Executive Summary

The Asia-Pacific region presents a unique lending ecosystem characterized by rapid economic growth, diverse regulatory environments, and high levels of financial inclusion drives. In countries such as India, Indonesia, Malaysia, Thailand, the Philippines, and Vietnam, banks and NBFCs face the dual challenge of meeting aggressive loan disbursal targets while ensuring compliance with evolving regulatory frameworks.

Traditional credit decisioning models, built on static rule-based engines and manual assessments, can no longer match the speed, scale, and precision required in APAC markets. The solution lies in agentic credit decisioning engines, self-directed, AI-first systems that can autonomously orchestrate decision workflows, adapt in real-time, and ensure explainability and audit readiness.

Newgen’s Agentic Credit Decisioning Engine is designed to help lenders across APAC deliver instant, explainable, and compliant credit decisions, whether in retail loans, MSME lending, credit cards, or complex corporate loans while aligning with local regulatory guidelines.

The APAC Lending Landscape: Opportunities and Challenges

1. A Region on the Move

Rising loan demand: APAC banking loan volumes are projected to grow at a CAGR of 6–8% over the next five years, driven by rising middle-class populations, digital-first banking, and government-led financial inclusion programs.
MSME lending focus: In countries like India, Indonesia, and the Philippines, MSMEs contribute over 40% of GDP, yet face chronic credit gaps. Digital-first decisioning can bridge this gap.
Mobile-first banking: Over 70% of customers in APAC access banking primarily via smartphones, expecting instant decisions.

2. Challenges for Lenders

Fragmented regulatory environment: From RBI in India to OJK in Indonesia, each regulator has unique compliance mandates.
Credit bureau coverage gaps: Limited or inconsistent credit history data in emerging economies increases the need for alternate data usage.
Fraud risks: Higher exposure to identity theft, synthetic identities, and small-ticket frauds.
Speed vs. compliance trade-off: Instant decisions can’t compromise regulatory adherence or auditability.

What is an Agentic Credit Decisioning Engine?

An agentic system is not just AI-enabled, it’s self-directed and proactive, capable of:

Autonomous orchestration of data gathering, scoring, and approval steps.
Real-time adaptability to market and customer risk profiles.
Built-in explainability to meet regulator demands.
Self-improving models based on new data patterns and outcomes.

Newgen’s engine integrates:

AI/ML Models: Predictive scoring, risk categorization, and fraud detection.
Business Rules Management: Customizable for each APAC country’s regulations.
Integration Hub: Connects with credit bureaus, eKYC services, alternative data providers, and core banking.
Audit & Compliance Layer: Auto-generates decision rationale, logs all decision steps, and ensures data lineage.

How Newgen Delivers Instant, Explainable, and Compliant Decisions

1. Instant Decisions

Pre-integrated data connectors for APAC credit bureaus (e.g., CIBIL India, CTOS Malaysia, CCRIS Thailand, PEFINDO Indonesia).
API-driven alternate data ingestion from telcos, utilities, and e-commerce for thin-file customers.
Real-time scoring models that can approve small-ticket loans in under 30 seconds.

2. Explainable AI

Natural language decision summaries for underwriters and regulators.
Transparent model logic stored in decision audit logs.
Reason codes for acceptance or rejection provided instantly to applicants (meeting fair lending requirements).

3. Compliance by Design

Country-specific rule templates for:
- RBI (India) lending guidelines
- Bank Negara Malaysia’s responsible lending framework
- OJK (Indonesia) reporting mandates
- BSP (Philippines) credit reporting requirements
Automated reporting to regulators via API feeds or bulk uploads.
Data privacy compliance with PDPA Malaysia, PDP Bill India, and other APAC privacy laws.

Use Cases Across APAC

1. MSME Lending in India

Challenge: High volume of first-time borrowers with no bureau history.
Solution: Newgen integrates GST data, UPI transaction history, and bank statement analytics to produce alternate credit scores.
Impact: Loan approval times reduced from 5 days to 2 hours; 30% increase in MSME approvals.

2. Digital Microloans in Indonesia

Challenge: Need for rapid small-ticket loan approvals under strict OJK oversight.
Solution: Pre-configured decision flows with automated fraud detection based on device ID and geolocation.
Impact: Fraud losses reduced by 25%, while loan turnaround dropped to under 5 minutes.

3. Retail Loan Portfolio Expansion in Malaysia

Challenge: Manual underwriting causing bottlenecks for personal loans.
Solution: Straight-through processing for low-risk applicants, with explainable rejection reasons for others.
Impact: Underwriting capacity doubled without increasing headcount.

Industry Data Snapshot

90% of APAC lenders say AI will be critical to credit decisioning by 2026 (PwC survey).
50–70% of applications in emerging APAC markets still lack complete credit bureau data necessitating alternate data models.
Digital-first loan products grew 3x faster than branch-based lending in APAC during 2020–2024 (McKinsey).
APAC regulators are increasingly adopting “explainable AI” mandates, mirroring EU’s AI Act requirements.

Key Benefits of Newgen’s Agentic Credit Decisioning Engine for APAC

Speed without shortcuts – Instant decisions backed by robust compliance checks.
Explainability at scale – AI-driven yet regulator-friendly outputs.
Regional flexibility – Adaptable to diverse country rules and lending norms.
Fraud resilience – Embedded fraud analytics for identity and behavioural anomalies.
Scalable architecture – Handles millions of decisions monthly without degradation.
Continuous learning – Models retrain automatically to adapt to evolving borrower profiles.

Implementation Blueprint for APAC Lenders

Phase 1 – Compliance Alignment
Map existing lending processes to local regulations; configure Newgen’s rules engine with country-specific templates.

Phase 2 – Data Integration
Connect with local bureaus, eKYC systems, and alternate data providers.

Phase 3 – Model Deployment
Deploy AI/ML models trained on local market datasets for accuracy.

Phase 4 – Go-Live with Audit-Readiness
Activate decisioning with real-time explainability and full audit trails.

Phase 5 – Continuous Optimization
Leverage feedback loops for ongoing performance tuning.

For APAC lenders, the race to capture growing loan demand while managing compliance and fraud risks is intensifying. The future belongs to those who can deliver instant, explainable, and compliant credit decisions at scale.

Newgen’s Agentic Credit Decisioning Engine is purpose-built for the complexities and opportunities of APAC. By combining Artificial intelligence with agentic orchestration and embedded compliance, it enables lenders to confidently accelerate credit growth, expand financial inclusion, and stay future-ready.

Agentic Credit Decisioning in APAC