Enterprise 8-Agent Deal Processing System with Market Intelligence

Core Logic

Uses Logistic Regression + Monte Carlo Simulation algorithm with O(n) complexity where n = credit factors. Capabilities include credit_bureau_integration, tradeline_analysis, probability_of_default, risk_tier_classification, and score_factor_generation. Sub-tasks: Fetch Credit Bureau Data, Parse Tradelines & Payment History, Calculate Probability of Default, Determine Risk Tier & Approval, Generate Score Factors. Fetches credit bureau data (Experian), parses tradelines and 24-month payment history, runs logistic regression models to calculate probability of default with 95% confidence, determines risk tier (super prime to deep subprime), calculates maximum approved amount, and generates score factors explaining the credit decision.

Core Logic

Uses Isolation Forest + Velocity Analysis algorithm with O(n log n) complexity for anomaly scoring. Capabilities include behavioral_feature_extraction, anomaly_detection, velocity_analysis, identity_verification, and ofac_screening. Sub-tasks: Extract Behavioral Features, Run Isolation Forest Model, Perform Velocity Checks, Verify Identity Elements, Generate Fraud Risk Score. Extracts 8 behavioral features from the application, runs Isolation Forest anomaly detection model, performs 5 velocity checks within configurable time windows, verifies identity elements (name, address, SSN, DOB, OFAC), and generates a composite fraud risk score with clear recommendation (proceed, manual review, or decline).

Core Logic

Uses Linear Programming + NPV Optimization algorithm with O(n * m) complexity for scenario generation. Capabilities include financing_calculation, scenario_generation, constraint_optimization, payment_optimization, and profitability_analysis. Sub-tasks: Calculate Financing Parameters, Generate Payment Scenarios, Apply DTI/LTV Constraints, Optimize Monthly Payment, Rank & Recommend Scenario. Calculates all financing parameters, generates 6 payment scenarios (finance and lease options across multiple terms), applies DTI/LTV/PTI constraints, uses linear programming to optimize monthly payments within constraints, ranks scenarios by customer fit and dealer profit, and recommends the optimal scenario with pros/cons analysis.

Core Logic

Uses TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) algorithm with O(n * m) complexity where n=lenders, m=criteria. Capabilities include lender_querying, decision_matrix_normalization, criteria_weighting, ideal_solution_calculation, and closeness_coefficient_computation. Sub-tasks: Query 12 Automotive Lenders, Normalize Decision Matrix, Apply Criteria Weights, Calculate Ideal Solutions, Rank Lenders by TOPSIS Score. Queries 12 automotive lenders simultaneously, builds a normalized 5x12 decision matrix with criteria weights, applies TOPSIS algorithm, calculates ideal and negative-ideal solutions, computes closeness coefficients, and ranks lenders with detailed rationale for the selection.

Core Logic

Uses Collaborative Filtering + Propensity Score Modeling algorithm with O(n * m) complexity for product-customer matching. Capabilities include customer_segmentation, propensity_scoring, affinity_analysis, bundle_optimization, and talking_point_generation. Sub-tasks: Segment Customer Profile, Calculate Propensity Scores, Apply Product Affinity Matrix, Optimize Product Bundle, Generate Talking Points. Segments customer profiles using behavioral clustering, calculates propensity scores for 7 product categories using collaborative filtering, applies product affinity matrix based on historical purchase patterns, optimizes product bundle composition, and generates personalized talking points and objection handlers for each recommended product.

Core Logic

Uses Rule Engine + Regulatory Database algorithm with O(n) complexity for rule evaluation. Capabilities include tila_compliance, ecoa_fair_lending, state_regulation_validation, ofac_screening, and disclosure_generation. Sub-tasks: TILA Compliance Check, ECOA Fair Lending Check, State Rate Markup Validation, OFAC Sanctions Screening, Generate Required Disclosures. Evaluates the deal against a comprehensive rule engine covering TILA disclosure requirements, ECOA fair lending compliance, state-specific rate markup validation, OFAC sanctions screening, generates all required disclosures, and produces adverse action notices when applicable with consumer rights information.

Core Logic

Uses Transformer + Time Series Forecasting (ARIMA) algorithm with O(n log n) complexity for market prediction. Capabilities include market_data_aggregation, competitor_analysis, rate_forecasting, ev_incentive_tracking, and dynamic_pricing_optimization. Sub-tasks: Fetch Real-Time Market Data, Analyze Competitor Pricing, Interest Rate Forecasting, EV Market & Incentive Analysis, Dynamic Pricing Optimization. Fetches real-time market data using Transformer models, analyzes competitor pricing within configurable radius, forecasts interest rate movements using ARIMA time series analysis, provides EV-specific insights including federal/state incentives and charging infrastructure, and generates dynamic pricing recommendations with floor/ceiling bounds.

Core Logic

Uses XGBoost + Behavioral Clustering + NLP algorithm with O(n*m) complexity for customer modeling. Capabilities include profile_analysis, behavioral_scoring, ltv_prediction, purchase_intent_analysis, and next_best_action_generation. Sub-tasks: Customer Profile Analysis, Behavioral Pattern Scoring, Lifetime Value Prediction, Purchase Intent Analysis, Next-Best-Action Generation. Analyzes customer profiles using XGBoost to determine buyer persona and financial health, conducts behavioral pattern scoring including urgency level and price elasticity, predicts lifetime value with confidence intervals, analyzes purchase intent stage, and generates prioritized next-best-actions with expected outcomes and success probability.