AI Agent reads bureau, banking, business photos and geo-location together — every transaction, every time — and turns them into a decision-ready credit evaluation report in under 3 minutes.
3,000+ transactions, seven different data sources, and fraud signals buried inside narration text. Models see structured fields. Humans see <20% of the file. Nobody sees all of it — until now.
Per applicant, spanning 14–16 months of banking history — impossible to read line by line or hand-engineer into model features.
Bureau, banking, business photos, geo-location, business KYC, web search and applicant-declared data — all structured differently.
Chit funds, BC pass-through accounts, family transfers inflating revenue, location mismatches, informal lender loans.
A well-trained underwriter needs 45–60 minutes per file. At scale, quality degrades and signals get missed.
Data science models are fast but shallow. Human underwriters bring judgment but can't scale. AI Agent does both — at full transaction coverage, every file.
| Signal / Capability | Data Science Model | Human Underwriter | AI Agent |
|---|---|---|---|
| Income cleaning — strips loan disbursals from UPI credits | ✗ Misses | Partial | ✓ Full |
| Bilateral flow flagging — chit fund / informal lender detection | ✗ Misses | ✗ Misses | ✓ Full |
| NACH bounce + balance-negative severity assessment | ✗ Counts only | Counts only | ✓ Full |
| Business name vs shop-photo cross-validation | Partial | ✓ Sees photo | ✓ Full |
| Stock value estimation from shop photos (turnover ratio) | ✗ Misses | Subjective | ✓ Full |
| Cross-source conflict detection (BKYC vs bureau vs banking) | ✗ Misses | Partial | ✓ Full |
| Missing EMI detection — hidden accounts from bureau gaps | ✗ Misses | ✗ Misses | ✓ Full |
| Multi-VPA informal lender pattern (5+ VPAs, same entity) | ✗ Misses | ✗ Misses | ✓ Full |
| Consistent application across 100% of transactions | ✗ Samples | ✗ Samples | ✓ 100% |
| Time to complete per case | ~5 min read | 45–60 min | < 3 min |
AI Agent ingests everything you already collect at login and disbursal, and reconciles it automatically.
Full tradeline history, DPD patterns, inquiry velocity, EMI modelling.
Every credit & debit classified — business income cleaned of loans, transfers, pass-through.
Shop front, interior, selfie — stock value estimation and turnover cross-checks.
Photo geo-tag vs declared address vs map API — mismatch flagged automatically.
Udyam/GST/shop licence cross-checked against declared turnover and category.
Every linked bank account verified by PAN/name — accounts belonging to others are excluded.
Cross-referencing business existence and reputation where available.
Self-reported turnover, tenure and category — checked against everything above.
Drawn from live underwriting cases. Applicant names below are illustrative; the detection logic is real.
The agent counts visible product categories, identifies equipment (refrigerator, display counters, bulk stock), and estimates total shop stock value — then computes a turnover ratio against adjusted banking income. A kirana store showing ₹20–32K of stock with ₹1.23L/month adjusted income implies a plausible 4–6× monthly turnover. The same photo paired with a claimed ₹3L/month income would imply an implausible 15× ratio — an automatic inflate-income flag.
For every active bureau tradeline, the agent searches banking debits for a matching NACH/lender narration. ₹6,800–9,700/month of EMI obligations were unaccounted for on the submitted account — a strong signal of an undisclosed second account. True FOIR moved from a reported 16–24% to a corrected 24–35%, changing the decision from low-risk to review pending a second statement.
One counterparty showed 47 credits and 42 debits over 10 months, each side roughly netting to zero — a classic chit-fund / beesi pattern — followed by a single ₹50,000 one-directional payout. The agent detects the symmetric count pattern, isolates the payout month, and excludes the full ₹2,42,000 from income.
A simple bounce count says "8 RTNs, 32% bounce rate, loan eventually repaid." The agent instead checks the balance after every penalty debit — finding 5 consecutive failures with the account balance driven negative. That's a liquidity crisis, not a minor bounce, and the risk rating is upgraded with an enhanced monitoring condition.
Real customers pay for goods once; they don't top up an overdraft account through five different VPAs with same-day reversals. The agent flags the bilateral pattern (₹6.66L in, ₹4.63L back out), the multi-VPA usage, and the OD top-up destination — reclassifying the entire ₹6.66L as informal borrowing, not business income.
Catching hidden MFI debt, chit-fund credits and informal borrowing reduces income overstatement — the primary driver of first-payment defaults in MSME lending.
Sub-3-minute analysis enables same-day disbursal without sacrificing underwriting quality. Human review shifts from analysis to judgment.
Every file gets the same multi-dimension analysis regardless of analyst experience — no case slips through with only a fraction reviewed.
Two real output reports with applicant identity, contact details and photos fictionalized for this preview. Structure, logic and figures reflect actual AI Agent output.
MSME and microfinance books where field-agent bandwidth caps how many files can be reviewed properly each day.
Digital-first lenders running short-tenure, small-ticket loans that need same-day, API-driven decisions.
Business banking and priority-sector lending teams looking to add depth to existing credit models without replacing them.
Built for regulated lenders. Bureau pulls, bank statements and business photos are sensitive by nature — the architecture reflects that.
Deployed inside your own VPC or on-prem infrastructure. Bureau, banking and photo data are processed there and never routed through or stored on Aspire's servers.
Your data is never used to train, fine-tune or improve any shared, foundation or third-party LLM — not Aspire's, not anyone else's.
Each deployment is logically isolated. No pooling or mixing of data across lenders, and no cross-customer learning of any kind.
Retention and deletion run on your policy and schedule — data is purged from the processing environment when you say so, not automatically retained by us.
Send us a sample bureau pull, bank statement and business photos — we'll return a full evaluation report the same way your team would receive it in production.