💳 Fintech & Banking Solution

AI Security for Fintech APIs

Secure financial AI integrations, ensure PCI-DSS compliance, and control high-volume transaction costs.

Fintech Security Challenges

PCI-DSS Compliance

Regulated financial platforms must ensure cardholder data (CHD) and sensitive authentication data (SAD) never leaks to external LLM provider environments. RaksHex redacts sensitive info at the API level.

Real-time Fraud Detection in AI Agents

Autonomous financial agents can be manipulated via prompt injection to transfer funds or override transaction thresholds. RaksHex monitors tool parameters in real time.

LLM Cost Attribution at Scale

High-volume transactional LLM calls create massive cloud spend. RaksHex tracks cost attribution per feature, model, and user session.

Frictional Case Studies

PayWise (Neobank)

Mitigating Prompt Injections in AI Chatbots

The Challenge:PayWise deployed a customer support agent with access to user account APIs. Attackers attempted direct prompt injections to execute unauthorized transfers.

The Solution:By routing LLM calls through RaksHex runtime firewalls, PayWise immediately blocked 99.8% of prompt injection strings and put high-value transfers behind AgentGuard approvals.

The Result:Zero transaction fraud incidents since integration, and full compliance audit log retention.

InsuTech (Insurance)

Securing Underwriting RAG Pipelines

The Challenge:InsuTech's underwriting agent queried a vector store containing patient bank details and PAN data, risking leakage to public models.

The Solution:RaksHex deployed an egress filter redacting Aadhaar, PAN, and bank accounts in real time before data reached external model gateways.

The Result:100% compliance with India DPDP Act and zero leakage of customer PII.

TradeFlow (Brokerage)

Optimizing High-Volume Trading LLM Costs

The Challenge:TradeFlow's financial advisor agent ran recursive loops that bloated token spend by $12,000 in a single week.

The Solution:TradeFlow configured RaksHex's cost attribution engine to trace thinking tokens and automatically shut down runaway loops using a budget kill switch.

The Result:Reduced LLM API costs by 64% within the first month of deployment.

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