Build the view, don't request it

Analysts build their own real-time monitoring and forecasting canvases at borrower and portfolio level, so action comes before the NPL, not after it.

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Portfolio Intelligence 142 live loans · live

DeployedAED 4.8M↑ 4.8% 142 loans

OutstandingAED 3.1M↑ 2.3% at work

On time94.6%↑ 2.1pt 12mo

PAR 30+6.4%↓ 0.9pt AED 201K

InsightsPerformanceRiskComposition

Your goal Grow the book 25% by Q2 without letting PAR 30+ pass 7%.

Active leversdrag to scrub

Revenue drop −18%

Cost rise +0%

Exposure at riskAED 720Kfrom AED 396K

Loans breaching4from 2

PAR 30+9.1%from 6.4%

At revenue −18%, ~~AED 720K~~ of the covered book breaches its DSCR target. The damage concentrates in one buyer's suppliers. Your 7% ceiling breaks at −12%.

Ask a downturn, or add a lever... Ask

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 live view of the book.

Build the canvas

Stress it

Ask it

01 · Monitoring canvas

Watch what you actually care about

Watchlists, DPD movement, concentration, cohort drift. Build a canvas per segment and pin it, one for real estate, another for food and beverage, rather than waiting on a report someone else has scoped.

Views at borrower and portfolio level

Watchlists, DPD movement, concentration, cohort drift

A canvas per segment, pinned as a dashboard

Canvases4 pinned

Real estate62 loans · PAR 4.1%

Food and beverage38 loans · PAR 9.2%

Retail27 loans · PAR 5.6%

Logistics15 loans · PAR 3.0%

Days past due, whole book

Current89.2%

31 to 60 days6.4%

61 to 90 days4.4%

Pin a canvas per segment. No waiting on a report someone else scoped.

02 · Simulation canvas

Stress the book before the market does

Forecast and stress views the analyst builds rather than asks us for. Model a revenue drop, a collection delay or a macro shock, and see which facilities breach their debt service capacity under it.

Flight path and stress scenarios

Debt service capacity recomputed under stress

Breaches surfaced before they arrive

ScenarioStress · unsaved

Levers

Revenue drop15%

Collection delay21 days

Facilities breaching cover3, from 2 today

Falcon Foods LLCDSCR 1.18

Nadeen Trading1.09

Gulf Print Co0.96

Three facilities breach under this scenario. Debt service capacity is recomputed per facility, not scaled off a portfolio average.

03 · Ask the book

A conversation with your loan book

Ask a question of your portfolio in plain language and pin the answer as a view. Alerts align to a stated objective: increase exposure, reduce balance sheet, lift yield, cut risk. They do not fire at everything.

Plain-language questions over live data

Alerts tied to your stated objective

Outcomes feed back into the model layer

Ask the bookLive data

AskWhich cohorts have drifted since March?

Food and beverage originated in Q1 has drifted 2.4 points on PAR 30+. Most of it sits with borrowers whose sales concentrate on one buyer group.

Your stated objective

Grow the book 25% by Q2on track

Hold PAR 30+ under 7%6.4%

Alerts fire against that objective, not at everything.Pin as view

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

Canvases you build, not reports you request

Ask your portfolio a question in plain language

Watches the book continuously, not quarterly

Live across UAE, KSA, Oman and Jordan

Trusted by risk leaders

Your team works alongside ours

Book a demo

Build the view, don't request it

Slowed by Reviews, Stuck with Risky Guesses

Three steps.

One live view of the book.

Watch what you actually care about

Stress the book before the market does

A conversation with your loan book

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