The Fraud Detection Buyer's Guide for Southeast Asian Banks

If you lead fraud prevention or risk management at a Southeast Asian bank or fintech, you've probably noticed the vendor landscape has become overwhelming. Dozens of platforms claim to solve "fraud" — but they're actually solving different problems, for different verticals, with different architectures.

The reality: the online fraud detection (OFD) market isn't one market. It's at least five, and understanding this segmentation is the single most important step in making a smart purchasing decision.

Why This Matters Now

The urgency is real. Juniper Research projects global e-commerce fraud losses will reach US$107 billion by 2029, up from US$44.3 billion in 2024 — a 141% increase. In the Asia Pacific region, Sumsub reports identity fraud surged 121% in 2024.

Southeast Asia's digital economy — projected to surpass US$300 billion in 2025 according to the e-Conomy SEA report — is growing fast, and fraud exposure is scaling with it. Meanwhile, 57% of fraud attempts now involve scams like phishing, romance fraud, and authorized push payment (APP) fraud, up from a fraction of that just a few years ago.

And the attackers are industrializing: reports of genAI-enabled scams increased 456% between May 2024 and April 2025, while an estimated 82% of phishing emails now show signs of AI generation.

The Five Segments of Fraud Detection

Here's the framework that matters. Every fraud detection product falls into one (or occasionally two) of these categories:

1 Transaction Monitoring — Banking Focus

Enterprise fraud management for financial institutions. On-premise or private cloud deployment. Custom ML models per institution. Data stays within the bank. Integrated case management and investigation workflows. This is the core of fraud detection for banks and the segment most relevant to regulated financial institutions.

2 Transaction Monitoring — Commerce Focus

Cloud-native SaaS fraud prevention for e-commerce and retail. Leverages network intelligence across merchants (privacy-preserving). Chargeback guarantee models. Optimized for approve/decline decisions at checkout. Less relevant for banks, but important if you operate a merchant payments ecosystem.

3 Device Intelligence & Behavioral Biometrics

Device fingerprinting, keystroke dynamics, mouse movement analysis, and malware detection. Critical for detecting account takeover, identifying when users are being coached by scammers (APP fraud), and spotting money mule accounts. This segment has become essential — not optional — as scam-driven fraud overtakes traditional payment fraud.

4 Bot Mitigation

Defense against automated attacks: credential stuffing, card testing, inventory hoarding, and scraping. Typically deployed at the CDN/edge layer. Most important for digital banking channels, e-commerce, and any public-facing API. The travel industry alone sees a 56% bot attack rate.

5 Fraud Orchestration

The integration layer. Connects multiple fraud tools, data sources, and decision workflows into a unified platform. Low-code/no-code rule builders. Enables rapid testing of new data sources without engineering effort. Becoming the backbone of modern fraud programs that use best-of-breed components.

The key insight: most organizations need solutions from two or three segments, not all five. The combination depends on your business model.

What Banks Typically Need

If you're a bank or regulated financial institution, your core stack usually looks like this:

Transaction monitoring (banking focus) — your primary fraud detection engine for real-time decisioning across payment rails
Behavioral biometrics — increasingly critical for detecting APP scams, where the legitimate account holder is being manipulated into authorizing a fraudulent transaction
Orchestration — to unify signals from multiple tools and enable your risk team to adapt rules without waiting for engineering cycles

Add bot mitigation if you have significant digital banking or API exposure. Commerce-focused monitoring matters only if you also operate a merchant payments ecosystem.

What E-Wallets and Fintechs Typically Need

For e-wallets and payment fintechs, the emphasis shifts:

Behavioral biometrics + identity verification — your core defense, especially for onboarding fraud and account takeover
Orchestration — to connect KYC, AML, and fraud workflows in one decisioning layer
Commerce monitoring — if you have a merchant ecosystem or marketplace component

Three Mistakes to Avoid

We see the same mistakes repeated across the region. Here are the three most common:

1. Too Many Disconnected Tools

Siloed tools create data gaps and blind spots. When your device intelligence can't inform your transaction monitoring, and your case management sits in a separate system, you lose the cross-signal analysis that catches sophisticated fraud. Go for an integrated platform or a robust orchestration layer — not five disconnected point solutions.

2. Heavy Engineering Requirements

If every rule change or data source addition requires an engineering sprint, your fraud team will always be months behind attackers. Platforms that require 12–18 months to implement and dedicate engineering teams for ongoing tuning are mismatched with the speed of fraud evolution. Look for low-code configuration, pre-built integrations, and fast time-to-value.

3. No Automation Capability

Manual review queues cannot keep pace with automated attacks. If your system flags transactions for human review but can't auto-decisioning at scale, your analysts will be overwhelmed and your false positive rate will drive customer friction. Look for ML-based automation, real-time decisioning, and models that adapt continuously.

Five Must-Have Capabilities

Regardless of which segments you need, every fraud detection investment should deliver these five capabilities:

Data ingestion from multiple sources — internal transaction data, device signals, third-party intelligence, and behavioral analytics. The system must combine these in real time.
Machine learning with continuous improvement — static rule engines alone can't keep pace. Look for ML that auto-adapts to new fraud patterns and allows rapid model updates.
Device and session assessment — understanding the device, location, and session behavior behind each transaction is now baseline, not premium.
Case management and investigation tools — your fraud analysts need efficient workflows to investigate alerts, document decisions, and generate regulatory reports.
Consortium or network intelligence — new fraud patterns emerge faster than any single institution can learn. Cross-institution data sharing — like Singapore's MAS COSMIC platform — is becoming a competitive necessity.

Key Trends Shaping 2026 Decisions

Six technology trends should inform your evaluation this year:

Fraud + AML convergence — the industry is moving toward unified financial crime platforms. Evaluating fraud detection separately from AML is increasingly outdated. Look for platforms that can do both.
Scam detection as a distinct capability — traditional fraud detection catches unauthorized transactions. Scam detection catches authorized transactions where the customer has been manipulated. This requires behavioral biometrics and is now the top priority for regulators.
GenAI for fraud prevention — AI co-pilots for investigators, automated pattern explanation, and synthetic data for model training. The same technology powering scam attacks can strengthen defenses.
Cyber-fraud fusion — Gartner predicts 20% of large enterprises will merge fraud prevention and cybersecurity teams by 2028. The attack surface no longer respects organizational boundaries.
Consortium intelligence — cross-institution fraud data sharing is moving from "nice to have" to "mandatory." Singapore's COSMIC, regional information-sharing initiatives, and vendor-facilitated consortium networks are all gaining traction.
On-premise and hybrid deployment — data residency requirements across SEA are tightening. Cloud-only vendors increasingly face pushback from banks whose regulators require local data processing. Look for deployment flexibility.

A Practical Evaluation Roadmap

If you're starting or refreshing a fraud detection evaluation, here's a phased approach:

Short Term (0–6 months)

Assess your current fraud maturity: Are you reactive (manual rules, siloed data), adaptive (ML models, some automation), or predictive (AI-native, consortium data)?
Identify your biggest gaps — for most SEA banks today, that's scam detection and account takeover prevention
Map your needs to the five segments. Don't buy capabilities you don't need.

Medium Term (6–18 months)

Deploy or upgrade your core transaction monitoring with ML-based decisioning
Add behavioral biometrics for APP scam detection
Join or evaluate consortium intelligence networks

Long Term (18+ months)

Build toward a unified financial crime platform (fraud + AML)
Prepare for cross-border payment fraud as ASEAN real-time payment linkages expand
Invest in orchestration to unify your growing tool stack

The Bottom Line

The fraud detection market is large, noisy, and fragmented. Vendors from different segments often position as if they solve the same problem — they don't. Understanding the five-segment framework and honestly assessing which segments match your risk profile is the difference between a strategic investment and an expensive shelf-ware purchase.

For Southeast Asian banks specifically, the combination of rising scam losses, tightening regulatory requirements, data residency constraints, and the speed of real-time payment rails means the bar for fraud detection is higher than ever. Choose solutions that are built for this reality — not adapted from other markets or other eras.

Run-True Decision is building a fraud decision engine designed for Southeast Asian banks — with real-time decisioning, on-premise deployment, and pre-built fraud detection templates for regional payment patterns. Talk to us about your fraud prevention strategy.