Decisions you can defend line by line

Rules-based eligibility and a trained probability-of-default model, weighted how you choose. A to D bands, and the drivers behind every decision on the face of it.

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AI Credit Risk Officer Application 4471 · 11s

BandB PD2.9% LimitAED 600,000 Term36 months Falcon Foods LLC · working capital · AED 750,000 requested

Your policy, checked in full 12 rules · 11 clear

Trading history at least 24 months38 months

DSCR at least 1.25x1.61x

Bureau, no 30+ day arrears in 12 months0

Single buyer concentration under 35%41%Refer

Why this band, in the officer's words Cashflow is steady and the bureau file is clean, so the model reads this as investment grade. The band is held at B rather than A by one thing: 41% of receivables sit with a single buyer. Lending AED 600,000 against the AED 750,000 asked keeps DSCR above 1.4x even if that buyer stops paying.

Recommended, with the reasoning attached. Your officer approves it. Approve at AED 600,000

Trusted by Risk Leaders at

Slowed by Reviews, Stuck with Risky Guesses

Manual underwriting process slows down decisions

Limited ability to confidently approve thin-file SMEs

Credit teams stretched across spreadsheets, PDFs, and rules engines

Difficult to scale loan book without compromising risk

Three steps.

One defensible decision.

Gates on your rules

Shows its reasoning

Proves it on your book

01 · Architecture

Two layers, combined on your terms

An eligibility rules layer can gate the population while the PD model ranks risk within it, or the two can be weighted differently by product. We would rather you configured that than adopt an Orbii score wholesale.

A rules-based eligibility layer you control

A PD model over bank, financial and bureau signals

Weighted differently for each lending product

Decision mixPer product

Model weight against rules weight

Working capital70 / 30model led

Invoice finance40 / 60rules led

Term loan55 / 45balanced

Overdraft30 / 70rules led

How the layers combine

Eligibility rulesgate the population, 12 rules you own

PD modelranks risk inside the gate

You set the weighting per product. We would rather you configured it than adopted an Orbii score wholesale.

02 · Explainability

Every band arrives with its reasoning

Assessments surface the strengths, the risks and the features that drove them, so a credit officer can agree or overrule with something concrete to point at.

A to D bands with a probability of default score

Lens health and contributing features exposed

Human in the loop, or fully automated

Why band BPD 2.9%

Drivers behind this decision

Repayment rhythm, 24mo+0.41

Banked revenue stability+0.28

Buyer concentration, 41%−0.22

Bureau enquiries, 6mo−0.11

In the officer's words

Collections are strong and regular, and the bank record supports the requested limit. The single buyer at 41% is what holds this at B rather than A, so the limit is set below the ask.

Recommended, with the reasoning attached.Officer approves

03 · Validation

Aligned to your book before it decides anything

Models are backtested against your own historical outcomes by delinquency band and aligned to your internal credit grades before they decide anything live.

Backtested against your historical outcomes

Aligned to your existing credit grades

A/B comparison across model versions

Backtest18,400 loans

Predicted against actualby band

Band A0.6% → 0.7%

Band B2.9% → 3.1%

Band C7.4% → 7.1%

Band D15.2% → 16.0%

Aligned to your internal credit grades before it decides anything live.

Run side by side against v3 on the same cohorts, so you can see what changed.

Make Credit a Growth Engine,
Not a Bottleneck

92% ↓

in credit evaluation time

+68%

in approvals

58% ↓

in default rate

How Foodics Lender boosted loan approvals by 30% using Orbii’s AI engine.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut et massa mi.

Designed for developers

One record, one API.

A single API and one webhook stream across intake, decisioning, servicing and portfolio. Every field traces to its source, every decision is logged.

LN-00001

POST /v1/applications

{

"borrower": "Falcon Foods LLC",

"documents": [ 4 files ],

"policy": "sme_v3"

}

// 201 Created

{

"loan_id": "LN-00001",

"status": "parsing",

"events": "webhook"

}

documents.parsed 312 fields from 4 documents09:41:02

application.assembled screened against sme_v309:41:04

decision.issued Band B, PD 4.1%09:41:06

loan.disbursed on approval—

repayment.posted on each instalment—

portfolio.updated continuously—

policy.applied sme_v3, by rules engine09:41:04

model.scored champion v4, Gini 0.6209:41:06

reason.recorded DSCR 1.42, zero days past due09:41:06

field.traced every value back to its source page09:41:06

reviewer.assigned credit officer, queue sme09:41:07

Every event carries the same loan id. Nothing is re-keyed between surfaces.

Built for credit teams
that move fast

A band, a score and the reasoning behind both

Your rules gate it, the model ranks within it

Human in the loop, or fully model driven

Live across UAE, KSA, Oman and Jordan

Trusted by risk leaders

Your team works alongside ours

Book a demo

Decisions you can defend line by line

Slowed by Reviews, Stuck with Risky Guesses

Three steps.

One defensible decision.

Two layers, combined on your terms

Every band arrives with its reasoning

Aligned to your book before it decides anything

Make Credit a Growth Engine, Not a Bottleneck

in credit evaluation time

in approvals

in default rate

How Foodics Lender boosted loan approvals by 30% using Orbii’s AI engine.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut et massa mi.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut et massa mi.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut et massa mi.

One record, one API.

Built for credit teamsthat move fast

Make Credit a Growth Engine,
Not a Bottleneck

Built for credit teams
that move fast