As digital transactions continue to rise across Asia’s BFSI sector, so does the sophistication of fraud. In 2023 alone, India recorded a 45% increase in digital payment fraud, according to the Reserve Bank of India. Cybercriminals are no longer relying on brute-force techniques – they’re leveraging automation, synthetic identities, deepfakes, and social engineering attacks to bypass traditional detection systems.

This escalating threat landscape demands more than reactive measures. It calls for proactive, predictive, and adaptive AI Defence systems – custom-built for the BFSI ecosystem.

The Problem with One-Size-Fits-All AI in Fraud Detection

Generic machine learning models or cloud-based APIs, while useful, are increasingly inadequate. They struggle with:

• Local context (e.g., Indian KYC documentation formats, regional naming conventions)
• Real-time adaptability
• Compliance with country-specific regulations
• Latency requirements for high-frequency transactions

Moreover, many pre-trained fraud detection models lack the granularity to understand complex patterns that are unique to BFSI institutions in specific geographies.

Enter Custom AI Models: Tailored for Your Risk Landscape

Custom AI models – built in-house or in collaboration with domain-specialist partners – are trained on a bank’s own transactional data, historical fraud patterns, customer behaviour, and regional fraud typologies. This enables them to identify subtle anomalies, such as:

• Unusual time-of-day transaction patterns
• Behavioural deviations (e.g., device fingerprint mismatches)
• Rapid changes in location or IP addresses
• Newly generated synthetic identities

When trained properly, these models not only reduce false positives but also flag emerging fraud signals before traditional systems would even recognize them.

Multi-Modal Fraud Detection: A Game Changer

Modern fraud doesn’t just happen through one channel. It’s multi-pronged – including social media phishing, mobile malware, insider collusion, and transaction manipulation. Custom AI models can be designed to process multi-modal data:

• Text (complaints, messages, support tickets)
• Audio (voice-based KYC verifications)
• Behavioural data (mouse movement, typing speed)
• Video (facial recognition during onboarding)

This holistic view allows for more robust and layered defences.

Real-Time Risk Scoring at Scale

In high-volume environments like net banking, UPI, or loan disbursement, real-time fraud detection is critical. Custom AI models can be optimized for:

• Low latency inference (under 100ms decision time)
• Contextual understanding (e.g., differentiating between a genuine loan top-up vs. a red-flag rapid credit request)
• Dynamic thresholds based on user history, transaction type, and current system-wide risk

According to a 2023 PwC report, financial institutions that use custom AI models for fraud prevention reduce losses by 30% compared to those using standard rule-based or generic ML models.

The India Edge: Local Language and Identity Context

India presents unique challenges and opportunities. Fraudsters often exploit:

• Regional language scripts in phishing attacks
• Rural onboarding systems with limited digital trails
• Loopholes in Aadhaar and PAN card usage

A model trained on Indian data, including regional language inputs, is far more effective than a generic model trained on Western datasets. For instance, a phishing detection model that understands code-mixed Hinglish can flag malicious SMS or WhatsApp messages more accurately.

Regulatory Benefits of Going Custom

In a regulatory environment where compliance is as critical as detection, custom AI models allow for:

• Transparent decision-making logs
• Local hosting or private cloud deployment
• Easy audit trails for AI decisions
• Adherence to RBI, SEBI, and IRDAI norms

With new guidelines emerging from regulators around explainability and model governance, BFSI institutions need AI that is not only accurate but also defensible.

Getting Started: Building Your Own Fraud AI Stack

To begin building custom AI models for fraud prevention, BFSI players should:

1. Ingest diverse internal data: Structured (transaction logs), unstructured (email/chat logs), and semi-structured (KYC forms).
2. Build a fraud taxonomy: Categorize fraud types specific to products (e.g., loan fraud, credit card skimming, mule accounts).
3. Collaborate with AI partners: With BFSI experience and secure MLOps pipelines.
4. Implement human-in-the-loop systems: For model feedback and evolution.
5. Continuously retrain: Using feedback loops and newly detected fraud patterns.

The Business Case: It Pays to Be Proactive

Every dollar lost to fraud impacts customer trust, brand equity, and operational cost. A 2023 LexisNexis report estimates that the true cost of fraud for Indian BFSI companies is INR 3.90 for every INR 1 lost, once investigation and recovery costs are factored in. Preventing even 10% of this can have a significant bottom-line impact.

Moreover, superior fraud detection also leads to:

• Fewer chargebacks
• Higher customer confidence
• Reduced manual investigation costs
• Improved regulatory standing

Conclusion

In today’s high-speed, hyper-digital BFSI ecosystem, fraud prevention isn’t just a security function – it’s a customer experience and business continuity imperative. Generic tools might offer a quick fix, but only custom-built AI models offer the accuracy, agility, and localization that modern fraud requires.

For India’s BFSI institutions, building these models isn’t just smart – it’s essential. Choose experienced partners like Process9 to fortify your fraud detection operations through cutting edge custom AI models.