人工智能+工具方案目錄

1. Solution Overview & Business EffectivenessWhat is FRAML? FRAML (Fraud and Anti-Money Laundering) is a next-generation, AI-enhanced graph-powered intelligence platform designed to combat financial crime through advanced network analysis, real-time pattern detection, and intelligent case management.

Core Technology Differentiator: Graph + AI Unlike traditional rule-based AML systems that operate on tabular data, FRAML leverages Ultipa's high-performance graph database to: - Discover hidden relationships across customers, accounts, transactions, and properties through multi-hop traversal (up to 10 hops) - Detect complex money laundering patterns (layering, structuring, circular flows) that are invisible to traditional systems - AI-powered insights for risk assessment, report generation, and pattern recognition - Real-time monitoring with sub-second query performance on billion-scale datasets

Use Case 1: Banking & Financial Institution AML Compliance Challenge: Banks process millions of transactions daily, struggling with high false positive rates (95%+) and manual investigation bottlenecks.

FRAML Solution: - Real-time transaction monitoring with graph pattern detection - Entity resolution to consolidate duplicate customer records - 12 China-specific AML models based on real cases from Tianjin Rural Commercial Bank: - Concentrated Inflow - Dispersed Outflow (集中转入分散转出) - Bidirectional Circular Flow (资金双向循环) - Corporate via Personal Account (企业流水通过个人账户) - Volume Exceeds Business Scale (资金规模远超经营) - High-Risk Region Accounts (高风险地区账户) - Nighttime Cash Activity (夜间现金快进快出) - Telecom Fraud Detection (电信诈骗)Benefits: - 70% reduction in false positives - 80% faster investigation time - Automated SAR/CTR/FINTRAC regulatory reporting - Real-time suspicious activity alerts Cost Savings: $2-5M annually per mid-size bank through reduced manual review

Use Case 2: Financial Intelligence Units (FIUs)Challenge: Agencies need to analyze complex financial networks across multiple institutions to identify money laundering and terrorist financing.

FRAML Solution: - Cross-institutional network analysis linking entities across banks - UBO (Ultimate Beneficial Owner) chain discovery through ownership graph traversal - Sanctions screening integration with PEP/watchlist matching - Regulatory-compliant reporting (FINTRAC STR, LCTR, CDR, EFT templates built-in)Benefits: - Complete visibility into ownership structures - Automated PEP and sanctions screening - Standardized regulatory report generation with AI assistance - Evidence-grade audit trails for prosecution

Use Case 3: Loan Fund Misappropriation Detection Challenge: Banks struggle to track whether loan funds are used for stated purposes, leading to fund misappropriation and increased NPL (Non-Performing Loan) rates.

FRAML Solution: - Loan fund flow tracking from disbursement through final usage - Usage vs. stated purpose analysis with AI-powered risk scoring - Borrower 360° profile with network visualization - Early warning indicators (payment delays, covenant breaches, rating changes)Benefits: - Proactive detection of fund misappropriation - Reduced NPL rates through early intervention - Comprehensive loan portfolio risk analysis - Compliance with credit supervision requirements

Use Case 4: SME Income Verification Challenge: Small/Micro enterprises often manipulate income records through fake invoices and circular transactions.

FRAML Solution: - Revenue pattern analysis identifying anomalies - Fake invoice detection through transaction network analysis - Circular transaction identification using graph cycle detection - Income authenticity scoring with AI modelsBenefits: - Reduced credit risk from income manipulation - Faster loan approval for legitimate businesses - Evidence-based verification reports