An AI agent tracks down the right person at a delinquent business, figures out the best way and time to reach them, and handles the entire collections conversation without a human lifting a finger.

Proximitty deploys autonomous AI agents that orchestrate the full collections lifecycle for delinquent business loans. The system begins with deep skip tracing—using NLP to parse public records, credit bureau data, email metadata, and unstructured documents to locate and verify borrower contact information. Once right-party contact is established, the agents use reinforcement learning and multi-armed bandit optimization to determine the optimal outreach channel (email, SMS, phone, letter), timing, tone, and escalation cadence for each borrower. The agents dynamically adjust strategies based on borrower response patterns, payment behavior, and real-time sentiment analysis of communications. All interactions are logged and audited against compliance rules across 100+ jurisdictions, with the system automatically flagging or halting outreach that would violate FDCPA, TCPA, or state-specific regulations. This eliminates the need for large manual collections teams while improving recovery rates and reducing regulatory risk.

It's like having a tireless, hyper-organized collections agent who never forgets a follow-up, always knows the rules in every state, and somehow always calls at the exact right moment.

The AI reads through messy tax returns, bank statements, and emailed financials and instantly turns them into clean, structured data that the servicing platform can act on.

Proximitty's document intelligence pipeline uses fine-tuned large language models to ingest and process the wide variety of unstructured financial documents that flow through business loan servicing—including tax returns, profit-and-loss statements, bank statements, accounts receivable aging reports, and ad hoc financial disclosures sent via email. The system performs document classification (identifying document type and relevance), named entity recognition (extracting borrower names, EINs, account numbers, revenue figures), and table extraction (parsing financial tables into structured schemas). These extracted data points feed directly into the servicing platform's decisioning layer, enabling real-time covenant monitoring, borrower health scoring, and automated trigger detection for loan modifications or escalations. The LLM-based approach handles the long tail of document formats and edge cases that traditional OCR and template-based extraction systems struggle with, providing a significant accuracy and coverage advantage. This capability is a core product differentiator, enabling Proximitty's customers to move from batch-processed, human-reviewed document workflows to continuous, autonomous financial monitoring.

It's like having a CPA who can speed-read every financial document a borrower has ever sent, instantly highlight the important numbers, and never misfile a single page.

The AI watches every signal from a borrower—payment patterns, financial health, even subtle behavioral changes—and raises a red flag weeks before a loan is likely to go bad, so the servicing team can step in early.

Proximitty builds and continuously retrains supervised machine learning models that predict the probability of business loan default at various time horizons (30, 60, 90 days). The models ingest a rich feature set that goes beyond traditional credit bureau data: real-time payment behavior patterns, extracted financial metrics from the document intelligence pipeline (revenue trends, cash flow volatility, receivables aging), borrower communication sentiment and responsiveness scores, macroeconomic indicators, and industry-specific risk factors. Anomaly detection models (isolation forests, autoencoders) run in parallel to flag unusual patterns—such as sudden changes in payment timing, communication frequency drops, or financial metric deterioration—that may not yet register in the primary scoring model. When a borrower's risk score crosses configurable thresholds, the system automatically triggers proactive servicing workflows: outreach to discuss restructuring options, automated loan modification proposals, or escalation to human servicing specialists. This predictive layer transforms loan servicing from a reactive, delinquency-driven process into a proactive, risk-managed operation, directly reducing losses and improving borrower retention for Proximitty's bank and fintech customers.

It's like a weather forecast for loans—instead of waiting for the storm to hit, you see the clouds forming weeks ahead and move everyone inside before it rains.