What challenges did HCLTech face before AI implementation?

Traditional fraud detection systems rely on rule-based mechanisms that struggle with sophisticated fraud patterns and produce high false positives. Legacy system complexity and siloed data make it difficult to investigate suspicious transactions in real time. Manual processes, long investigation timelines, and weak reporting workflows hurt customer trust and create compliance risk.

What was the impact of the AI solution?

The article says the approach leads to far fewer noisy alerts reaching investigators. It says only high-confidence cases are escalated and false positives drop because decisions include context. It says customers get timely, professional communication and callbacks and manual outreach are reduced. It says reporting is consistent, repeatable, and far less manual. The article characterizes the approach as improving accuracy, scalability, and compliance for modern fraud prevention.

Source

HCLTech FraudShield: Agentic AI for real-time financial transaction fraud investigation

Use case typeWorkflow automationUpdated Jan 8, 2026

FraudShield uses an Agentic AI-driven approach for real-time financial transaction fraud investigation, increasing accuracy and scalability. The solution addresses high false positives, delayed detection, fragmented investigation processes, inconsistent data across channels, and regulatory reporting pressure in financial fraud operations.

Organization: HCLTech
Industry: Finance
Location: India
Published: January 2026

Reported outcomes

Strategic outcomes

Risk & complianceImproved fraud investigation accuracy and compliance readinessCost efficiencyReduced manual investigation workCustomer experience & trustMore timely and professional fraud communicationsScale & capacityScalable real-time fraud lifecycle processing

Primary read

Use case focus

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1Workflow automation

Traditional fraud detection systems rely on rule-based mechanisms that struggle with sophisticated fraud patterns and produce high false positives.
Legacy system complexity and siloed data make it difficult to investigate suspicious transactions in real time.
Manual processes, long investigation timelines, and weak reporting workflows hurt customer trust and create compliance risk.

FraudShield uses a multi-agent agentic architecture for fraud operations.
A risk evaluation agent scores incoming transactions using metadata, behavioral signals, and external threat feeds to prioritize cases.
A deep investigation agent correlates user profile, transaction history, device and location checks, merchant reputation, and known incident data to produce concise investigation records.
A customer engagement agent crafts context-aware messages using sentiment signals and captures customer replies.
A reporting agent compiles compliance-ready investigation reports and preserves a full decision trace for auditors and regulators.
The architecture uses AWS GenAI services, Amazon Nova Pro, Anthropic Claude, CrewAI orchestration, and transaction/log storage for scalable real-time processing.

Technologies

Amazon Nova Pro Anthropic Claude 3 CrewAI AWS GenAI

The article says the approach leads to far fewer noisy alerts reaching investigators.
It says only high-confidence cases are escalated and false positives drop because decisions include context.
It says customers get timely, professional communication and callbacks and manual outreach are reduced.
It says reporting is consistent, repeatable, and far less manual.
The article characterizes the approach as improving accuracy, scalability, and compliance for modern fraud prevention.

Architecture

FraudShield is presented as a multi-agent agentic architecture: a risk evaluation agent prioritizes transaction alerts, a deep investigation agent correlates contextual signals across data sources, a customer engagement agent generates sentiment-aware communications, and a reporting agent builds audit-ready fraud reports. The article says the architecture is built on an AWS GenAI tech stack and uses CrewAI orchestration, Amazon Nova Pro, Anthropic Claude, storage for transaction metadata and investigation logs, and external search/location services.

Implementation partners2

CrewAI Anthropic

Sources & evidence1

Groundedness: 5/5Type: Blog PostPublished: Jan 8, 2026Publisher: HCLTechEvidence: VendorConfidence: Medium

Primary source

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