Automated Borrower Litigation Checks for Lenders

Lending · Risk & Compliance

Updated for 2026 · Claw Editorial

Before a bank or NBFC disburses a loan, someone has to answer a simple question: is this borrower, or the guarantor, currently facing a court case that could affect their ability to repay? Answered manually, that question can take a credit or risk officer days of searching individual court websites, and it still leaves gaps. This page explains why that check matters, why the manual version is slow and unreliable at scale, and what a borrower litigation check API actually needs to do well for a lending workflow.

01Why lenders check a borrower for pending litigation

A borrower litigation check means searching court records for any case, civil or criminal, that names the applicant, a co-applicant, a guarantor, or the promoters and directors of a borrowing entity. Lenders run this check for a plain business reason: pending litigation is a credit risk signal.

An applicant fighting a cheque-bounce case, a property dispute, or a recovery suit from another lender is a different risk than one with a clean record, even if their income and credit score look identical on paper. For secured lending, an undisclosed property dispute can undermine the collateral itself. For business loans, litigation against the company or its directors, including insolvency proceedings, can point to stress that has not yet shown up in financial statements. This is why litigation screening sits alongside credit bureau checks and KYC as a standard part of underwriting at most banks and NBFCs, not an optional extra.

The check is not only about the loan going bad. Lenders also have their own compliance obligations, including keeping a documented, auditable trail of the due diligence performed on each borrower. A litigation check that lives only in an underwriter’s memory, or a screenshot from a court website, does not hold up well when a regulator or auditor asks how a lending decision was made.

02Why the manual version breaks down at scale

Doing a litigation check by hand means someone has to search a borrower’s name, one court at a time, across whichever court websites are relevant, then read through the results to work out which cases, if any, actually belong to this person and not someone who happens to share a name. In India that is a genuinely hard problem, for reasons that are structural, not a matter of effort.

Courts are not one system

There is no single national portal that returns every case against a person across every court. High Court and district court data lives on separate systems, tribunal data lives elsewhere again, and coverage and search quality vary a lot by state. A single borrower check can mean visiting several different portals.

Common names create false positives, and false negatives

India has a small number of very common names shared by millions of people. A plain name search returns pages of unrelated cases to sift through, and small spelling or transliteration differences (a middle name included or dropped, a different romanisation of the same name) can just as easily cause a real case to be missed entirely. Getting this wrong in either direction is costly: too many false positives slow down every application, and a missed case defeats the point of checking at all.

It does not scale with loan volume

A retail lender approving hundreds or thousands of applications a day cannot put a manual, multi-portal search in front of every one of them without either slowing down turnaround time badly or skipping the check for smaller-ticket loans, which is exactly where risk quietly accumulates.

The hard part of a borrower litigation check is not reading a court case. It is being confident that the case belongs to this borrower, and not to someone who shares their name.

03What a litigation check API actually does

A borrower litigation check API lets a lender’s loan origination system (LOS) or underwriting engine send a borrower’s identifying details, typically name, and sometimes PAN or address, and receive back a structured result: whether any matching litigation was found, and enough detail (court, case type, status) for a credit officer to assess it, instead of a scanned document or a manual lookup.

Used well, this turns a task that used to sit outside the loan application, done separately by a risk team, into a step inside the application itself, alongside the credit bureau pull and the KYC check, returning a result in seconds rather than days.

This is different from ongoing case tracking, which follows a case a lender already knows about through hearings and orders over time. A litigation check API is about discovery: finding out, at the point of lending, whether litigation exists at all.

04What to look for in the API

Not every litigation data provider is built the same way, and for a lending use case, four things matter most.

• Coverage: which courts the data actually comes from, and how current it is. A tool that covers only a few states or only certain court levels will miss cases for borrowers outside that footprint.
• Name matching that handles Indian names well: the API should tolerate spelling variations and near-matches (proximity and phonetic matching) rather than requiring an exact string match, since that is where both false positives and missed cases come from.
• Verified, citable results: for a decision that may need to be justified to an auditor or regulator later, the result should point back to a real court record, not an unverified summary.
• Integration effort and turnaround time: a REST API that returns a structured, machine-readable response fits into an LOS far more easily than a service that only produces a PDF report for manual review.

One more point worth checking directly with any provider: how the underlying data is sourced and kept current, since a litigation database is only as useful as how recently it was updated.

05Manual search vs an API, side by side

06How it fits into the lending workflow

In practice, an automated litigation check sits as one step in a longer intake process, usually alongside KYC and the credit bureau pull.

1. Applicant details are captured at the start of the loan application, with the applicant’s consent for the checks that will be run.
2. The lending system calls the litigation check API with the applicant’s name and available identifiers, the same moment it runs KYC and bureau checks.
3. A structured result comes back, flagging whether litigation was found and giving enough case detail for a human reviewer to assess relevance, rather than a raw document dump.
4. The credit or risk team applies its own policy to the result. Not every case is disqualifying; a minor consumer dispute is not the same as an active recovery suit or insolvency proceeding, so this step still needs human judgement.
5. The check and its result are stored with the loan file, so there is a record of what due diligence was performed if the file is reviewed later.

For a fuller picture of what belongs in that due diligence process beyond litigation alone, see legal due diligence before lending for banks and NBFCs and, for finding every case tied to a specific person by hand as a fallback or spot check, how to find all cases against a person in India.

07Where Claw fits

Claw is an all-in-one legaltech platform for Indian advocates, law firms, and corporate legal teams, combining AI-based case search, an AI legal assistant (Legal GPT), case management, and compliance automation across all Indian courts and tribunals.

Claw has a public/partner API for programmatic access to court and litigation data, which lenders can use to screen a borrower’s name against Claw’s case search database of 30 crore judgements, covering 25 High Courts (1980 to 2026) and the Supreme Court (1950 to 2026). The search is built to be name-tolerant, using proximity and phonetic matching so that minor spelling variations do not cause a case to be missed, and results come back with verified, court-ready citations rather than an unverified summary, typically in under 5 seconds. That combination, structured API access plus citation-grade results, is what makes it usable as a step inside a loan origination system rather than a separate manual task.

Claw’s case search covers the Supreme Court and High Courts; it does not claim to search district courts or tribunals directly. For litigation that a lender already knows about and wants to follow through hearings and orders over time, Claw’s case tracking, separately, covers 8200 or more courts including tribunals and district courts. Lenders that specifically want to compare litigation-check API providers head to head, including Perfios, which offers a Litigation BI product covering thousands of courts and tribunals for background and litigation screening, should see the dedicated Perfios litigation search API alternatives comparison.

08Sources and further reading

References used for this page:

• Perfios Litigation BI (litigation and background screening product): perfios.ai
• Claw: clawlaw.in
• Reserve Bank of India (lending and due diligence guidance, general reference): rbi.org.in

This is not an exhaustive list of litigation data providers. Confirm current coverage, API terms, and pricing directly with any vendor before integrating.

09Frequently asked questions