The bottleneck in a two-day loan process is usually not the bureau pull or the credit decision. It is waiting for usable bank data. The fintech press has made claims about AI cutting loan turnaround times by 80 percent or bringing underwriting costs close to zero. Some gains are real, but they usually begin with data access. After working with NBFC and fintech lending teams in India for two years, we have seen a clear difference between improvements that remove operational delay and those that only automate a later step.
The practical version is simple: change how financial data enters the process before trying to automate the judgment. Teams that concentrate on the AI layer while leaving document collection unresolved usually make only a limited difference to total turnaround time.
Where Time Goes in a Typical Underwriting Process
For an NBFC personal loan or small business working capital application, underwriting commonly takes two to five working days. The time is not distributed evenly.
Bureau retrieval and identity verification take fifteen minutes to one hour. The range depends on API response times and the manual identity checks still required.
Bank statement collection takes one to three working days. This is normally the largest delay, and it depends almost entirely on the borrower completing the upload. A digitally active borrower may finish in an hour. A borrower who needs follow-up, has lower digital fluency, or submits the wrong documents can extend the process to multiple days.
Statement parsing and normalisation take thirty minutes to four hours. The main variables are whether OCR handles the submitted documents cleanly and whether an analyst must correct or re-enter information from failed extraction.
Analysis and credit note preparation require one to three hours of analyst time. This is substantive work: reviewing income patterns, locating liabilities, checking anomalies, and preparing the note for committee consideration.
Committee review and the lending decision take thirty minutes to two hours, depending on the loan size and the organisation's committee cadence.
If the objective is to reduce a three-day process, the fifteen minutes spent on bureau retrieval is not the main constraint. The two-day wait for statements, followed by analysis preparation, is where the elapsed time sits. Consider an NBFC credit team handling a personal loan application: faster access to the account data changes the schedule more than a marginal improvement to an already short bureau step.
What AA Integration Changes in Practice
When a lender connects to the AA framework as a Financial Information User and creates a consent-based collection flow, bank statement access can move from two days to minutes for borrowers who complete consent. For a lending operation, this is the largest available reduction in total turnaround time.
That is a data infrastructure change, not an AI improvement. The information arrives structured, signed, and queryable instead of as a PDF requiring parsing and normalisation. The gain comes from access to the data. It does not, by itself, improve the analytical judgment applied afterwards.
An AA integration by itself can materially shorten turnaround because it removes the dominant time sink. Conversely, a sophisticated analysis layer placed on top of PDF collection addresses a secondary constraint while the primary delay remains. The result can be useful, but the total process will still wait on the borrower and the document workflow.
What AI Adds After Data Access Is Fixed
Once structured financial data is available quickly through AA, the analyst's constraint moves from collecting and preparing information to interpreting it. This is where query assistance has practical value. It does not replace the analyst's judgment. It reduces the time spent on repeatable parts of the review.
That routine work includes calculating average monthly inflow, locating salary credits and their timing, finding recurring NACH debits and amounts, detecting NACH return events, calculating end-of-month balance patterns, and extracting the debit-to-income ratio. These are deterministic operations on transaction data. On a standard PDF statement, an experienced analyst may spend fifteen to forty minutes completing them manually.
A query layer can return those calculations promptly, with the relevant transactions. The analyst can then focus on irregular patterns, narration context, incomplete or ambiguous information, and the recommendation itself. Reducing repetitive calculation can improve review quality as well as speed because the analyst is less burdened by mechanical work.
The useful division is therefore straightforward: the system performs deterministic work, while the analyst performs judgment work. We are not saying that AI should decide credit without review. We are saying that routine evidence preparation can be separated from the parts of underwriting that require context and accountability.
Where the Efficiency Gain Stops
Removing human review from credit decisions based on bank statement data can create compliance and risk issues that exceed the time saved, especially in lending categories where the decision requires context. Audit trail requirements, fair practices obligations, and the complexity of many cases all argue against making analyst removal the objective for most NBFC and fintech lending operations.
A generic model trained on public financial data, or on another lender's portfolio, is not automatically suitable for a particular product, borrower segment, or credit policy. A model that works well for a large NBFC's salaried personal loan book may not have comparable predictive validity for a small business working capital product in another geography. Performance does not reliably transfer between products and populations. Finding that out after deployment can create correction costs larger than the efficiency gained during the period of excessive confidence.
Automating the credit note while leaving its evidence inaccessible also creates a transparency gap. If a system produces a structured summary but the supporting transactions cannot be retrieved, the credit committee and regulatory functions cannot verify it. A quicker output that cannot be checked is not a better process than a slower output with verifiable evidence.
A Practical Sequence for Deployment
For a lending team moving from a PDF workflow, an achievable efficiency sequence is as follows.
Start with data access. Implement AA integration and create a consent flow through which borrowers can share bank data directly. Track consent completion for the relevant borrower segment and address the points where applicants drop out. The objective is to have a meaningful portion of applications with structured bank data available within thirty minutes of application.
Next, create the query layer. Define the calculations analysts perform on every application, including income assessment, EMI burden calculation, NACH return detection, and balance pattern analysis. Implement these as structured queries over AA data. Return the same evidence an analyst previously calculated manually, in a consistent format with the supporting transactions.
Then add credit note generation. Once query results are structured and consistent, a template-based note populated from those results can reduce routine analyst effort. For straightforward applications, the analyst reviews and annotates the generated note instead of writing it from the beginning.
Finally, measure outcomes before widening automation. Run the new process alongside the existing one, track decision quality as well as speed, and calibrate the scope to the product and borrower segment before extending it.
This order is less appealing than promising an 80 percent turnaround reduction through AI. It does, however, reflect the operating reality: remove the collection delay, make the evidence queryable, then automate repeatable analysis while keeping review accountable.