While evaluating loan proposals, lenders often face an overload of data from multiple sources on the borrower’s business, financial data and performance, credit rating, products and services, GST, EPF, litigation, information on group companies, etc.

Analysing such large chunks of data and ensuring that all the information needed for effective evaluation of the loan proposal has been extracted from the data is a mammoth task.

Lenders are generally under pressure to disburse loans within a short timeframe. With limited time on hand and the necessity of processing enormous amounts of data, there is a risk of critical information points relating to the borrower’s business and finances being overlooked or missed.

Consumability of data is a huge challenge if the lender does not have the right tools/models to break the information down and analyse it.

Inability to effectively use the data on hand leads to confusion and prolongs the decision-making process, impacting evaluation of the loan proposal.

Analysts spend a lot of time on data collection due to lack of a one-stop shop for data, leaving them with little time for data analysis.

While conducting a forensic analysis of a company, firms are often overwhelmed by the amount of data they receive on parameters such as financial performance, defaults, litigation, non-payment of taxes and fraud.

Extracting all the critical information necessary for a successful forensic audit from such a mountain of data is a considerable task.

The key to efficient forensic analysis is uncovering all complex relationships and structures to get a complete picture of the corporate web. Because of huge amounts of data, forensic firms find it difficult to establish all the relevant relationships.

Data is scattered in the public domain. Gathering it is time-consuming and challenging. There is always a risk that important information would be missed or overlooked.

The lack of a one-stop shop for data results in more time being spent on data gathering rather than on data analysis.

While evaluating vendors or customers, corporates are often faced with the difficult task of going through large chunks of data to assess key parameters such as financial stability, debt levels, and GST compliance.

Ensuring that no important or critical data point is overlooked while doing a complete background check is a challenge.

Authenticity of data received from the entity being assessed is a challenge due to worries over tampering and trust-related issues.

Completeness of the data received from the potential vendor/customer whose background check is being carried out is also often a concern. It is difficult to ascertain whether the vendor/customer has concealed any negative information such as critical litigation filed against them, or non-compliance with GST.

The data needed for effective background checks is scattered in the public domain across multiple sources. Gathering it is not only time-consuming and challenging but also raises the risk of critical data being missed.

The possibility of cost-overruns is high due to lack of a single window for obtaining data.