What counts as ‘alternative data’ in credit scoring?

Alternative data includes digital footprints such as mobile phone usage, e-commerce purchases, utility bill payments and social media interactions. These sources can complement traditional credit histories, especially in markets with large underbanked populations.

How can we reduce bias in AI credit models?

Use diverse training data that represents your entire customer base, avoid sensitive attributes, test models for disparate impact and implement human oversight. Regulatory guidelines often require explainability and fairness audits.

Is AI enough to stop fraud?

AI greatly improves fraud detection, but criminals adapt quickly. Combine AI with multi-modal authentication, robust identity verification and ongoing monitoring. Human analysts still play a role in investigating flagged cases.

AI Finance: Fraud Detection & Inclusive Credit Scoring Guide for Southeast Asia

Financial services in Southeast Asia use AI for risk scoring, fraud detection and personalised products. AI-powered credit scoring models leverage alternative data such as social media activity and e-commerce transactions to enable faster and more inclusive lending across ASEAN, benefiting micro, small and underbanked segments.

Foundations of AI in Finance

Financial institutions in Southeast Asia are using AI to detect fraud, verify consumer information and offer personalised products. AI-powered models analyse transactional data, alternative data and behavioural patterns to assess creditworthiness. Real-time analytics enable faster decisions and extend credit to underbanked populations. Regulatory frameworks like Singapore’s MAS guidelines and Indonesia’s draft regulations ensure fairness and transparency. Understanding these fundamentals will help you design robust AI solutions.

A Data-to-Decision Framework

Use a four-step process: 1. Data collection – Gather traditional financial data and alternative data such as mobile usage, payment history and social media activity. 2. Model development – Train AI models to detect fraud patterns and estimate credit risk, ensuring diversity in training data to avoid bias. 3. Decision engine – Implement real-time decision rules that combine AI outputs with human oversight; ensure compliance with local regulations. 4. Monitoring and feedback – Continuously monitor model performance, update with new data and audit for fairness and explainability.

Common Mistakes and How to Fix Them

Common pitfalls include relying on narrow data sources that exclude segments of the population, ignoring bias and fairness, and not accounting for emerging fraud techniques like deepfakes and generative AI. Failing to align with regulatory guidelines can lead to fines or reputational damage. Avoid these by diversifying data, building explainability into models, adhering to guidelines such as those from MAS and Indonesia’s financial authorities, and using multi-modal authentication to combat sophisticated fraud.

Prompts

Credit Scoring Model

Design an inclusive credit scoring approach

You are a fintech founder in Manila. Outline a credit scoring system for microloans that combines traditional credit data with alternative data such as mobile payments, utility bills and social media activity. Describe how you will validate the model to prevent bias.

Fraud Detection Script

Detect suspicious transactions

Write a pseudocode algorithm that flags potential fraud in a digital wallet platform in Jakarta. Your algorithm should consider transaction amount anomalies, unusual device locations and behavioural patterns, and it should trigger multi-factor authentication when risk is high.

Regulatory Compliance

Compare regional regulations

Summarise the key differences between Singapore’s AI risk management guidelines and Indonesia’s draft regulations for AI-driven credit scoring. Highlight the main principles financial services should follow in both markets.

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.

AI in Financial Services: Fraud Detection and Inclusive Credit Scoring in Southeast Asia

AI Snapshot

Perfect For

Foundations of AI in Finance

A Data-to-Decision Framework

Common Mistakes and How to Fix Them

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Prompts

Design an inclusive credit scoring approach

Detect suspicious transactions

Compare regional regulations

Frequently Asked Questions

Ready to experiment?

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