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Bolehlah.ai
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/ developer surface · v4.2.1

Proprietary AI for
regional credit underwriting.

Bolehlah.ai is the engine brand of the AI credit intelligence platform that powers licensed lenders across Southeast Asia. Direct API access to /decisions, /scoring, /inference — trained on regional borrower data, explainable by design.

api.bolehlah.ai · operational | p50 142 ms | p99 412 ms | uptime 99.97% | region ap-southeast-1
~/ · bolehlah-ai-sdk
$ curl -X POST https://api.bolehlah.ai/v1/decisions \
    -H "Authorization: Bearer sk_live_...",\
    -d '{
      "applicant": { "ic_hash": "sha256:7a3b...", "cohort": "MY_GOV" },
      "request":   { "principal": 25000, "tenure_months": 60 },
      "context":   { "salary": 5250, "commitments": 1008.45 }
    }'

> HTTP/2 200
{
  "decision": "approve",
  "score": 78,
  "probability_default": 0.042,
  "inference_latency_ms": 138,
  "model_version": "v4.2.1",
  "explanation": {
    "dsr_weight": 0.40,
    "ctos_weight": 0.30,
    "tenure_weight": 0.15,
    "capacity_weight": 0.10,
    "history_weight": 0.05
  },
  "audit_hash": "0x8af2c41e...c91e"
}

API surface

Three endpoints.
One inference core.

POST /v1/decisions

Run a single applicant through the full decision pipeline. Returns verdict, score, default probability, factor weights, and an audit hash.

POST /v1/scoring

Score-only endpoint for portfolio sweeps. Accepts batch of up to 10,000 applicants; returns Bolehlah scores and cohort-relative percentiles.

POST /v1/inference

Raw inference endpoint for research and custom workflows. Accepts feature vectors, returns model outputs with full feature attribution.

Model card

Explainable
by design.

Every decision returns a weighted factor breakdown that regulators, auditors, and borrowers can read. Nothing is a black box.

Model card available to deployed institutions and authorised regulators.

bolehlah-credit-core · v4.2.1

released 2026-03-18

Architecture
Gradient-boosted ensemble + transformer cohort encoder
Training data
4.2M anonymised ASEAN-5 loan outcomes, 2018-2026
Markets
MY · ID · PH · VN · TH
Output
Decision + score + P(default) + feature attribution
Latency (p50 / p99)
142 ms / 412 ms
Audit log
Hyperledger Besu · 7-yr retention
Updated
Weekly retrain · monthly deploy

Benchmarks

Measured against
rulebook baselines.

Deployed institutions that run Bolehlah.ai against their internal scorecards, over 12 months, on equivalent applicant pools.

Default reduction

-35%

vs. rulebook underwriting baseline

Approval lift

+22%

at equivalent default rates

Autonomous rate

80%

decisions with zero human review

Decision time

3 s

end-to-end borrower journey

Architecture

AI-first
data model.

// First-class entities in the platform schema

decisions        // one row per AI verdict, immutable
inferences       // raw model outputs, feature attribution
model_versions   // deployed versions + A/B test allocations
training_samples // anonymised outcomes used for retraining
---
loans            // delivery artefact, not the source of truth
borrowers        // identity + cohort attachment
lenders          // deployed institutions, configured thresholds

Telemetry

Every request emits decisions_served, inference_latency_ms, model_version_deployed, explanation_generated to the observability plane.

Ledger

Each decision hashed to Hyperledger Besu. Tamper-evident audit trail. Regulators can verify any historical decision in under a second.

Training

Aggregated, anonymised outcomes flow into the training pipeline under a permanent licence per deployed-institution contract. The flywheel compounds.

Build against
the credit future.

API access is granted to deployed institutions, regulators, and credentialed AI research teams. Sandbox keys available in under 48 hours.