Qlik helped save money for Kazakhstan taxpayers

The Datanomix.pro team, a Qlik partner in Kazakhstan, has developed a system for auditors who check the transparency of public procurement in the country.

The Datanomix.pro team found out how global specialists assess financial risks. Experts studied law enforcement data and identified 43 risks that may arise from planning to contract execution.

They adapted these methods to the public procurement system of Kazakhstan and developed an automatic risk check system based on Qlik Sense. The analytical tool focuses the auditors’ attention on the most risky purchases, increasing the effectiveness of audits.

Project background

Every year, 18 thousand organizations enter into more than 1.5 million public procurement contracts in Kazakhstan. Previously, the process of monitoring public procurement was carried out by specialists of the Internal State Audit Committee manually. As a result, a large share of non-competitive purchases at inflated prices remained unnoticed by auditors.

When choosing a contractor for a project, the Committee looked for experience and understanding of the subject area. The Datanomix.pro team already had twelve years of experience in analyzing data to identify economic and corruption risks. As well as a portfolio of completed projects in this area: for the Financial Police, the Prosecutor General's Office, the Anti-Corruption Service, the Economic Investigation Service and the Financial Intelligence Unit - all in the Republic of Kazakhstan.

The project team chose Qlik Sense as a platform for the future tool. The decision was influenced by the high speed of development, the ability to connect many disparate sources, and the flexibility in examining data for end users. The latter is especially important - only the auditor makes the final decision on each risk indicator, so the tool had to have the freedom to explore the data.

Project implementation

The auditors placed high demands on the automation of the public procurement monitoring process.

The "OKO" project consisted of several stages:
1.      Adapting the risk assessment methodology to the goals and objectives of auditors.
2.      Conducting a needs assessment. Relevant government officials provided information on their use cases.
3.      Development of interactive dashboards based on Qlik, which cover the auditor needs to carry out the preliminary control function.
4.      Development of training materials and checklists, as well as training. About 40 civil servants were trained on how to use the service.

The developed system based on Qlik Sense analyzes all stages of the public procurement process, including 3 million items of procurement plans, 1.5 million contracts and 700,000 lots. "OKO" identifies risky planning processes, tenders and contracts, narrowing the selection for auditors. It focuses their attention from 3 million positions to 130,000 - with the greatest risk of wastage.

The analytical system is also integrated with the treasury, the register of legal entities, the register of state enterprises, the database on certificates of origin of goods and other registers to expand the documentary base.

Risk Assessment Methodology and Data Sources

"OKO" uses several data sources that are updated daily and contain over 100 million rows. Below are some of the risks that are identified for certain data sources.

1. Public procurement data: plan elements, tenders, contracts, clients, suppliers, etc.

• Procurement plans help at the stage of early identification of risks, when there is an opportunity to intervene and prevent them. This includes looking for signs of unreasonable restriction of competition (for example, purchasing from only one source), non-competitive purchases of overpriced goods, or too short turnaround times that artificially limit competition.
• One possible risk scenario: overpricing when purchasing from a single source. "OKO" algorithms analyze the median price for each specific product during the year and identify price anomalies. The auditors then analyze these anomalies and, if necessary, take action to adjust the price and change the procurement method to a competitive one.
• The algorithm uses tender data to identify "red flags" of unreasonable restriction of competition. For example, an indication of a specific brand in a lot or a combination of unrelated goods in one lot. "OKO" also identifies "red flags" of possible supplier collusion and identifies the risks of potential corruption between the customer and the supplier.

2. Tax data of legal entities. The system uses data on taxes paid by legal entities to check the reliability of suppliers and look for signs of shell companies.
3. Database of certificates of goods origin. According to the rules of public procurement in Kazakhstan, for some groups of goods there are preferences for local producers. However, unscrupulous vendors often forge such certificates. Therefore, for each contract, "OKO" automatically checks whether the supplier had a valid certificate at the time the contract was concluded.
4. Register of unscrupulous suppliers. Some suppliers are not allowed to enter into public procurement contracts. The system automatically checks if the supplier is on the list of dishonest suppliers.

Project results

The Internal State Audit Committee of the Republic of Kazakhstan reported it had already identified financial irregularities in the amount of $93 million. Almost 70% of "red flags" were identified at the planning stage and prevented.

A total of 1,685 procurement department employees were disciplined. As a result of audit activities, 190 cases were transferred to law enforcement agencies.

"The Committee has carried out work to improve the risk management system. In 2021, together with Datanomix.pro, an analytical system for assessing and identifying risks in the public procurement system was developed. This system was presented to the head of state at the Digital Bridge forum and received a prestigious international award, competing in the final with Shell and the UK Government Purchasing Service"

Almas Kavkitaev, Deputy Chairman of the Internal State Audit Committee of the Ministry of Finance of the Republic of Kazakhstan

Automating the identification of risks in public procurement has proven its effectiveness, so the Ministry of Finance approved the financing of the "OKO" project in 2022 as well.

The "OKO" project has already received international recognition at the World Commerce & Contracting Innovation & Excellence Awards 2021 and won in the Operational Improvements nomination. The award recognizes projects that have delivered significant business value through innovation in contract management processes. Shell, the UK Public Procurement Service, Canada Post, Baxter International, Aramco Americas and others competed in the final with the "OKO" project.

Our system is a symbiosis of a person and a computer: it identifies risks, shows the so-called "red flags", draws the attention of inspectors to them, but does not label them, because often the cause of offenses is not malicious intent, but the human factor - a mistake, inattention or elementary laziness.

Vitaliy Trenkenshu, CEO of Datanomix. pro

Looking ahead: an open audit process

From the start of the project in March 2021 to September 24, 2021, the analytical system automatically analyzed 2,115,344 procurement plan items and 1,159,639 contracts worth more than $14 billion. As a result, 132,929 positions with risk factors were identified.

To raise awareness of public procurement issues in the country, it is necessary to open up the audit and monitoring process by providing NGOs and journalists with access to the Qlik-based analytical tool "OKO" developed by Datanomix.pro.

Datanomix.pro will enhance the analytics tool with a CRM system so that NGOs can analyze risky purchases and automatically generate letters to procuring entities and regulators for due process. There are already 10 Kazakh NGOs ready to create a stakeholder platform in Kazakhstan. There is an agreement to share access to the tool and conduct training for civilian observers.

Source: VC.RU