Pricing

Human vs. AI workforce - Southeast Asia lenders

Benchmarks assume USD-denominated planning common for regional HQs in Singapore and Malaysia, with loaded compensation bands reflecting hub markets (Singapore, Bangkok, Jakarta, Manila) and growth corridors. Figures are illustrative-not a quote. Metering for agents and LLMs follows transparent patterns comparable to other enterprise agent platforms.

Illustrative savings band

~58-88%

Typical reduction in modeled variable labor when acquisition, credit operations, early arrears, and back-office workflows are automated-while keeping MAS, BSP, OJK, and local conduct requirements in the human-approved design.

Faster onboarding without linear headcount adds

Retail, SME, and digital lending onboarding still involve e-KYC, income verification, and CRM discipline. Agents accelerate straight-through steps; RMs stay on complex structures and policy exceptions.

Typical human benchmark (SEA)

Sales and ops roles in Singapore and KL hubs often show $35K-$85K+ equivalent loaded packages; tier-2 cities lower but volume-heavy.
Meaningful application progression still consumes 20-45+ minutes of human time once KYC and suitability steps are included.
Multi-country ASEAN footprint duplicates policy training and audit sampling.

MAS, BSP, OJK, and Bank Negara emphasize fair dealing, AML, and documentation discipline-automation must embed those controls by design.

AI workforce + LendingIQ (illustrative)

English and local-language journeys with immutable logs, consent capture, and LOS/core integration.
Platform fee + metered workflows; LLM and messaging at contracted rates.
Human gates for high-value corporate or cross-border structures.

Many SEA teams model USD per completed onboarding pack or per qualified SME file.

Example: retail / SME acquisition factory

~2,400 qualified applications/month; ~3.5 FTE of mixed sales-ops capacity.

Modeled human run-rate

~$38K-$52K/mo loaded labor (illustrative model)

Modeled AI + LendingIQ

~$6K-$14K/mo LendingIQ + LLM usage for comparable throughput (illustrative)

Typical savings vs model

Often ~65-84% vs. modeled human run-rate at this volume

How to read these numbers

All figures are illustrative USD planning models for ASEAN contexts-not a binding quote. City tier, BPO mix, and incentive structures move loaded cost materially.
Cross-border and Islamic finance windows may require separate agent configurations-scoped in commercial proposals.
We align models to your entities, languages, and core systems in an AI audit.

Reference

Monetary Authority of Singapore

Want numbers tied to your LOS, channels, and SLAs- We’ll calibrate human baselines and agent throughput in a short working session.

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