Servicing is where the truth arrives

A standard LMS records what happened. This one reads it. Repayment behaviour, delinquency patterns and borrower conduct become live signal rather than a quarterly report.

Book a demo

Intelligent LMS Loan management

LN-00001 Active Critical risk Harris Group · originated Feb 2025 · 24 month bullet

OutstandingAED 203Kprincipal

RepaidAED 166K45% repaid

Overdue0days

Next13 Sep2026

OverviewPaymentsScheduledAuditDocuments

Signals read on this loan

Payments 24 Statements 12 Documents 8 Restructures 1 Buyer changes 2 Contact events 5

What this loan is teaching the model every signal, every loan

→Eleven payments on time, but utilisation climbing and 41% of receivables sit with one buyer.Portfolio

→Bullet loans in the Q1 2025 cohort are underpredicted by 0.4 bands against outturn.Underwriting

→Suggested: raise buyer concentration weight 0.8 to 1.1, retrain on 6 fresh cohorts.Tuning

Schedules, penalties, restructures and a full audit trail. Suggested, never applied silently. Review 3 suggestions

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 closing loop.

Services the loan

Moves the thresholds

Retrains the model

01 · Servicing

The full servicing record, with the intelligence native

Schedules, disbursement, repayments, penalties, restructures and write-offs. The authoritative servicing record, with portfolio analytics running over it in real time rather than bolted on afterwards.

Schedules generated on activation

Repayments tracked and allocated per contract

Audit and document trail on every loan

Loan LN-00001Active · 24mo

Outstanding203Kprincipal

Repaid45%AED 166K

Overdue0days

Schedule and allocation

Instalment 1401 Aug 2026AED 9,420allocated

Instalment 1501 Sep 2026AED 9,420allocated

Instalment 1601 Oct 2026AED 9,420scheduled

Penalties, restructures and write-offs all land on the same audit trail.

02 · Credit policy

Thresholds that move when the book teaches you something

Rules and thresholds shift as performance data accumulates, surfaced as suggestions for your team to accept or reject, never applied silently behind your back.

Dynamic thresholds, suggested not imposed

Early warning before arrears, not after

Every AI-driven action logged with its rationale

Threshold suggestions3 to review

What the book has taught the policy

Minimum trading history1,240 loans over 18 months24 → 21 mo

DSCR floordefaults cluster below 1.301.25 → 1.30

Buyer concentration cap3 of 5 losses were single buyer35 → 30%

Suggested, never applied. Your team accepts or rejects each one, and the decision is logged with its rationale.

Early warning arrives before the arrears do.Review 3

03 · The loop

Most underwriting tools decision and walk away

Every repayment, every missed instalment, every recovery is a labelled example. Outcome data returns to the model layer, so the next version of your model is trained on what actually happened.

Every repayment becomes a labelled example

Outcomes feed straight back into the model

The next model is trained on your own book

Outcomes to model1,240 labelled

Labelled examples per month

Repaid on time1,014

Late, then cured173

Default53

v5 retrained on what actually happened, not on a benchmark book.Gini +0.03

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

In production, servicing live books today

Every repayment becomes a labelled example

Schedules, restructures and a full audit trail

Live across UAE, KSA, Oman and Jordan

Trusted by risk leaders

Your team works alongside ours

Book a demo

Servicing is where the truth arrives

Slowed by Reviews, Stuck with Risky Guesses

Three steps.

One closing loop.

The full servicing record, with the intelligence native

Thresholds that move when the book teaches you something

Most underwriting tools decision and walk away

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