothing to improve cash management and projections for the following year's budget posture. 4. analysis of spending priorities using the Analytical Hierarchy Process as a strategy for reallocation. 5. monitoring the achievement of socio-economic indicators. (sample: http://bit.ly/AIFA_Kab_AcehBarat ) This model was developed flexibly by evolving needs, like adding an analysis of spending priorities in response to the need for budget reallocation during the COVID-19 pandemic. In 2022, it is planned to add a module to this model to be able to monitor economic indicators in more real-time by utilizing big data, like mobility index and media sentiment analysis. In addition, this model has been proposed to be integrated with the harmonization model between central and regional expenditures within the framework of integrated funding. Model is evaluated periodically to ensure the accuracy of the text classification engine. The dashboard is also evaluated by LGs to suit their needs, like adding analysis per work unit.
b. Please clearly explain the obstacles encountered and how they were overcome. (100 words)
The obstacle is the resistance to new technology and the readiness of LGs to utilize the financial advices. This innovation uses Soft Systems Methodology to formulate feasible and desirable solutions, for example by transitioning the automation in Excel which is more familiar to use. After the automation is accepted, continuous improvement is carried out by introducing new tools, like Python and Tableau, together with capacity building. By 2021, LG’s financial system had been migrated, which hampers data transmission and data quality. The MoF always coordinates with LGs and the Ministry of Home Affairs so that data is still transmitted properly.

Question 6

a. Please explain in what ways the initiative is innovative in the context of your country or region. (100 words maximum)
AIFA is a pioneer in the use of artificial intelligence in the government. The use of machines capable of automating data processing can significantly increase the efficiency of time and human resources so that these resources can be diverted to more strategic matters. Monitoring and evaluation of policies can also be carried out in real-time so that LGs can be more responsive in making budget adjustments to improve regional financial management in order to accelerate the fulfilment of essential services. Transparency and public accountability have also increased because the publication of data is carried out more quickly and in detail.
b. Please describe, if relevant, how the initiative drew inspiration from successful initiatives in other regions, countries and localities. (100 words maximum)
The development of digital government and open data in government are the inspiration for this innovation. Then, this inspiration is translated into a financial advisor model by utilizing artificial intelligence and data analytics which are very commonly applied in the private sector. In accordance with the New Public Management (NPM) framework, the practice in the private sector is applied in the government so that the efficiency achieved in the private sector can also be gained in the government.
c. If emerging and frontier technologies were used, please state how those were integrated into the initiative and/or how the initiative embraced digital government. (100 words maximum)
This innovation was developed using technology to build artificial intelligence in the context of human-machine collaboration. The concept that was built on the automation of data standardization using text analysis, data analytics methods to build a financial advisor model, and the construction of a dashboard with Tableau is a very adaptive concept to technological developments. For example, this model can be combined with Python, which is an open source and is being popularly used as a tool for data analytics. This model also allows it to be implemented later using cloud technology.

Question 7

a. Has the initiative been transferred and/or adapted to other contexts (e.g. other cities, countries or regions) to your organization’s knowledge? If yes, please explain where and how. (200 words maximum)
AIFA has been presented in some knowledge sharing with other units in Central Government and LGs. This model has also been written as a short course project from the Australia Awards Indonesia and was accepted. The concept of text classification has been adapted to the spending thematic analysis in order to gain more information detail from regional financial data. The automation process of text classification can be utilized to gain information detail about the stunting budget in LGs for Stunting Prevention Acceleration Team of Indonesia; regional climate budget tagging for LGs; regional budget in handling TBC, AIDS, and Malaria for The Global Fund to Fight AIDS, Tuberculosis, and Malaria Indonesia, Country Coordinating Mechanism; the infrastructure budget in LGs for Ministry of Public Works and Housing.
b. If not yet transferred/adapted to other contexts, please describe the potential for transferability. (200 words maximum)
This innovation has the potential to be developed more comprehensively and applied or adapted by other units. - Idea: The budget performance evaluation for each LG is also accepted by LGs and declared to be adoptable for evaluating the budget performance for each working unit in LGs. - System: AIFA is developed flexibly according to the user's needs so that this system can be adopted for another modelling. - Technology: AIFA uses tools that are easy to operate so that the transfer learning process can be done easily. The technology developed in the future uses an open-source platform, like Python, making it easy to adopt.

Question 8

a. What specific resources (i.e. financial, human or others) were used to implement the initiative? (100 words maximum)
- 7 data analysts, 5 data scientists, and 4 data engineers at the MoF; a Business Leader and 2 data analysts per LG. - Technology infrastructure, like PC, PostgreSQL databases, data analytics tools, and internet access. - The budget for purchasing a Tableau license at the MoF is IDR80 million a year. Budget efficiency from eliminating overtime for manual input with an average budget of IDR8 million a month, paperless thereby reducing scan and warehousing budgets by IDR200 million a year, and budgeting for preparing manual reports at the LGs because the data is automatically and digitally interconnected.
b. Please explain what makes the initiative sustainable over time, in financial and institutional terms. (100 words maximum)
- Regulation: the MoF Regulation Number 231/PMK.07/2020 which recognizes the data interconnection as a mechanism for submitting data to SIKD and Decree of the MoF Number 91/KMK.01/2021 which includes data analytics as a Strategic Initiative of the MoF. - Managerial: determination of the work team by the Director General of Fiscal Balance, periodic training programs for work teams, talent management through careers as a computer scientist and Central and Regional Financial Analysts. - Budget: budget is provided annually for this project because it has been included in the Ministry of Finance's Strategic Plan document.

Question 9

a. Was the initiative formally evaluated either internally or externally?
Yes
b. Please describe how it was evaluated and by whom? (100 words maximum)
- Evaluation of text classification and benchmarking with other text classification techniques, for example, using Python; - There are KPIs related to follow-up on feedback from data users; - There are KPIs related to data service user satisfaction; - Evaluation of local government deposits in banking (related to the forecasting feature); - Evaluation of the quality of budget refocusing and reallocation during the fiscal year 2020 COVID-19 pandemic (related to the spending priority analysis feature); - Evaluation of the dashboard from the local government as a user. - Evaluation of essential service achievements. - Evaluation of regional fiscal independence.
c. Please describe the indicators and tools used (100 words maximum)
- Evaluation of text classification results done periodically, specifically comparing them with the standard chart of accounts; - KPIs follow up on feedback on data usage so that it becomes material for improving data management; - Service user satisfaction surveys are provided to users; - Optimization of regional cash balances in banking; - Deviation of APBD realization against the target of refocusing and budget reallocation based on spending priorities. - Local government satisfaction survey on the dashboard. - Fulfilment of mandatory spending. - Local Own-Source Revenue contribution.
d. What were the main findings of the evaluation (e.g. adequacy of resources mobilized for the initiative, quality of implementation and challenges faced, main outcomes, sustainability of the initiative, impacts) and how this information is being used to inform the initiative’s implementation. (200 words maximum)
• Text classification is 100% successful in classifying according to standards; • All feedback has been followed up with improvements (realization of the 2020 KPI reached 120 of 100); • The data service user satisfaction index reached 4.65 (target 4.5), the response rate reached 100% in 2020, and public information disclosure had a score of 80.14 in 2021 (“Towards Informative”); • Optimization of regional cash balances in banking by 8.18%; • The realization of the 2020 APBD amidst the uncertainty during the pandemic approached the refocused budget (98.5% for revenue and 95.2% for expenditure); • Score of the LGs’ satisfaction survey on the dashboard is 3.42 out of 4. • Local governments that fulfil education and health spending increased from 75.6% to 82.5%. • The contribution of Local Own-Source Revenue increased from 24.6% to 26.8%. From the evaluation results, the model is considered to be quite representative of the needs of the LGs. However, policy developments and local government needs will be very dynamic, so more flexible analytical tools are needed, such as the use of cloud technology. This is to ensure that the available resources are not used up only for technological adjustments to development needs, but can focus on the analysis.

Question 10

Please describe how the initiative is inscribed in the relevant institutional landscape (for example, how it was situated with respect to relevant government agencies, and how the institutional relationships with those have been operating). (200 words maximum)
AIFA is used to strengthen the role of the MoF through an evaluation of fiscal decentralization using digital platform to ensure optimal use of intergovernmental transfer. This innovation has become a Strategic Initiative of the MoF. The use of data in SIKD as a single true source for regional financial data has also been used by various related parties, like (i) the Central Bureau of Statistics for the release of GRDP data; (ii) the Ministry of Education, Culture, Research, and Technology for evaluation of education spending; (iii) the Ministry of Health for the National Health Account; (iv) the Corruption Eradication Commission for the JAGA KPK application; (v) the Ministry of National Development Planning for the SEPAKAT application; and (vi) Bank Indonesia for monetary studies. This supports the role of the MoF as the supervisor of financial data in the implementation of One Indonesia Data. AIFA has also succeeded in realizing the simplification of reporting and the efficiency of business processes in LGs so they only need to submit reports to SIKD as single true source. Other parties who need regional financial data no longer need to ask directly to the LGs since they will be provided through SIKD.

Question 11

The 2030 Agenda for Sustainable Development puts emphasis on collaboration, engagement, partnerships, and inclusion. Please describe which stakeholders were engaged in designing, implementing and evaluating the initiative and how this engagement took place. (200 words maximum)
Responsible Director General of Fiscal Balance who is responsible for the development and implementation of AIFA. Approval The Minister of Finance is authorized to decide on this innovation as a tool for evaluating and providing official financial advice through the determination as a Strategic Initiative for the MoF. Support • The LGs act as a producer of financial data that submits data to SIKD as the basis for the data analytics process in the context of preparing financial advice, and the LGs provides feedback during piloting so that the model can be implemented and according to the needs of the LGs; • Financial system developer in each local government as a supporter related to the technical interconnection of LGs’ data to SIKD. Consult Consultation with decision-makers at LGs regarding the information and advice needed to make the model implementable. Informed • Internal leaders of the MoF as users of data analytics results in supporting the process of policy formulation and decision making; • Leaders in external ministries/agencies as users of regional financial data for sectoral financial performance evaluation; • LGs as the beneficiary of the financial advisor model in order to improve the quality of regional financial management.

Question 12

Please describe the key lessons learned, and how your organization plans to improve the initiative. (200 words maximum)
Policy evaluation can utilize technology so that it can be carried out in a more real-time. This is able to encourage quick responses from policy makers and public service providers so that the impact can be immediately felt by the community. Soft control is a more effective approach in order to make related parties follow the wishes of policy makers, compared to hard control. This is in line with The Nudge Theory which has a concept of the formation of an environment that indirectly makes the parties voluntarily follow the wishes of the policy makers. Through the development of AIFA, LGs will voluntarily submit data to the MoF in the context of preparing fiscal consolidation as a basis for policy making at the national level because LGs feel the benefit by receiving financial advice and feel supervised by the public who can easily access the data. The MoF is making and will refine the roadmap for the use of data analytics in order to build a data-driven organization so that an evidence-based policy can be realized. Human resource development in LGs also needs to be carried out so that this model can be the developed independently in each LGs.

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