AI Credit Assessment
AI credit assessment across any borrower file.
Read the documents. Spread the financials. Catch the inconsistencies. Draft the memo. The lender signs the decision.
Kita is AI credit assessment software for lenders who underwrite thin-file and informal-economy borrowers. We turn fragmented borrower data into decision-ready signals.
Definition
What is AI credit assessment?
AI credit assessment is the structured analysis of a borrower file using vision and language models. It reads every document, normalizes the financial data, reconciles numbers across sources, surfaces fraud signals, and produces a credit assessment with citations. A credit assessment is not a credit decision. The lender owns the decision.
How it works
01
Read every document
Bank statements, tax returns, ID documents, audited financials, payslips, mobile-money exports. Typed, scanned, photographed, handwritten. One pipeline.
02
Reconcile and spread
Normalize three years of statements. Catch restated income, mismatched IDs, doctored balances. Spread the financials into one structured view.
03
Draft a defensible assessment
A credit assessment calibrated to your policy with citations back to the source documents. Your underwriter reviews and signs.
Comparison
AI credit assessment vs. manual file review.
| Aspect | Manual review | Kita AI credit assessment |
|---|---|---|
| Hours per file | Four to twelve hours of analyst work | Minutes from intake to drafted assessment |
| Documents handled | What an analyst remembers to look at | Every document in the file, with citations |
| Financial spreading | Re-typed into Excel by hand | Auto-spread with line-item provenance |
| Fraud signals | Caught when an analyst notices | Detected at the document layer every time |
| Consistency across files | Varies by analyst, varies by day | Same rigor applied to every file |
| Audit trail | Handwritten notes, scattered spreadsheets | Structured output with full document citations |
Who it's for
Built for the three lender scenarios we serve.
Microfinance
Thin-file consumer lending.
Assess borrowers from mobile money, e-wallet records, and informal income evidence. AI credit assessment makes the diligence affordable at micro-loan economics.
SME and trade finance
Multi-year audited financials.
Three years of audited statements, spread and reconciled against tax returns and bank statements. The kind of analysis a senior credit officer does, applied to every file.
CDFI and mission lenders
The loans bureau scores miss.
Bring institutional-grade rigor to character-and-capacity lending. AI credit assessment scales the diligence without giving up the human judgment that defines mission lending.
The product
Kita AI Underwriter
The AI Underwriter is the credit-assessment layer in Kita. It spreads financials, reads the borrower narrative, and drafts a credit assessment calibrated to your policy. The output is decision-ready. The decision is yours.
See the AI UnderwriterFAQ
Common questions
Is AI credit assessment the same as a credit score?
No. A credit score is one number. AI credit assessment is the full analyzed file: spread financials, reconciled documents, fraud signals, and a written narrative with citations. The score is one input. The assessment is what an underwriter actually reads.
Does Kita decide whether to approve the loan?
No. Kita returns a credit assessment. The lender reads it, edits the memo, and signs the decision. Kita is software that prepares the file with the same rigor every time.
How does AI credit assessment work for thin-file borrowers?
For borrowers without traditional credit history, Kita reads alternative data: mobile-money records, e-wallet history, business registrations, tax filings, utility bills, informal income evidence. The assessment is built from what is available, with explicit confidence on each signal.
Can the credit assessment be calibrated to our policy?
Yes. The financial-ratio thresholds, the qualitative narrative, the fraud-tolerance bands, and the assessment template all calibrate to the lender. Your policy is the input.
Which document types are supported?
Bank statements, tax returns (BIR, SAT, IRS), audited financial statements, payslips, government IDs (PhilSys, INE, KTP, Aadhaar), business registrations (SEC, RFC, NIB), mobile money (GCash, M-Pesa, MTN), and 50+ more. Any document and any format.
Is the output explainable for regulators and auditors?
Yes. Every field in the assessment cites the source document and the location within it. An auditor can trace any number back to the page it came from. The narrative explains the reasoning. The human underwriter signs the decision.
How long does it take to deploy?
Most lenders are receiving structured assessments inside two weeks via the API. Full calibration to a lender policy takes four to eight weeks of iteration with the underwriting team.
Every borrower file. Same rigor. Minutes per assessment.
Talk to the team about AI credit assessment calibrated to your policy.