Who Is Litigating Cheque Bounce Cases?

Cheque Bounce Cases

Cheque Bounce Cases

Under Section 138 of the Negotiable Instruments Act are an important source of case load at the Indian judiciary. This has inspired many attempts at modifying laws and court procedures so as to reduce the burden. In this journey, empirical evidence about the nature of the litigants is required. In this article, we establish a dataset about these matters, and measure the shares of financial firms, non-financial firms and individuals.

We find that in Mumbai, financial firms filed 52% of cases, and that 83% of cases were against individuals. Cases filed by financial firms are likely to be disposed quicker than those filed by individuals. We explore how the cheque is used as a means of credit, and why financial firms accept them as collateral / security.

It appears that financial firms are using cheques and Section 138 as a coping mechanism for poor civil remedies. While there is a need for legal system reform in the context of S.138 of the N.I. Act, it would also be useful to find solutions in banking regulation and personal bankruptcy law.

We conclude with a recommendation of caution. Just as the amendment in 1988 has led to certain behaviours and industry practices, new solutions will alter the equilibrium, creating new incentives and new behaviours. The patterns seen in Mumbai are not present in regions of lower economic activity like Jhabua-Nimar. We need to be aware of the wide differences across different districts and states of India, and be mindful of complexity, as we proceed on the path to legal system reform.


Section 138 of the Negotiable Instruments Act, which was introduced in 1988, creates the possibility of imprisonment for upto two years, a fine upto twice the amount of the cheque, or both, in response to cheque bouncing. The Act prescribes a six month time horizon for disposing these cases. This 1988 amendment is widely used as an example of the need for judicial impact assessment: The legislative action substantially enhanced the load upon the judicial branch, but there was a lack of commensurate operational planning and resourcing to deal with the enhanced case load.

What fraction of the pending cases or the flow of new cases emanates from this?

A precise answer to this is not feasible under the present state of legal system data in India, but it is likely to be about 15 per cent (Chapter 3, Law Commission of India, 2014 [1] ; Supreme Court in Makwana Mangaldas Tulsidas vs The State Of Gujarat, 2018 [2] ; Mahadik D, 2018 [3] ). An important paper in this literature, Damle and Gulati, 2022 [4] examines 363,720 cases across 8 States and 2 Union Territories and estimates that cheque dishonour cases represent 13.2% of the courts’ workload and take 395 days for disposal.

One pathway to legal system reform lies in an 80:20 analysis, in a vertical approach of finding solutions that are specific to certain classes of matters. Many thinkers have proposed making progress on S.138 of the N.I. Act as a component of legal system reform (Law Commission of India, 2008 [5] ; Law Commission of India, 2009 [6] ). Alongside this is the proposal for decriminalisation of cheque bouncing, broadly drawing on the concept that debtors prisons are not how modern economies operate. All these discussions require more knowledge about the nature of litigants in these matters, which is presently lacking.

This article seeks to fill this gap. In their paper, Damle and Gulati, 2022 [4] establish that the impact of Section 138 cases on caseload, pendency and time to disposal varies by State. We ask the questions: Who are the litigants in Section 138 cases? Does the nature of cases vary based on who the participants are? Do these characteristics vary based on location?


The e-courts database for district courts was used to build a dataset about pending and disposed cases relating to Section 138 of the Negotiable Instruments Act. This was done for India’s most advanced region (Bombay). For a comparison, this was also done for the group of districts (termed “homogeneous region” by CMIE) with the highest share of households in agriculture. This is the “Jhabua-Nimar” homogeneous region, which comprises six districts in Madhya Pradesh – Alirajpur, Barwani, Burhanpur, Dhar, East Nimar (Khandwa), Jhabua, West Nimar (Khargone). These two datasets thus show the full range from the old India to the new India.

Litigants were classified into three groups:

  • Financial Firms
  • Non-Financial Firms
  • Individuals

This was done through a process of looking for keywords in the name:

  1. Financial Firms typically have the terms bank, finance, invest, loan, and related keywords and variations.
  2. Non Financial Firms have terms like ltd, pvt, corporation.
  3. Non Financial Firms may contain common nouns from the English Language.
  4. Litigants with the term proprietor in the name were categorised as individuals.
  5. Those that did not fit these criteria were categorised as individuals.

This classification heuristic requires a standard corpus of English words. We used the NLTK Wordnet corpus and identified all words in the names of litigants that overlapped. A manual cleanup was required as the corpus contained some proper nouns. We assessed the words which made up 95% of the instances of overlap with the corpus and eliminated names and common nouns that could be Indian names (“Rout”, “Harsh”, “Baby”, etc.). In Mumbai, we found 8133 unique words appearing 763,593 times. The 95% filter resulted in 1,165 unique words in Mumbai. For Jhabua-Nimar, we found 1,006 unique words appearing 19,974 times. The 95% filter resulted in 345 unique words.

These heuristics will of course engage in a small rate of misclassification. Some names like Banku and Chitra containing the terms Bank and Chit could be classified incorrectly. We do not account for firms that have common nouns in their name from languages other than English. In many cases, an individual proprietorship may have the term company or finance in their name. The methodology does not take into account spelling errors.

In order to assess the accuracy of the work, it is important to estimate the defect rates associated with these heuristics. We manually analysed a random sample of 100 cases (and 200 litigants) in each district, in order to measure the error rate. We found two errors in our Mumbai analysis. They are:

  1. Ms M. D. Vora Co. is a non-financial firm categorized as an individual.
  2. Alexander Xavier Dsouza is an individual categorized as a non-financial firm. Alexander is present in the wordnet corpus, and appears 16 time in the dataset which puts it in the bottom 3% of words by frequency, which is why it was excluded in the manual cleanup.


Similarly, we found seven errors in our Jhabua-Nimar analysis. They are:

Two cases where non-financial firms with names in Hindi were misclassified as individuals:

  1. Shri Krishna Prajapati Sikh Askari Sanstha Mayardit.
  2. Subbalakshmi Sakho Shakira Sasha Maya. Hamond By Nitesh Bhawar.

Two cases where financial firms with typos were misclassified as individuals:


 One case of an individual misclassified as a financial firm:

  1. Kashish Finance H.U.F Propriter Vijay Rathore.

Two cases where the State was a party. The State was misclassified as a non-financial firm.

This suggests a defect rate of 1% for Mumbai and 3.5% for Jhabua-Nimar. This gives us a sense of the extent to which the estimates presented ahead should be treated with ..


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