Automations
This pillar addresses treasury workflows that forecast cash position, model short-term exposure, and trigger payment, borrowing, or supplier-term decisions before liquidity stress appears. Pages should show how custom ERP-connected workflows can reduce working-capital drag, improve treasury decision speed, and create a more proactive finance operating model.
This foundational page outlines the architecture for a custom, multi-agent system that continuously forecasts cash positions, models short-term exposure, and triggers payment, borrowing, or supplier-term decisions to prevent liquidity stress. It details the integration of ERP, TMS, and banking APIs with predictive models and orchestration logic (e.g., LangGraph) to reduce working-capital drag and create a proactive finance operating model. The implementation focus is on building a production-grade workflow with approval gates, exception routing, and real-time dashboards for treasury teams.
This page details a custom workflow where specialized agents autonomously gather data from ERP, bank feeds, and AP/AR systems to generate and refine a rolling 13-week cash forecast. It explains how the architecture reduces manual aggregation effort, improves forecast accuracy through ML-based variance analysis, and provides early warning signals for liquidity gaps. Implementation covers data pipeline design, agent collaboration patterns, and integration with treasury dashboards for actionable insights.
This page describes a custom-built system that automates the calculation, execution, and accounting of notional and physical cash pooling across multiple legal entities and currencies. It focuses on the business outcome of optimizing interest income and reducing external borrowing by creating a centralized liquidity view. The architecture involves real-time bank API integration, intercompany loan tracking, and automated journal entries into the general ledger, with strict controls for regulatory and policy compliance.
This page explains a custom automation that analyzes supplier invoices, internal cash forecasts, and dynamic discounting offers to autonomously decide which invoices to pay early for optimal ROI. It covers the business case of capturing supplier discounts to improve margins and strengthen supplier relationships. The technical blueprint includes OCR/LLM invoice data extraction, discount yield calculation logic, integration with AP systems, and routing for approvals above predefined thresholds.
This page outlines a workflow that uses AI agents to match invoices to POs and contracts, validate against budget codes, and schedule payments based on cash flow forecasts and supplier terms. It targets the reduction of manual AP workload, late payment penalties, and early payment inefficiencies. Implementation details include document understanding pipelines, rules engines for approval routing, and integration with treasury systems for optimal payment date calculation.
This page details a custom collections system where agents analyze customer payment history, credit scores, and real-time cash needs to prioritize collection efforts and personalize dunning communication. The business outcome is a measurable reduction in Days Sales Outstanding (DSO) and bad debt. The architecture combines CRM and ERP data, uses LLMs for communication drafting, and orchestrates outreach across email and messaging platforms with human escalation paths.
This page describes an automation workflow that uses machine learning to predict and apply customer payments to open invoices with high accuracy, even with incomplete remittance data. It eliminates the manual lockbox reconciliation process, accelerating cash visibility. The solution architecture includes parsing bank files, using historical patterns to train matching models, and providing a clean exception queue for finance staff to resolve only the most complex cases.
This page explains a custom system that monitors real-time cash positions against forecasts and automatically triggers drawdowns on revolving credit facilities or commercial paper issuance when pre-defined liquidity thresholds are breached. It focuses on ensuring liquidity while minimizing borrowing costs and manual intervention. The build involves integrating with bank APIs, embedding covenant checks, and creating audit trails for all autonomous borrowing decisions.
This page outlines a workflow that automatically sweeps excess cash into a ladder of money market funds, short-term securities, or other permitted instruments based on yield, risk, and liquidity parameters. It aims to maximize risk-adjusted returns on idle balances. The architecture includes connections to broker-dealer platforms, yield curve analysis, autonomous execution via APIs, and integration with the general ledger for straight-through processing.
This page details a custom system that continuously aggregates transactional and balance sheet data across ERPs to calculate net FX exposure by currency pair and autonomously execute hedging instruments like forwards or options. It reduces manual exposure gathering and improves hedge effectiveness. The implementation covers data extraction from SAP/Oracle, exposure netting logic, integration with treasury trading platforms, and compliance with hedging policy limits.
This page describes a workflow where AI agents monitor all treasury transactions (payments, trades, borrowings) in real-time against a digital policy rulebook, flagging exceptions for immediate review. It strengthens internal controls and reduces operational risk. The architecture involves parsing transaction feeds, using a rules engine to evaluate policy compliance, and routing violations to designated officers with full context for rapid resolution.
This page outlines a system that automates the cumbersome process of managing bank account documentation, signatory updates, and KYC requests across multiple banking partners. It reduces operational risk and frees up treasury staff from administrative tasks. The workflow includes a centralized document repository, agentic communication with bank portals (via RPA or API), and synchronization with internal HR systems for employee status changes.
This page provides a technical blueprint for a custom agent that bridges SAP FICO modules with external treasury management systems (TMS) and bank feeds to maintain a single, real-time source of truth for cash positions. It eliminates reconciliation delays and errors. Implementation details focus on BAPI/IDoc integrations, data validation logic, exception handling, and the orchestration layer needed to keep SAP and TMS data in sync autonomously.
This page details a mission-critical workflow that continuously reconciles transactional data between enterprise ERP systems (like Oracle Fusion, SAP) and Treasury Management Systems, identifying and resolving breaks without manual intervention. It ensures data integrity for accurate forecasting and reporting. The architecture involves change data capture, intelligent matching algorithms, and automated journal entry proposals to correct discrepancies.
This page describes a workflow that automates the reconciliation of high-volume general ledger accounts (e.g., cash, intercompany) and proposes necessary adjusting entries. It accelerates the month-end close and improves accuracy. The system uses rules and ML to match transactions, identifies unreconciled items, drafts journal entries with supporting documentation, and routes them through approval workflows before posting to the ERP.
This industry-specific page details a custom workflow for SaaS companies that models future cash flows based on subscription metrics (ARR, churn, expansion), billing cycles, and collection patterns. It provides predictable liquidity visibility crucial for growth planning. The architecture integrates CRM (Salesforce), billing platforms (Zuora), and ERP data to feed a forecasting engine that updates autonomously with each new contract or renewal.
This page outlines a healthcare-specific automation that predicts cash collection from patient receivables and insurance claims by analyzing claim status, payer behavior, and denial patterns. It improves cash flow predictability for hospitals and clinics. The workflow integrates with EHR and practice management systems, uses AI to estimate payment timing and amounts, and triggers follow-up actions on aging or denied claims to accelerate collections.
This page describes a workflow for construction and engineering firms that automates the drawdown of funds from project financing facilities based on certified work completed, while managing cash across multiple job sites. It optimizes working capital tied up in projects. The system integrates project management software, validates draw requests against contracts, executes funding transactions, and updates project-level cash forecasts.
This page details a proactive monitoring system that uses cash flow forecasts and real-time bank balances to predict potential overdrafts days in advance and triggers preventive actions like intraday funding transfers. It avoids costly overdraft fees and reputational risk. The architecture involves connecting to bank APIs for real-time balance feeds, forecasting models, and automated communication to treasury staff with recommended actions.
This page explains a workflow where agents continuously scan entity-level cash balances, identify idle funds exceeding target thresholds, and autonomously execute deployment instructions such as intercompany loans, debt paydowns, or short-term investments. It maximizes the utility of corporate cash. The build requires defining deployment rules, integrating with internal loan tracking systems, and executing transfers via banking APIs or TMS.
This page outlines a system that monitors accounts receivable aging and sales pipelines to flag dangerous concentration in a single customer or industry, triggering alerts for credit limit reviews or hedging actions. It mitigates liquidity risk from customer insolvency. The workflow aggregates data from ERP and CRM, calculates concentration metrics in real-time, and uses predefined rules to generate alerts and recommended actions for treasury and sales leadership.
This page details a high-volume automation that fetches bank statements via APIs, matches transactions with ERP entries using fuzzy logic and ML, and produces reconciled statements with exception reports. It eliminates a tedious, error-prone monthly task. Implementation covers secure bank connectivity, scalable matching engines, and a dashboard for finance to review and approve reconciliations, significantly reducing the accounting close cycle.
This page describes a workflow that automates the end-to-end process of drafting, submitting, and tracking Letters of Credit and bank guarantees. It reduces processing time from days to hours for international trade teams. Agents extract data from purchase contracts, populate bank application forms, submit via bank portals (using RPA/API), and monitor status, alerting stakeholders of any amendments or expiry dates.
This page goes beyond traditional models to detail a custom workflow that ingests internal financial data and external signals (e.g., commodity prices, exchange rates) to train and retrain ML models for more accurate cash flow predictions. It improves forecast reliability for strategic decision-making. The architecture covers data pipeline design, model training and deployment orchestration, and integration of predictions into the treasury management workflow.
This page explains a sophisticated workflow that ingests real-time macro data (interest rates, GDP forecasts, inflation) and uses agentic reasoning to adjust liquidity models and stress testing scenarios automatically. It helps treasury prepare for external economic shocks. The system involves connecting to data providers, parsing reports, and updating model parameters within the TMS or forecasting platform to reflect changing economic conditions.
This page details a compliance automation that continuously tests key SOX controls over treasury transactions (e.g., payment approvals, bank reconciliations, hedge accounting) and generates evidence for auditors. It reduces manual control testing labor and improves audit readiness. The workflow integrates with transaction systems, executes test scripts autonomously, flags control failures, and compiles documentation into a secure audit trail.
This page outlines a workflow that screens high-value treasury payments (intercompany, investments, vendor) in real-time against sanctions lists and monitors for unusual patterns indicative of money laundering. It strengthens the financial crime compliance program. The architecture involves payment message interception, integration with third-party screening services, and case management routing for suspicious activity that requires human investigation.
This cross-functional page describes a workflow where an AI agent reviews sales contracts during the negotiation phase to identify payment terms (e.g., extended dating, milestone payments) and assess their impact on working capital and cash flow. It empowers sales with data-driven guidance. The system uses document AI to extract terms, calculates the DSO and cash flow impact, and provides recommendations or requires treasury approval for non-standard terms.
This page details a workflow that ensures committed spend from procurement systems (like Coupa, Ariba) flows automatically into cash flow forecasts, giving treasury earlier visibility into future outflows. It closes a critical data gap for accurate forecasting. The architecture involves integrating P2P platforms with the TMS, normalizing purchase order and contract data, and updating forecast models in real time as commitments are made.
This page outlines a system that automatically incorporates payroll data (salary, bonus, tax payments) from HR systems like Workday into short-term cash forecasts. It improves forecast accuracy for one of the largest and most predictable cash outflows. The workflow securely extracts scheduled payroll amounts, accounts for variable components like commissions, and feeds the data into the cash forecasting model on a rolling basis.
This forward-looking page details the architecture for a custom workflow that uses smart contracts on a permissioned blockchain to automate complex payment obligations like intercompany settlements, dynamic discounting, and trade finance. It promises reduced settlement times, cost, and reconciliation effort. Implementation considerations include digital identity, integration with legacy ERP, and the governance model for deploying and amending smart contracts.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
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