FinAGG/ Products/ FAME Score
01 · SCORING · v 4.2 · GA

A credit score for borrowers bureaus can't see.

FAME Score is a cash-flow-first underwriting score for thin-file Indian borrowers — built on 11 million Account Aggregator + GSTN + bank-statement profiles, calibrated against 36 months of repayment outcomes.

● GA build 4.2.0
Coverage28 states · 1,142 PIN codes
Training data11.4 M profiles
Score range300 – 900
BandsA+ · A · B+ · B · C · D
Latency · p99820 ms
Refreshreal-time on event
THE OUTPUT
§ 01

A score, six drivers, one decision.

Every FAME Score returns the band, the bureau-equivalent, six explainable drivers and a recommended action — wired into your LOS in a single call.

SUBJECT · 28FXXPS9712E1ZW
742 / 900
BAND B+ · PASS · WC eligible
vs CIBIL
786
Δ −44
CASH-FLOW A
GST DISCIPLINE A+
BUREAU HEALTH B+
VINTAGE B
VOLATILITY B−
FRAUD SIGNAL CLEAN
// 01

Calibrated to outcomes, not signals.

Trained against actual 36-month repayment, recovery and write-off events from partner lenders — not survey panels or synthetic data.

// 02

Thin-file native.

FAME Score is most useful exactly where bureaus aren't — first-time borrowers, sole-proprietor MSMEs, new GSTINs, kirana digitisation.

// 03

Explainable, line-by-line.

Every band carries six drivers and the source rows that moved them. Your credit officer can defend any score to an auditor.

// 04

Refreshes on event, not on cron.

A GST default, a bounce, a new enquiry — the score updates the moment it happens, and the alert lands in your queue.

// 05

Recommended action included.

Returns a policy-aware suggestion — approve / refer / decline, with amount and tenor bounds tuned to your underwriting matrix.

// 06

Co-existence with bureaus.

Designed to layer on CIBIL / Experian / CRIF — most customers run FAME + bureau in tandem with a policy gate on either.

HOW IT WORKS
§ 02

Consent in, score out.

Four agents, one request. Average wall-clock: 4.1 seconds.

STEP 01

Consent

Borrower approves AA + GSTN consent in a single flow. PAN, GSTIN and mobile keyed in.

~ 90 s · borrower-side
STEP 02

Ingest

11 bank accounts, 24m of GSTR-1/3B and 3-bureau CIC pulled, deduplicated, normalised.

~ 2.1 s · server
STEP 03

Reason

FAME v4 ranks 412 features, scores six drivers, produces the band and a citation set.

~ 820 ms · model
STEP 04

Decide

Policy engine returns approve/refer/decline + amount, tenor and rate bounds.

~ 60 ms · policy
API
§ 03

One endpoint.

REST, webhook or LOS plugin. SDKs for Python, Node, Java.

POST · /v4/score · curl
200 OK · 812ms
› curl https://api.finagg.ai/v4/score \
-H "Authorization: Bearer fa_live_•••••" \
-d '{
"gstin": "28FXXPS9712E1ZW",
"consent": "ACT-7F2C-9B",
"policy_id": "wc-sme-v17"
}'
───────────────────────────────────
{
"score": 742,
"band": "B+",
"decision": "APPROVE",
"drivers": {
"cash_flow": 0.82,
"gst_discipline": 0.88,
"bureau": 0.74,
"vintage": 0.58,
"volatility": 0.46,
"fraud": 0.01
},
"amount_max": 3800000,
"tenor_max_m": 24,
"citations": ["bs#4", "gst#3b-22", "cic#tl-09"]
}
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