How quickly can AI fraud models be deployed?

Timeline varies by complexity. Simple transaction scoring models can go live in 4-8 weeks. More complex behavioral analytics or network fraud detection may take 3-6 months including model training, testing, and regulatory validation. We'll help you build a phased roadmap during the training programme.

What if AI fraud models generate too many false positives initially?

All AI fraud models require calibration. We teach threshold tuning, champion-challenger testing, and customer impact analysis to optimise the balance between fraud detection and customer experience. Expect 2-3 months of iterative refinement post-deployment.

Can AI detect completely new fraud tactics it hasn't seen before?

Yes, through unsupervised anomaly detection. AI identifies unusual patterns even without labelled fraud examples. However, supervised models (trained on known fraud) are more accurate for established tactics. We teach hybrid approaches combining both methods for comprehensive coverage.

Do we need data scientists on our fraud team to use AI tools?

Not necessarily. Many AI fraud platforms offer no-code interfaces for fraud analysts. The training focuses on using AI tools, interpreting outputs, tuning models, and managing vendors — not building algorithms from scratch. Technical depth is adjusted to your team's background.

How do we explain AI fraud decisions to customers who are blocked?

We teach explainability frameworks including reason codes (e.g., 'transaction declined due to unusual location'), customer communication scripts, and appeal processes. You'll learn to balance fraud prevention with customer transparency and regulatory expectations for algorithmic fairness.

Training Solutions

SHIELD-FS

Cohort-basedSubsidy eligible

AI Fraud Detection & Risk Management for Financial Services

Fraud and risk teams can deploy AI to detect payment fraud, account takeovers, and identity theft in real-time, reduce false positives by 70%+, and protect customers across digital channels while maintaining regulatory compliance and operational efficiency.

Equip fraud prevention and risk teams with AI tools to detect payment fraud, identity theft, credit risk anomalies, and cyber threats in real-time. Built for banks, payment processors, e-wallets, and fintech platforms protecting customers across Southeast Asia's rapidly digitising financial ecosystem.

Duration3-4 days

InvestmentUSD $20,000 - $40,000

Best forFraud analysts, risk managers, cybersecurity teams, and payment operations professionals at financial institutions combating sophisticated fraud across digital banking, e-commerce, and mobile payment channels

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THE CHALLENGE

Sound familiar?

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We're detecting fraud after it happens — AI could identify suspicious patterns in real-time before transactions complete.

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Card fraud is costing us 15-20 basis points in losses annually, and our rule-based systems can't keep up with evolving tactics.

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Customer account takeovers are up 300% since we launched digital banking, but we can't tell legitimate logins from fraudsters.

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Our fraud review queue has 5,000 flagged transactions daily — 95% are false positives, overwhelming our analysts.

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Scammers are using AI-generated deepfakes to bypass our video KYC process; we need AI to fight AI.

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We're losing good customers because our fraud controls are so aggressive they block legitimate transactions from Indonesia and the Philippines.

Trusted by enterprises across Southeast Asia

Financial Services

Healthcare

Education

Manufacturing

Professional Services

Government

OUTCOMES

What you'll achieve

Problems you'll solve

Fraud detection systems generating 90-95% false positives, blocking legitimate customer transactions
Payment fraud losses at 15-25 basis points annually due to delayed detection and evolving fraud tactics
Account takeover fraud rising 200-400% with digital banking adoption across Southeast Asia
Fraud review teams overwhelmed by manual transaction investigations, taking 24-48 hours per case
Credit risk assessment relying on static models instead of real-time AI behavioral analysis
Cyber threat detection reactive instead of predictive, missing early warning signs of attacks

Value you'll gain

Loss Prevention: Reduce fraud losses by 40-60% through real-time AI anomaly detection and transaction blocking
Customer Experience: Cut false positive rates by 70-80%, reducing friction for legitimate customers
Speed Improvement: Detect and block fraudulent transactions in <100 milliseconds instead of hours or days
Cost Reduction: Decrease fraud investigation costs by 50% through AI automation of routine case analysis
Threat Intelligence: Identify emerging fraud patterns 30-60 days earlier using AI behavioral analytics
Compliance Confidence: Demonstrate robust fraud controls to regulators through AI auditability frameworks

OUR PROCESS

How we deliver results

Step 1

Fraud Landscape Assessment

We analyse your fraud loss data, detection systems, channel vulnerabilities (mobile banking, e-commerce, ATM), and customer friction points to identify AI automation opportunities.

Step 2

Risk Training Customisation

We tailor the programme to your fraud patterns (payment fraud, identity theft, account takeover), tech stack (fraud detection platforms, scoring engines), and customer segments.

Step 3

Hands-On AI Fraud Training

Your fraud and risk teams gain practical experience with AI tools for real-time fraud detection, behavioral analytics, identity verification, and cyber threat intelligence across 3-4 days of workshops.

Step 4

Use Case Development

Teams design 3-5 AI fraud use cases (e.g., real-time card fraud scoring, account takeover detection, synthetic identity prevention) tailored to your institution's risk profile and channel strategy.

Step 5

Implementation & Model Validation

We provide 90-day support including AI model testing, threshold calibration, customer impact analysis, and regulatory documentation to ensure fraud AI systems are effective and auditable.

What you'll receive

Customised AI fraud training programme (3-4 days)
5 training modules with hands-on labs and fraud case studies
3-5 AI fraud use cases with implementation roadmaps and ROI analysis
AI model validation and customer impact assessment frameworks
Fraud detection performance dashboards and monitoring templates
Regulatory documentation for AI fraud systems
90-day post-training support and implementation guidance

Best for

Fraud analysts, risk managers, cybersecurity teams, and payment operations professionals at financial institutions combating sophisticated fraud across digital banking, e-commerce, and mobile payment channels

IS THIS RIGHT FOR YOU?

Finding the right fit

This is ideal for you if...

Financial institutions experiencing rising fraud losses across digital channels
Banks and payment processors with high false positive rates overwhelming fraud teams
Fintech platforms scaling rapidly and facing sophisticated fraud attacks
Fraud teams preparing to replace legacy rule-based systems with AI
Institutions seeking to balance fraud prevention with customer experience

Consider another option if...

Organizations without fraud teams or significant fraud losses (AI may not be cost-effective)
Teams expecting AI to eliminate 100% of fraud (fraud is an arms race; AI reduces losses, not eliminates them)
Institutions unwilling to invest in model calibration and continuous improvement

See yourself in the list above?

Let's Talk

CURRICULUM

What you'll learn

2 days total

Introduction to AI in fraud prevention, machine learning fraud models, behavioral analytics, and AI risk management for financial institutions.

What you'll be able to do

Explain how AI transforms fraud detection from rule-based to behavioral pattern recognition
Identify fraud use cases where AI delivers 10x improvement over traditional systems
Assess trade-offs between fraud detection accuracy and customer experience friction
Navigate regulatory expectations for AI fraud systems (explainability, auditability, fairness)
Evaluate AI fraud vendor capabilities and integration requirements