What is alternative data in credit scoring?

Alternative data includes information from social media, e-commerce purchases and mobile payments that, when processed by AI, offers deeper insights into creditworthiness.

How can AI reduce fraud in digital finance?

AI models analyse transaction patterns and biometric data in real time, detecting anomalies like deepfakes and phishing, and enabling multi-modal authentication for secure transactions.

Are there regulations for AI in finance in Southeast Asia?

Yes. Regulators like Singapore’s Monetary Authority and Indonesia’s Financial Services Authority issue guidelines ensuring AI use is fair, transparent and accountable; fintechs must comply with data protection and consumer consent requirements.

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Guide

Intermediate

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Financial Services Professionals and Fintech Entrepreneurs

Southeast Asia

AI in Finance & Fintech: Fraud Detection and Inclusive Lending in Southeast Asia

AI Finance; Fraud Detection; Inclusive Lending

AI Snapshot

The TL;DR: what matters, fast.

AI analyses alternative data to deliver faster, more inclusive credit decisions in Southeast Asia.
Implement a data-to-decision framework: gather alternative data → train scoring models → validate results → ensure fairness and compliance.
Address regulatory and security challenges such as proactive cybersecurity investment and multi-modal authentication.

Perfect For

Bankers, fintech founders and compliance officers seeking to expand financial inclusion and reduce fraud.

Financial services in Southeast Asia rely on AI for risk scoring, fraud detection and personalised products. As digital payments surge, AI enables real-time decisions while regulatory frameworks ensure fairness and accountability.

Foundations of AI in Finance

AI models use alternative data like social media, e-commerce and telco records to assess creditworthiness and detect fraud. Regulators such as Singapore’s MAS issue guidelines to ensure AI use is fair and transparent.

Framework for Fraud Detection and Inclusive Lending

Identify data sources, train predictive models, implement real-time monitoring and multi-modal authentication, validate fairness, and ensure compliance with local regulations.

Common Mistakes and How to Fix Them

Pitfalls include poor data quality, overfitting models and neglecting regulatory guidelines. Fix by using clean data, performing regular audits and engaging regulators early.

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Prompts

Credit Scoring

Design an inclusive credit model

Act as a fintech data scientist. Outline a plan to build an AI credit scoring model for micro-loans in Malaysia using alternative data (e-commerce, telco, social media). Include fairness checks and key metrics.

Fraud Detection

Improve fraud detection

You are the head of fraud at a digital bank in Indonesia. Describe how to implement multi-modal AI to detect deepfake transactions, including document verification, biometric checks and behavioural analysis.

Regulatory Compliance

Summarise ASEAN finance AI regulations

Summarise the key AI and data analytics guidelines from the Monetary Authority of Singapore and Indonesia’s Financial Services Authority for AI-based credit scoring. Highlight fairness, transparency, and consumer protection requirements.

Frequently Asked Questions

Ready to experiment?

Pick one of these prompts and see where it takes you. The interesting bit is not just getting results - it is discovering what happens when you tweak the parameters or combine different approaches. If you end up with something unexpected (whether that is brilliantly unexpected or amusingly terrible), we would genuinely love to see it.

Share your results, your variations, or the weird tangents you went down trying to get things just right. That is often where the best insights come from: the collective trial and error of people actually using these tools in practice.

And if you found this useful, we have got plenty more practical how-to guides covering everything from creating images for your blog to helping you automate boring work tasks. Each one is built the same way: real techniques, actual examples, no fluff.

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