How to Reduce Loan Processing TAT Without Hiring More Staff

Loan turnaround time is one of the most competitive metrics in retail lending. Borrowers increasingly compare processing speeds across lenders before submitting applications. An NBFC that can deliver a decision in 4 hours competes differently than one that takes 3 days — even if the interest rates are similar.

Most lending ops teams that want to reduce TAT focus on the credit assessment process. But for most NBFCs, the majority of TAT is not in underwriting — it is in document processing. Getting documents from the applicant, reading them, entering the data, and making it available to the underwriter is where the most time is lost.

Where TAT Actually Goes in the Loan Process

A typical retail loan application at an NBFC involves the following documents:

• KYC documents — Aadhaar, PAN, passport or voter ID
• Income proof — salary slips (3 months) or ITR (2 years)
• Bank statements (6 months)
• Property documents (for secured loans)
• Employment proof — offer letter, employment certificate

For a salaried applicant, this is 10 to 15 documents. For a self-employed applicant or a business loan, it can be 20 or more.

Each document needs to be received, verified as the right document, read, and its relevant fields entered into the loan origination system before the underwriter can begin assessment. At an NBFC processing 100 applications per week, this document entry step alone accounts for 100 to 150 hours of team time per week — before a single credit decision is made.

The Three Document Processing Bottlenecks

1. Document collection delays

Applicants submit incomplete document sets. Missing documents are identified only after manual review, which happens hours or days after submission. Each missing document request adds a round-trip cycle to the TAT.

Automated document classification solves this. When an applicant uploads documents, the system identifies what has been submitted and what is missing immediately. A real-time checklist tells the applicant what is still needed before any human review begins.

2. Manual data entry time

Once documents are received, each one needs to be read and entered. A bank statement with 6 months of transactions, income and expense patterns identified, and EMI obligations flagged — that is 20 to 40 minutes of work per document, per analyst.

Automated extraction eliminates this entirely. Bank statement data, salary figures, ITR income, KYC details, and property document fields are extracted and pushed to the LOS without manual entry.

3. Queue-based processing

In most lending ops teams, documents wait in a queue for an available analyst. If the team is processing 30 applications and each takes 2 hours of document work, new applications submitted in the morning may not be processed until the evening or the following day.

Automated extraction removes the queue. Documents submitted at any time are processed immediately — the LOS is updated within minutes of document upload, not hours.

What a Realistic TAT Reduction Looks Like

For an NBFC that currently averages 3 days from application to decision:

• Day 1 — currently: application received, documents collected manually, initial review begins. With automation: application received, documents classified and data extracted within 30 minutes, LOS pre-populated, underwriter queue updated.
• Day 2 — currently: document entry completed, underwriter begins assessment. With automation: underwriter assessment already in progress since Day 1 afternoon.
• Day 3 — currently: decision reached. With automation: decision reached on Day 1 or Day 2.

A 3-day TAT reducing to same-day or next-day is not unusual when document processing is the primary bottleneck and automation removes it.

Where to Start

The highest-impact first step for most NBFCs is bank statement extraction. Bank statements are the longest documents in the loan file, take the most time to process manually, and contain the income and obligation signals most critical to the credit decision. Automating bank statement processing first produces the largest single TAT reduction before any other document type is addressed.