AI Financial Services & Banking with Chinese LLMs

# AI credit risk assessment for SME loan application import requests loan_application = { "business_name": "Suzhou Precision Manufacturing Co.", "industry": "CNC machining", "years_in_operation": 7, "annual_revenue_yuan": 12500000, "loan_amount_yuan": 2000000, "loan_purpose": "Purchase 3 new 5-axis CNC machines for aerospace contracts", "existing_debt_yuan": 800000, "collateral": "Factory building (appraised 4.5M yuan), equipment lien", "financial_highlights": "Revenue grew 18% YoY, gross margin 32%, EBITDA margin 15%. Cash flow positive 5 consecutive years.", "red_flags": ["One late payment 14 months ago (supplier dispute, resolved)", "Key customer concentration: 35% revenue from single aerospace client"], "industry_outlook": "Aerospace parts demand growing 12% annually, domestic substitution policy favorable" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "deepseek-v4", "messages": [ {"role": "system", "content": "You are a credit risk analyst AI. Assess loan applications, calculate risk scores (1-100), recommend approval/conditions/rejection, and explain reasoning. Format as structured JSON."}, {"role": "user", "content": f"Assess this loan application: {json.dumps(loan_application, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# Real-time transaction fraud risk scoring import requests transaction = { "tx_id": "TXN-20260825-884721", "amount_yuan": 48500, "merchant": "Overseas Electronics Store (Hong Kong)", "merchant_mcc": "5732", "cardholder": {"age": 34, "account_age_months": 48, "avg_monthly_txn_yuan": 8200}, "device": {"new_device": True, "location": "Shenzhen", "vpn_detected": False}, "txn_pattern": { "time_since_last_txn_minutes": 3, "previous_txn_amount_yuan": 120, "previous_txn_merchant": "7-Eleven", "daily_txn_count_today": 2, "unusual_velocity": "Yes - 485x normal transaction size" }, "historical_flags": ["No prior fraud alerts", "Consistent spending pattern for 4 years"] } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "glm-4", "messages": [ {"role": "system", "content": "You are a fraud detection AI. Score transaction risk (1-100), classify as low/medium/high/critical, explain risk factors, and recommend action. Format as structured JSON."}, {"role": "user", "content": f"Assess fraud risk: {json.dumps(transaction, ensure_ascii=False)}"} ], "max_tokens": 1500 } ) print(response.json()["choices"][0]["message"]["content"])

# Regulatory compliance gap analysis import requests compliance_data = { "new_regulation": "CBIRC Notice 2026-14: Enhanced AML requirements for cross-border transactions exceeding 50,000 yuan", "regulation_summary": "Requires real-time monitoring, enhanced due diligence, 72-hour reporting window, and quarterly risk assessments for high-risk corridors.", "internal_policies": [ "Policy AML-2019: Threshold-based monitoring at 100,000 yuan", "Policy AML-2021: Manual review for cross-border, 5-day reporting", "Policy KYC-2020: Standard due diligence, annual refresh" ], "current_systems": ["Rule-based AML engine (2018)", "Manual compliance reporting"], "deadline": "2026-12-31" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "qwen3-235b", "messages": [ {"role": "system", "content": "You are a regulatory compliance AI. Analyze new regulations against existing policies, identify gaps, estimate implementation effort, and generate action plans. Format as structured JSON."}, {"role": "user", "content": f"Analyze compliance gaps: {json.dumps(compliance_data, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# Automated equity research report generation import requests stock_research = { "ticker": "600519.SS", "company": "Kweichow Moutai", "sector": "Consumer Staples - Alcohol", "latest_earnings": { "revenue_yuan_bn": 150.5, "revenue_growth": "+15.3% YoY", "net_profit_yuan_bn": 74.7, "net_margin": "49.6%", "eps_yuan": 59.45 }, "valuation": {"pe_ttm": 28.5, "pb": 9.2, "dividend_yield": "1.8%"}, "recent_news": [ "Direct-to-consumer digital platform launched, 23% of sales now online", "New production base in Guizhou expanding capacity by 15%", "Youth consumption trends show baijiu interest declining in tier-1 cities" ], "peer_comparison": {"Wuliangye PE": 22.1, "Luzhou Laojiao PE": 19.8}, "analyst_consensus": "Buy, target price 1850 yuan (current 1690)" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "deepseek-v4", "messages": [ {"role": "system", "content": "You are an equity research analyst AI. Generate structured investment reports with business overview, financial analysis, valuation, risks, and recommendation. Use professional finance terminology."}, {"role": "user", "content": f"Generate research report: {json.dumps(stock_research, ensure_ascii=False)}"} ], "max_tokens": 3000 } ) print(response.json()["choices"][0]["message"]["content"])

# Banking customer service chatbot with product recommendation import requests conversation = { "customer": {"segment": "mass affluent", "age": 42, "risk_profile": "moderate", "aum_yuan": 2800000}, "query": "I have 500,000 yuan sitting in my savings account earning 1.5%. What should I do with it?", "context": "Customer has no prior investment experience. Recently received year-end bonus. Has mortgage balance of 800,000 yuan at 4.2%.", "available_products": [ "Wealth management: 3.5-4.5% expected, 1-year lock", "Government bonds: 2.8%, liquid", "Mixed fund: 5-8% expected, moderate risk", "Mortgage prepayment: guaranteed 4.2% return" ], "compliance_constraints": "Must include risk disclosure. Cannot guarantee returns. Must assess suitability." } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "glm-4", "messages": [ {"role": "system", "content": "You are a certified wealth advisory AI for a Chinese bank. Provide personalized financial advice, recommend suitable products, include risk disclosures, and never guarantee returns. Be empathetic and educational."}, {"role": "user", "content": f"Customer query: {json.dumps(conversation, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# AML alert investigation and SAR narrative generation import requests aml_alert = { "alert_id": "AML-2026-08-25-004421", "customer": {"name": "Zhang Wei", "account_type": "corporate", "business": "Import/export trading", "account_age_months": 14}, "trigger_pattern": "Structuring: 47 transactions of ~49,800 yuan each over 12 days (just below 50K reporting threshold)", "total_amount_yuan": 2340600, "counterparties": [ {"name": "Unknown individual accounts", "count": 23, "relationship": "Unclear business purpose"}, {"name": "Offshore shell company", "count": 12, "relationship": "Registered in BVI, no visible operations"} ], "geographic_risk": "60% of counterparties in high-risk jurisdictions", "customer_explanation": "Claims payment for electronic components, but no invoices provided" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "qwen3-235b", "messages": [ {"role": "system", "content": "You are an AML investigation AI. Analyze alerts, assess suspicion level, identify red flags, and draft Suspicious Activity Report (SAR) narratives. Format as structured JSON."}, {"role": "user", "content": f"Investigate AML alert: {json.dumps(aml_alert, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])