Simonov A.A., Murin A.V., Koltsova E.A.
Ivanovo State Power Engineering University, Russia

Partner’s or Client’s Reliability:

Primary Assessment Methodology

The article addresses the problem of relationship with partners and clients in various business spheres. The issue raised is of great importance as a lot of companies face losses and damage to reputation due to inability to correctly identify their partners and clients. The paper aims to describe the mechanism which allows decreasing the possible risks of cooperation with partners and clients using reliable data sources.

To achieve the aim, several methods are employed, namely correlation analysis of data to identify errors and inaccuracies and assessing the risks of cooperation using the developed methodology for data assessing. The analysis covers data collected from government databases and cartography and search system.

For evaluating potential customers and partners data from the following services and sources is used:

· The Federal Tax Service;

· The Unified Federal Register of Legally Significant Information;

· Federal Arbitration Court of the Russian Federation;

· Unified Information System in the Sphere of Procurement of the Russian Federation;

· Federal Bailiff Service;

· Google News;

· Yandex News;

· Yandex Maps;

· Federal News Channels.

Let us start by looking at the methodology for assessing the risks of cooperation in more detail. It consists of four stages.

At the first stage, the presence and coincidence of the details of the partner or client in question in state sources are checked.

At the second stage, the partner or client is checked for litigation as a plaintiff / defendant, in open executive proceedings, etc. Their names are checked in the list of unscrupulous suppliers in the register.

At the third stage, the partners or clients relationships history for previous periods is checked.

At the fourth stage, verification of the documents received from the partner or client is carried out to identify the requisites in the government databases.

The final score is formed by linear convolution and looks like the sum of the estimates of all stages and is in the range from 0 to 100 points. Then, the final score is transformed into the risks of cooperation with a partner or client according to the following indicators:

· If the final score is less or equals 40, the level of risk is in the zone where cooperation with the enterprise is possible with little or no change.

Recommended solutions:

Ø It is necessary to check the quality of the evaluation.

Ø Cooperation with a deferred payment is possible.

Ø Cooperation with a large order is possible.

· If the final score ranges between 40 and 65, the risk level is in the zone of increased danger, when without further verification it is impossible to consider cooperation.

Recommended solutions:

Ø Additional verification of documents, data and evaluation is required.

Ø There is a possibility of cooperation on prepayment.

Ø Cooperation on conducting a small transaction with deferred payment is possible.

· If the final score exceeds 65, the level of risk is in the critical zone and prior to further cooperation additional checks and revision of the clauses of the contract are necessary.

Recommended solutions:

Ø It is necessary to review the data obtained.

Ø It is necessary to ascertain the observance of laws.

Ø Only prepayment is possible.

In conclusion, it must be admitted that the proposed methodology is not perfect and requires further research related to the impact of the facts on the overall risk assessment of cooperation. Some facts about partners and customers can become obsolete due to errors at the first steps or mistakes in the development of the company profile, and therefore old data should have less influence on the final assessment of the risk of cooperation. It can also be necessary for the future enhancement of risk assessment system by considering large parameters and including new data sources. Although another problem could arise here: this is increasing uncertainty since the risk of obtaining disinformation increases in proportion to the increasing data.