Why automate TDS Receivable Reconciliation?

TDS Reconciliation


What is TDS?

TDS or “Tax Deducted at Source” is a form of withholding tax introduced to collect tax from the very source of income in India. 

A person or company who has to make payment for a specified service/product to any other person or company has to deduct tax during payment itself based on the rates defined by the government. He also has to remit the same to the government against the vendor/partner.

So the entity who makes the payment is the deductor and the entity who receives the payment is the deductee.

What is TDS Payable?

TDS payable is the tax deducted at source that the deductor (person/company who is making the payment) has to pay to the government on behalf of the deductee.

What is TDS Receivable?

TDS receivable is the tax deducted at source that the deductee (person/company who is offering the service or product) has to ensure he has received from his customers based on the invoices that he has raised against his customers.

How do you do TDS Receivables reconciliation?

TDS Receivables reconciliation is the process of validating the tax paid by a person/company’s customers to the government against the book of accounts maintained internally by them and sometimes with the customers’ statement of accounts. Moreover the tax paid is available in the Form 26AS report available in the Income Tax website that can be downloaded.

Furthermore the datasets corresponding to invoices, credit notes, receipts, settlement of receipts against invoices, customer information and ledgers corresponding to TDS receivables. 

When all the above datasets are in harmony with each other, the reconciliation is considered to be complete. When the records do not match 100%, and the cause for the unreconciled records are known and validated, corrective actions are taken in the book of accounts.

Table 1: Data Required for TDS Receivable Reconciliation

Dataset Name Dataset Description General Update Frequency
Sales & Returns Register Invoices and credit/debit notes done by the company over a period of time. Daily
Receipts Records which acknowledge that a sum of money has been received against a specific sale Daily
Settlement of Receipts / advances against Invoice Report Records which link the credits in receivable ledger to that of the invoices/debit or credit notes. Daily
TDS Ledger Records which specify the TDS portion for every transaction in the sales and returns register and also for advance receipts. Daily
Customer Master Data on customers. Eg: Customer ID, TAN ID, Legal Entity, TAN Mapping to Customer ID. Whenever a customer is created.
Form 26AS Statements that provide details of any amount deducted as TDS or TCS from various sources of income of a taxpayer. Monthly

Common Issues that hinder the reconciliation:

For the TDS receivable reconciliation to be successful, many of the below issues need to be addressed. This ensures that the reconciliation can be done faster and in a more accurate manner reducing the financial risk involved with taxation.

1. The 4 V’s of data:

a. Volume: In enterprises or in today’s Digital economy with sheer volume of transactions, with respect to the sales and receipts can be a nightmare for reconciliation.

b. Velocity: In Retail transactions, part of the sales register comes from offline and part of it comes from online. Further with receipts with so many options available with respect to payment gateways, financing, part payments, subscriptions etc. the data comes from multiple sources and at varying times. Collating all of it and making sense takes its own effort and doing it manually leads to a huge number of issues. With huge volume, the complexity is compounded.

c. Variety: Furthermore just like the above stated example for retail, data comes from different sources. In addition internally there can be multiple applications maintaining a Sales register because of geography, multiple entities etc. Form 26AS is the government’s document which mostly will be in PDF Format. Same goes with TDS ledger and Receipts. Consolidating them into a single standard format is tedious. Again while consolidating, we may miss a set of documents which will affect the veracity of the data.

d. Veracity: Veracity indicates truthfulness of the data.
For example: Let’s say we’ve filed an invoice of Rs.100000 and TDS of 10% for a customer. In an ideal scenario, he should’ve paid Rs.90000 for us and Rs.10000 as TDS to the government .But he might have paid us Rs.80000 only and paid TDS accordingly, taking into account some discount or disallowance which is not captured properly in internal systems or not accounted properly.
There are many other such scenarios when the veracity of the data is susceptible and causes huge reconciliation issues.

2. No uniform structure for comparison

Moreover on top of the data issues mentioned above, another big issue is to ensure we are comparing apples to apples and not apples to oranges. So we need to ensure that the data on both sides of the equation are in the same format and in the same structure to even start the reconciliation. 

A simple example would be the data formats across the datasets. The date format may be dd-mm-yyyy in the Sales register and yyyy-mm-dd in Form 26AS, so the challenge lies in bringing them all together under one umbrella.

3. TAN ID vs Customer Account ID

Then, in our recent experiences, we have seen cases where a single TAN ID is matched against either a single or multiple customer ids (in case of multiple locations) which is valid but the reverse where a single customer id is mapped to multiple TAN’s which happens as a result of mergers or acquisitions causes problems. 

In simple terms, let us consider TAN ids A and B, a customer account 12345, if 12345 is mapped against both A and B, it is difficult to reconcile because the customer may have paid to either TAN A or B, imagine the scenario with huge volume of transactions.

4. Rule Engine Changes due to Government’s new guidelines:

During the first wave of Covid (May 2020), the government had reduced the TDS rates by 25 percent to improve liquidity. Here the customer who is supposed to pay 10% TDS in case of section 194A, would have paid 7.5% due to the government’s intervention. So reconciling for that financial year and subsequent years, required another subset of guidelines. 

5. Customer Statement of accounts on TDS not available

Finally, another crucial aspect to be considered while reconciling, is that a customer may have hundreds of transactions in the Sales Register against him, but in Form 26AS, there might not be a one to one mapping against him, as a result of the customer paying in batches. 

Also Form 26AS does not have the invoice references which can map it back to the Sales Register/Receipts. So it becomes manually impossible to match them since it involves finding the right set of combinations from the sales register against that of Form 26AS .

What is the way forward then? Can Automation help?

All of the above challenges require data engineering after acquisition and there is a complexity involved as well. When we do this manually, processing the data with great accuracy takes a tremendous hit.

Automation is the way to go and it can not only improve your efficiency from a resourcing standpoint but also from a reconciliation success standpoint and reducing your financial risk with respect to taxation.

An application that helps you with automation of this analysis should have powerful data acquisition and engineering capabilities, highly configurable rules engine and adjust easily with low coding effort.

DataTwin Automated TDS Receivable Reconciliation

We built the DataTwin TDS Receivable Reconciliation product with all of the above in mind. DataTwin Platform offers a robust platform on which features below have been built.

Data Acquisition Module

  1. 1. Map your data mentioned above against the DataTwin standard structures using a simple screen.
  2. 2. Load your Comma/Tab separated values or excel files for sales, returns, Form 26AS, receipts etc., manually or via SFTP.
  3. 3. API to pull in the above mentioned data from other applications like ERP, billing systems, payment gateways etc. in real-time.

Data Engineering Module

  1. 1. Transformation of Data to a unified structure across the datasets to ensure a proper reconciliation.
  2. 2. Before applying AI/ML on transactions, the datasets are connected logically based on link references like customer ID, invoice number, date range etc. and classified as Reconciled / Unreconciled.
  3. 3. After application of AI/ML, algorithms transactions are grouped together based on recommendations.
  4. 4. TAN vs customer ID mapping, based on AI/ML algorithms, wherever the mapping is not clear or there are multiple TAN to a customer ID are resolved.

Actionable Analytics

  1. 1. Listing all exception identified by system for a manual review
  2. 2. Options / Tools to be available to review unreconciled items from a different dimension
  3. 3. System to force recommendations using special algorithms to resolve complicated cases
  4. 4. Options to delink the connected records just to dissolve grouping and to regroup with different data sets.
  5. 5. Review of Customer ledgers which are not mapped to TAN.
  6. 6. Review of Customer ledgers which are not mapped to TAN.
  7. 7. Review of Customer Name with Name of the deductor as per Form 26AS.
  8. 8. Analysis that consider Merger & Acquisition of Companies and its associated reconciliation.
  9. 9. Analysis of Customer Name changes in ERP system resulting change in mapping of Customer Ledger with TAN.

With this as our arsenal DataTwin TDS Receivable reconciliation helps in

1. Unearthing the truth:

  1. a. Identify TDS amount which are missed by their customers to file their return
  2. b. Maintain Books of Accounts by recommending True up or True down entries in TDS Accounting
  3. c. Reconcile and match at micro / invoice level so as to enable us to have a preview at Macro / TAN level
  4. d. Composite reconciliation of Receivables, Revenue, Collections, TDS Accounted & Form 26 AS Data

2. Efficiency Improvement:

  1. a. Using Data Engineering and other robust latest technologies, we increased the efficiency of reconciliation from a mere 42% to more than 90%.
  2. b. Turn around time to end customer is quick and accurate which increases efficiency in the whole ecosystem

3. Increases profits:

  1. a. From the time of collection of data to the delivery of the reconciled data sheet we took only 3-4 days and if all required data is given we can compile it in 3-4 hours.. Like the saying, “Time is Money”, we increase your profit by saving your time.
  2. b. Targeting zero unreconciled entries will result in less provisions for doubtful debts and minimize bad debts thereby indirectly maintaining good health in P&L

4. Enables continuous growth:

  1. a. Platform facilitates to focus more on Business processes & Operations than on Data Management/its associated issues, this ensures efficiency to focus more on growth
  2. b. No Backlogs as it was timely reconciled and resolved hence it reduces friction between parties who are on a fast growing track.
  3.  during payment.

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