Signzy Lending
The statement was a PDF. Anyone could have edited it — Signzy Lending pulls bank data through India's Account Aggregator framework — structured, verifiable, and carrying a consent record instead of arriving as an emailed PDF.
Buy through TechBag
Same software. Better outcome — at a lower cost.
How it’s rated
Full scoreboard ↓Quick answer
The Signzy API family
This page covers Lending — Account Aggregator and analysis. The rest of the marketplace:
Quick facts
30-second orientation- Product
- Lending — Account Aggregator and analysis
- Inside it
- AA APIs, risk assessment, bank statement analysis
- The shift
- Consent-based data, not emailed PDFs
- Why it matters
- Provenance is part of a defensible decision
- Honest scope
- It supplies inputs; your credit policy decides
- Model risk
- Explainability and fair lending stay yours
- Pricing
- Quote-only — typically per data pull
- In India via
- TechBag — INR/GST, scoping and support
Understand alternative underwriting before you buy it
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What is Signzy Lending?
Consent-based bank data and the signals derived from it — income regularity, obligations and cash-flow shape, with a consent artefact recording what was shared and why.
An emailed PDF vs a consented pull — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | PDFs emailed by the borrower | Lending (Signzy) |
|---|---|---|
| Income proof | PDFs emailed by the borrower | AA data, direct from the bank |
| Provenance | Unknown and uneditable | A consent artefact with the pull |
| Parsing | Whatever format arrives | Structured on retrieval |
| Fraud context | A separate system | Onboarding signals on the same contract |
| Audit answer | Reconstructed under pressure | Recorded when it happened |
| What it is NOT | — | Not your credit policy, and not your model risk |
Account Aggregator is a PUBLIC framework: everyone retrieves the same data, so the analysis is what you are buying. Backtest it. And model risk, explainability and fair-lending scrutiny remain entirely yours.
The four pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Account Aggregator
India’s consent framework lets a borrower authorise their bank to share statement data with a lender, structured and verifiable with a consent artefact.
Bank statement analysis
Derives income regularity, obligations, bounce history and cash-flow patterns from transactions. Analysis quality matters more than data access.
Risk assessment
Combining the derived signals into something a credit team can act on. It informs a decision your policy makes — the thresholds, exclusions and approvals stay entirely on your side.
The consent artefact
The record of what the borrower authorised, for what purpose and for how long. Easy to treat as plumbing, and it is part of the answer when a regulator asks how a decision was reached.
One consented path from Account Aggregator data to a credit signal — analysis your policy decides on, with the consent kept.
Six capabilities. Consent, derive, assess.
Signzy Lending reads cash flow with consent — income, obligations and the rest of the Signzy portfolio.
Consent-based bank data
Pulls statement data through Account Aggregator, so it arrives structured with provenance, not as a borrower’s email attachment.
Income, obligations, bounces
Derives income regularity, obligations, bounce history and cash-flow shape from transactions, where vendors actually differ.
A view, not a verdict
Signals combined into a credit view your team can act on. The thresholds, exclusions and approval decisions remain entirely within your own credit policy.
The artefact that survives audit
What the borrower authorised, for what purpose, for how long. Part of the answer when a regulator or ombudsman asks how a lending decision was reached.
Identity already verified
Shares a marketplace with onboarding, so identity checks and fraud signals reach the credit decision, not another system.
What you can show later
Records which data was pulled, under what consent and what the assessment returned, so decisions can be reconstructed.
Everyone gets the same data. Not the same reading.
Here’s what genuinely sets it apart — and exactly where it stops.
Account Aggregator changed the input, and the audit
Statements once arrived as emailed PDFs — unverifiable, editable, needing parsing. Account Aggregator lets borrowers consent to banks sharing data directly, structured and verifiable, with a record of what was shared, by whom and why — part of a defensible answer to a regulator. Ask how vendors use AA data.
Everyone can read the same statements — the analysis is the product
Account Aggregator is public, so every licensed participant gets the same statements. Vendors differ in interpretation: salary versus own-account transfers, seasonality versus decline, obligations missing from bureau files, real bounces versus bank errors. Test derived signals on your own past borrowers with known outcomes.
The identity work is already done
Lending shares a marketplace with Signzy’s onboarding and fraud APIs, so verified borrowers aren’t re-verified and onboarding fraud flags reach the credit decision, instead of sitting in separate systems. That only helps if you buy the other lines; standalone, Signzy competes on analysis quality.
The decision, the model risk and the fairness obligation stay yours
The platform supplies data, signals and an assessment; your credit policy sets thresholds, exclusions and approval. You own model risk governance, explainability when borrowers or an ombudsman ask, and fair-lending scrutiny, since alternative data can correlate with prohibited characteristics. Plan the documentation gap.
The numbers behind the platform
What your lending rollout looks like
Decide what you cannot see today
Income you cannot verify, obligations off the bureau file, or borrowers with no history at all. Each points at a different part of this line.
Get the AA consent flow right
Consent is part of the journey and the audit trail: a clumsy step costs completions, a sloppy record costs the audit.
Backtest on your own book
Run derived signals against loans with known outcomes; with everyone reading the same statements, it’s the only real test.
Write the policy around the signals
Set thresholds, exclusions and what a score triggers. The model informs; your policy decides, and regulators read it.
Assemble the model risk file
Explainability, monitoring, drift and fair-lending review: ask what you get, and produce the rest before your first review.
Review outcomes by segment
Alternative signals can correlate with prohibited characteristics. Test for that on a schedule, not after a complaint.
The review scoreboard
Modelled on Gartner Peer Insights structure. *Counts and breakdowns are illustrative pending verified review collection.
Quick poll — what’s driving your evaluation?
Talk to an advisor“The consent trail mattered more than we expected. When our auditors asked how we sourced income data, it was a record rather than a reconstruction.”
“The signals looked good in the demo, but we needed to backtest them against our own loans with known outcomes.”
“Ask what documentation you get for the model risk committee. We had to produce a fair share of the explainability material ourselves.”
“Every vendor gets the same AA statements; we were really buying the interpretation, which is worth testing properly.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and review-site grids score satisfaction, not fit for your estate. So here’s TechBag’s synthesis of the alternative lending data market — tap any vendor to see why it sits where it does.
TechBag Alternative Lending Grid
Execution strength vs product vision — the classic market map, minus the paywall.
AA with consent trail, on one contract.
Consent & Audit Record × Cash-Flow Signal
The grid nobody publishes — the strength of the consent and audit record vs quality of the derived cash-flow signal.
Derived signals plus the audit artefact.
Positions are TechBag’s illustrative synthesis of public review-platform data and vendor documentation — not a reproduction of any analyst graphic. Verify before relying on it.
Lending vs the alternatives
Against emailed statements, a bureau score alone and an in-house AA build — on provenance, analysis and who owns the decision.
| Dimension | Signzy Lending | Emailed statements | Bureau score alone | In-house AA build |
|---|---|---|---|---|
| Data provenance | AA with consent record | Unknown | Bureau-sourced | AA, yours |
| Income visibility | Derived from cash flow | Manual reading | None | Yours to build |
| Analysis quality | Test it yourself | Analyst judgement | N/A | Your data science |
| Who owns the decision | You do | You do | You do | You do |
| Model risk documentation | Ask for it | Human rationale | Bureau-supplied | Yours to produce |
| India data residency | Named region | Your inbox | In India | Your servers |
Which approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose Signzy Lending if…
- You want consent-based bank data with an audit trail, not emailed PDFs
- Onboarding or fraud already runs here, so identity and fraud signals reach credit
- You will backtest the derived signals against your own book before committing
- India data residency matters for financial data — Signzy names it, many do not
A bureau score may be enough if…
- Every applicant already has a thick bureau file and the score alone decides the loan
- Nobody will own validating and monitoring models built on bank-statement signals
- Volumes are low enough that manual statement review remains workable
Do not expect…
- AA access to be a differentiator — it is a public framework; the analysis is the product
- Model risk, explainability or fair-lending scrutiny to transfer to the vendor
- The same-contract advantage unless you actually buy the onboarding or fraud lines
What does unverifiable income cost you?
Drag the sliders (monthly applications; average loan margin). Estimates model applicants declined for unverifiable income plus the manual effort of reading statements by hand. Illustrative.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models your actual environment and modules.
Three ways to consume it
Quote-only — Signzy publishes no price. TechBag scopes the billing unit, the AA integration and the backtesting effort, then quotes in INR with GST.
Lending
Best when you cannot verify income
- Account Aggregator with a consent trail
- Income, obligations and bounce signals
- Records that survive an audit
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
+ the wider marketplace
Best across the journey
- Borrower already identity-verified
- Fraud flags reach the credit decision
- One contract, not three integrations
Buy it for less — TechBag pricing beats list
Whatever the list prices above, TechBag negotiates a significantly better deal — with GST-compliant INR invoicing and local support. Ask us for your discounted quote.
Get an India-ready quote
Tell us your requirements and current tools — we’ll model it against what you spend today.
The 8 questions to ask every vendor
Take this into your next vendor call — including ours.
What can you not see today — income, hidden obligations, or no history at all? Each points at a different capability.
Can you run the derived signals against loans with known outcomes? AA access is universal; the interpretation is what you are buying.
Is the AA consent step clean for the borrower, and does it produce the record you will need at audit?
What explainability and monitoring material do you receive for your model risk committee? Expect to produce some yourself.
How will you test that derived signals do not correlate with characteristics you cannot lend on? That obligation is yours.
Is it documented that the model informs and your credit policy decides? A regulator will ask to see that.
Is in-country storage documented for your deployment? Bank statement data is sensitive under DPDP.
Is billing per consent, per pull or per assessment? Nothing is published, and the unit changes the economics at volume.
Questions buyers ask
Ready to evaluate Signzy Lending?
Backtest the derived signals against loans whose outcomes you already know — every vendor reads the same statements — or let a TechBag advisor scope the AA integration and the model risk file.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.