AI for Debt Restructuring: From lender negotiations to IBC filings — a CFO’s field guide

If you’re a CFO dealing with a stressed balance sheet — whether it’s a working capital crunch, a term loan that’s turned NPA, or a restructuring mandate handed down by the board — the work is essentially the same: analyse the situation, model the scenarios, prepare the lender pack, negotiate terms, and document everything for compliance.

Every step of that process is information-dense, repetitive, and time-critical. It’s exactly the kind of work AI handles well.

This article covers five specific workflows we’ve built for debt restructuring — from the initial financial health scan to the documentation an IBC professional needs for the Resolution Plan. These aren’t theoretical. They’re live in engagements we’re running right now.

Why debt restructuring is a good AI use case

Three characteristics make debt restructuring well-suited for AI acceleration:

1. High document volume, structured output requirement. Lender packs typically run 200–500 pages across financial statements, projections, security documents, and legal opinions. AI can read, summarise, cross-reference, and flag inconsistencies faster than any human team.
2. Repetitive modelling with varied assumptions. Scenario modelling — DSCR under three revenue scenarios, leverage ratios with and without haircut, EMI restructuring with extended tenor — is formulaically similar across engagements. Once your model framework is built, AI populates the numbers.
3. Tight timelines with high error cost. Lenders impose response windows. Missing a lender deadline with an incomplete or inconsistent pack is expensive. AI-assisted document review catches errors before submission.

The five AI workflows for debt restructuring

Workflow 1: Financial health scan and triage

Before deciding on a restructuring strategy, you need a rapid assessment of the company’s financial position against standard lender thresholds.

The AI workflow ingests your last 3 years of audited financials (P&L, balance sheet, cash flow) plus the current interim statements, and outputs:

• DSCR (Debt Service Coverage Ratio) — by year and projected forward
• Current ratio and quick ratio trend
• Net working capital position and working capital cycle
• Debt-to-EBITDA by lender tranche
• Security coverage ratio per loan against latest asset valuations
• Classification of each loan facility: standard, SMA-0, SMA-1, SMA-2, or NPA

This takes a finance team 2–3 days to compile manually. The AI workflow does it in 4–6 hours, with a clean output formatted for the opening section of the lender pack.

Workflow 2: Scenario modelling and Resolution Plan drafting

The cornerstone of any restructuring negotiation is the Resolution Plan — a document that tells lenders: here is our baseline, here are the scenarios, here is the recovery we’re projecting under each, and here are the asks (haircut, extended tenor, reduced interest, moratorium).

The AI workflow builds this from your inputs:

1. Base case financials (actuals + management projections)
2. Restructuring request parameters (moratorium months, rate reduction, principal haircut %)
3. Two stress scenarios (conservative and pessimistic on revenue, with working capital implications)
4. Sources and uses of funds post-restructuring

Output: a 15–20 page Resolution Plan draft with populated financial tables, DSCR projections by scenario, and the plain-English narrative that lenders actually read first. This draft then goes to your restructuring advisor or CA for review and refinement.

Workflow 3: Lender communication drafting

Debt restructuring involves constant written communication: initial standstill request letters, banker committee meeting minutes, responses to due diligence queries, and formal correspondence to the lead lender. Each piece needs to be factually consistent with your Resolution Plan and calibrated to the tone each relationship requires.

The AI workflow maintains a “fact base” — a structured summary of your financial position, the restructuring terms, and any commitments made to date. Every communication draft pulls from this fact base, ensuring consistency across 20–30 documents produced over a 3–6 month restructuring process.

This is where AI saves the most hidden time: the time your CFO or advisor spends cross-checking that the number in the letter matches the number in the Resolution Plan that matches the number in the financial model.

Workflow 4: Document checklist and due diligence tracker

Banks submit a due diligence checklist early in the process. It typically runs to 80–120 line items: audited financials, provisional financials, asset valuations, title deeds, insurance policies, board resolutions, promoter declarations, and so on.

The AI workflow converts this checklist into a live tracker, assigns each item to a responsible person in your team, tracks submission status, and auto-composes the transmittal letters for each document batch submitted to the lender.

For engagements with multiple lending institutions (consortium of 3–8 banks is common for mid-size companies), this becomes essential. Each bank may ask for slightly different formats or additional items. The tracker handles this without spreadsheet chaos.

Workflow 5: IBC process support — CIRP documentation

If the restructuring is formal under the Insolvency and Bankruptcy Code, the Interim Resolution Professional (IRP) or Resolution Professional (RP) has specific documentation requirements under the IBBI regulations.

The AI workflow supports:

• Preparation of the Informational Memorandum (IM) that goes to prospective Resolution Applicants
• Claim verification support — cross-referencing creditor claims against your books
• Resolution Plan compliance checklist (Section 30 of IBC)
• Committee of Creditors (CoC) meeting minutes drafting

The three restructuring pathways: how AI differs by route

What AI cannot replace in debt restructuring

Two things require human judgment that AI cannot replicate:

Relationship reading. Which lender is the most risk-averse in the consortium? Who has the most to lose from a formal NPA classification? Who is the lead relationship manager and what is their career risk in this deal? These signals determine negotiation strategy, and they come from relationship intelligence that lives outside any document.

Legal judgment. The intersection of IBC, SARFAESI, and FEMA in a cross-border restructuring involves judgment calls that require a qualified restructuring professional. AI can draft the documents. It cannot advise on whether a particular clause will survive a challenge at NCLT.

The correct framing: AI handles the information processing so your restructuring advisor or CA spends their time on judgment, not document assembly.