Investigation of Conflict of Interests
FUNDAMENTALSFEATURED
Fuzzy match functionality of excel can be used to identify any apparent conflict of interest situations
You will find that conflict of interest is omnipresent in all the cases of fraud. In my professional career, I have seen it present in all the fraud cases that I have investigated. Conflict of interest helps the investigator to spot a red flag. This clue helps the investigator perform a deep dive review in that particular area. This becomes very important in investigation otherwise you end up looking for a needle in a haystack.
Conflict of interest between employees and vendors is a common issue in many organizations. A conflict of interest occurs when an individual's personal interests interfere with their professional duties, creating a potential ethical dilemma. In the context of employee-vendor relationships, this can arise when an employee has a personal relationship with a vendor, such as a family member, friend or romantic partner, or has a financial interest in the vendor, such as a personal investment.
Such conflicts can lead to biased decision-making and favoritism towards the vendor, potentially resulting in the selection of inferior products or services. This can negatively impact the organization's reputation and lead to lost opportunities and reduced profits. Furthermore, it can also undermine trust and credibility among employees and stakeholders, damaging the organization's internal morale and culture.
During our investigation process, checking conflict of interest is always a first step of our work plan. This checks were performed as a sanity check. To check conflict of interest between employee and vendor or vendor and customer you need the following data:
Employee master (containing details such as name, address, PAN, personal email ID, contact no., bank account no. etc.)
Vendor master (containing details such as name, address, GST no, email ID, contact no, bank account no. etc.)
Customer master (containing details such as name, address, GST no, email ID, contact no, bank account no. etc.)
Next step is to perform a fuzzy match (using excel) between employee master and vendor master or vendor master and customer master on the fields such as name, address, email ID, contact no or bank account no. etc. The objective of the fuzzy look up is the identify those employees who might have empaneled themselves as a vendor. But, this workstep is not fool proof method to rule out conflict of interest. There can be conflict of interest situations arising out of cosy relationships between vendor and the employee which require circumstantial evidence to prove.
Let me illustrate how to perform fuzzy look up using one use case.
We have employee master on the left hand side and vendor master on the right hand side. Let us perofrm the fuzzy match on the email ID and address as follows:
Before we can perform fuzzy matching, we must first convert each dataset into a table. This can be done by selecting the table range and then press Ctrl+T
Repeat the same steps to convert the second dataset (vendor master) into a table
To perform Fuzzy matching, click the Fuzzy Lookup tab along the top ribbon:
Then click the Fuzzy Lookup icon within this tab to bring up the Fuzzy Lookup panel. Choose Table1 for the Left Table and Table2 for the Right Table. Then highlight Team for Left Columns and Team for Right Columns and click the join icon between the boxes, then click Go:
The results of the fuzzy matching will be shown in the cell you currently have active in Excel (Refer Table 1)
Excel also shows a Similarity score, which represents the similarity between 0 and 1 of the two names that it matched. Feel free to adjust the minimum Similarity score within the Fuzzy Lookup panel to allow for matching between text values that have lower similarity scores.
Table 1 results indicates that Bill Gates (Gates Foundation) and Mukesh Ambani (Jio Platforms) were found common in the employee master and vendor master. You can refer to the similarity score which can be exact match (1) or approximate match (0.8-1). These results can further be used to perform detail document and transaction review of the shortlisted vendors to see any anamolies in the invoices, payments, award of contract etc.