Automations
This pillar focuses on custom compliance workflows that monitor transaction flows, customer networks, sanctions data, and anomalous behavior continuously rather than through batch-based reviews. The pages should show financial buyers how graph models, case-routing logic, and audit-ready reporting systems come together to lower false positives, accelerate investigations, and strengthen regulatory posture.
This foundational page details a custom, end-to-end workflow architecture that ingests transaction, customer, and external data streams to monitor risk, prioritize alerts, and route cases continuously. It shows financial institutions how to replace batch-based reviews with a real-time orchestration layer, reducing false positives, accelerating investigations, and creating a defensible audit trail for regulators.
This page explains a custom workflow where specialized agents orchestrate the ingestion, matching, and alerting logic for global sanctions and watchlists across customer and transaction data. The architecture reduces manual screening overhead, improves match accuracy with contextual reasoning, and ensures continuous updates are applied without operational downtime.
This page covers the implementation of a custom ML-driven workflow that establishes individual customer behavioral baselines and flags deviations in transaction patterns, login activity, or communication for investigation. It details the data pipeline, model serving, and alert fusion architecture required to move beyond rule-based systems and reduce false positives.
This page outlines a custom workflow that automatically screens new and existing customers against PEP databases, monitors for changes in political status, and triggers Enhanced Due Diligence (EDD) reviews. It explains the integration of external data vendors, the logic for risk re-assessment, and the case creation architecture to maintain ongoing compliance.
This page details a custom workflow where AI agents continuously scan global news, regulatory filings, and other unstructured sources for negative mentions of clients or linked entities. It covers the NLP pipelines, entity resolution, and risk-scoring logic that automates what is typically a manual, time-consuming research task for analysts.
This page explains a specialized workflow architecture for monitoring international wire transfers, focusing on high-risk corridors, nested correspondent relationships, and potential sanctions evasion patterns. It details the integration with SWIFT and payment messaging systems, the application of jurisdiction-based rules, and the alert prioritization logic.
This page describes a custom workflow where agents extract, validate, and continuously verify ultimate beneficial ownership (UBO) data from corporate documents and registries. It addresses the challenge of opaque corporate structures, detailing the document AI, graph analysis, and discrepancy alerting needed to automate a critical but manual CDD component.
This page focuses on the post-detection orchestration layer, where a multi-agent system scores, clusters, and routes AML alerts based on risk, customer context, and investigator capacity. It explains how this workflow reduces analyst fatigue, ensures high-risk cases are addressed first, and integrates with case management systems like Actimize or Oracle Mantas.
This page details a custom workflow that employs machine learning and reasoning agents to triage and dismiss clearly false alerts before they reach an analyst's queue. It covers the feedback loop from investigator decisions, the model retraining pipeline, and the governance controls required to safely automate this high-volume, low-value task.
This page explains how to build a custom workflow that dynamically assigns new AML cases to investigators based on expertise, workload, and case complexity. It details the agentic logic for evaluating case attributes, querying team availability via systems like ServiceNow, and ensuring equitable distribution to optimize investigation throughput.
This page describes a workflow where an AI agent automatically gathers and synthesizes customer profiles, transaction histories, prior alerts, and external intelligence into a preliminary case dossier. It shows how this automation shaves hours off each investigation's start-up time, allowing analysts to begin with context rather than data collection.
This page outlines a custom workflow that transforms investigation findings, timelines, and evidence into a structured, narrative SAR draft compliant with FinCEN guidelines. It details the LLM orchestration, template management, and human-in-the-loop review steps required to cut SAR preparation time from hours to minutes while maintaining quality.
This page explains the architecture for a workflow that continuously recalculates customer risk scores based on transaction behavior, external events, and refreshed KYC data. It moves beyond static annual reviews, detailing the real-time data ingestion, scoring model invocation, and alerting logic that triggers CDD or EDD actions proactively.
This page details a systematic workflow that automates the periodic review and refresh of customer information, financial activity, and risk profiles. It covers the scheduling logic, data aggregation from core banking systems, exception identification, and task generation for relationship managers, ensuring compliance without manual checklist management.
This page describes a complex workflow for high-risk customers, where agents coordinate the collection of source of wealth/funds documentation, perform adverse media deep-dives, and prepare summary packages for senior approval. It automates the labor-intensive EDD process, ensuring consistency and freeing up specialized investigators for analysis.
This page covers a custom onboarding and refresh workflow that uses computer vision and document AI to verify IDs, utility bills, and corporate registries for authenticity and tampering. It details the integration with vendor APIs, the logic for flagging discrepancies, and the handoff to human review for ambiguous cases, speeding up verification cycles.
This page explains a workflow where agents automatically construct and update network graphs from transaction data, account linkages, and corporate structures to uncover hidden relationships. It details the graph database architecture, anomaly detection on network patterns, and how this provides investigators with visual intelligence that manual methods cannot.
This page details a workflow that applies unsupervised ML to transaction data to automatically identify clusters of similar, potentially suspicious behavior (e.g., structuring, layering). It explains the pipeline for feature engineering, model inference, and the creation of consolidated alerts for investigator review, moving beyond pre-defined typologies.
This page describes a workflow where agents perform automated open-source intelligence (OSINT) gathering on subjects of interest, querying public records, business registries, and news sources. It details the orchestration of browser automation tools, data synthesis, and report generation to augment human investigators with scalable, external data collection.
This page covers a workflow that ingests and analyzes emails, chat logs, and scanned documents within an investigation using NLP and entity extraction. It shows how to build a RAG-based system that allows investigators to query vast amounts of unstructured text quickly, uncovering leads and connections that would otherwise be missed.
This page explains a workflow designed to detect specific money laundering methods (e.g., trade-based, smurfing) by encoding typology knowledge into a combination of rules and ML models. It details how to operationalize financial intelligence unit (FIU) guidance into a continuously monitoring system that flags complex, multi-step schemes.
This page outlines an end-to-end workflow that formats, validates, and submits Currency Transaction Reports (CTRs) and Suspicious Activity Reports (SARs) directly to regulatory bodies. It covers the integration with case management systems, the business logic for filing deadlines, and the secure transmission architecture to eliminate manual filing errors and delays.
This page details a workflow that automatically pulls data from disparate AML systems (transaction monitoring, screening, case management) into a unified compliance dashboard. It explains the ETL architecture, data normalization, and reporting layer that gives Chief Compliance Officers a real-time view of program effectiveness and key risk indicators.
This page describes a critical governance workflow that automatically logs all decisions, model scores, and user actions within the AML ecosystem to create a defensible audit trail. It details the event-sourcing architecture, cryptographic hashing for integrity, and report generation for internal audit and regulatory examinations.
This page explains a workflow that continuously monitors the performance of transaction monitoring and screening models for drift, bias, and degradation. It automates the back-testing, benchmarking, and report generation required by model validation policies, reducing the manual burden on quantitative teams and improving model governance.
This page details a workflow for retail/digital banks that unifies signals from fraud detection (e.g., account takeover) and AML systems to identify complex financial crime. It explains the architecture for sharing alerts, orchestrating a joint investigation response, and preventing siloed analysis that misses cross-domain threats.
This page outlines a custom workflow for monitoring high-velocity, low-value transactions on platforms like Venmo or Zelle. It focuses on detecting mule accounts, scam patterns, and structuring behavior, detailing the real-time data ingestion from payment rails and the lightweight, high-speed analytics required for this domain.
This page describes a workflow tailored for crypto VASPs, where agents monitor blockchain transactions, link wallet addresses to exchange customers, and flag patterns indicative of mixing, layering, or sanctions evasion. It covers the integration of blockchain analytics APIs and the unique risk-scoring logic needed for digital asset compliance.
This page explains a workflow for commercial banks that automates the monitoring of trade finance instruments for red flags associated with trade-based money laundering (TBML). It details the analysis of shipping documents, invoice discrepancies, and dual-use goods screening, integrating with trade platforms like CGI's Trade360 or SAP.
This page focuses on corporate banking, detailing a workflow that monitors complex treasury activities like cash pooling, intercompany loans, and notional pooling for illicit fund movements. It explains the integration with treasury management systems (TMS) and the logic to detect circular transactions or masking of fund origins.
This page covers a workflow for investment banks to automate the risk monitoring of prime brokerage clients, analyzing trading activity, collateral flows, and wire transfers for potential market abuse or money laundering. It details the integration with order management systems and the need for low-latency analysis of large trading datasets.
This page details a critical workflow for managing the ongoing risk of correspondent banking partners. It automates the collection of partner due diligence, transaction volume analysis, and news monitoring, triggering reviews when risk indicators change. This reduces the manual burden of managing these high-risk, strategically important relationships.
This page describes a workflow for wealth managers that monitors the complex, cross-border investments and transactions of HNWI clients for unusual activity. It details the aggregation of data from portfolio management systems, the application of risk-based behaviors specific to private banking, and the discreet alerting mechanisms required.
This page outlines a workflow for trust companies and banks that automates the oversight of fiduciary accounts, ensuring transactions align with the trust deed and identifying potential misuse of funds. It covers the ingestion of legal documents, the rule-based monitoring of disbursements, and exception reporting to trust officers.
This page details a workflow for FinTechs and MSBs to automate the oversight of their own sub-agents or merchants. It monitors for excessive chargebacks, suspicious transaction patterns, and sanctions exposure, generating risk scores and triggering compliance reviews to manage their own downstream regulatory risk.
This page explains a workflow for non-financial sector compliance, automating the monitoring of real estate transactions handled by title companies for signs of layering or illicit fund insertion. It details the analysis of closing disclosures, wire transfer records, and buyer/seller profiles against PEP and sanctions lists.
This page covers a workflow for art galleries, jewelry dealers, and auto dealerships subject to AML regulations. It automates customer identification, source of wealth checks for high-value purchases, and reporting of cash transactions over thresholds, integrating with point-of-sale and CRM systems.
This page addresses a foundational technical challenge, detailing a workflow that automatically extracts, cleans, and links customer and transaction data from core banking, CRM, and legacy systems into a unified golden record. It is a prerequisite for effective surveillance, focusing on the data pipeline architecture and entity resolution logic.
This page details an advanced workflow where agents analyze alert outcomes and investigator feedback to automatically adjust transaction monitoring rule thresholds and model parameters. It creates a closed-loop system that optimizes detection rates while controlling alert volumes, moving towards a self-tuning compliance program.
This page describes a workflow that continuously evaluates the effectiveness of AML detection rules, identifying those that are redundant, obsolete, or generating excessive false positives. It automates the analysis, recommendation, and governance approval process for retiring or modifying rules, improving system efficiency over time.
This page outlines a GRC-focused workflow that automates the calculation of inherent AML risk across products, geographies, and client types, and then factors in control effectiveness to determine residual risk. It aggregates data from across the compliance stack to generate dynamic risk heatmaps for management reporting.
This page details a workflow that automates the testing of key AML controls (e.g., screening hit resolution rates, SAR filing timeliness) by pulling evidence from system logs and case files. It schedules tests, generates findings, and tracks remediation, transforming a manual, sample-based audit process into a continuous assurance program.
This page explains a converged security operations workflow where a single orchestration layer processes alerts from both fraud and AML systems, correlates events, and initiates a coordinated response. It details the architecture for breaking down silos between security and compliance teams to combat modern, blended threats more effectively.
This page focuses on a KYC/onboarding workflow that uses AI to identify synthetic identities or first-party fraud during account opening. It details the analysis of application data, cross-referencing with external sources, and risk-scoring logic that prevents bad actors from entering the system, a critical first line of AML defense.
This page describes a specialized workflow that continuously monitors for signs of synthetic identity usage post-onboarding, such as fragmented credit histories or anomalous transaction patterns. It employs agents to investigate linked accounts and behaviors, automating the detection of this growing and complex fraud/AML risk.
This page details an advanced workflow designed to identify sophisticated sanctions evasion techniques, such as the use of front companies, layered transactions through non-sanctioned jurisdictions, or obfuscated vessel ownership. It employs network analysis and pattern recognition on transactional and corporate registry data to flag high-risk scenarios.
This page explains a workflow that analyzes international trade data—invoices, bills of lading, customs forms—for mismatches in price, quantity, or quality of goods. It automates the red flag detection for over/under-invoicing and phantom shipments, a traditionally manual process critical for banks and trade platforms.
This page covers a workflow that uses network graph analysis and AI to identify clusters of accounts associated with shell companies and trace the layered movement of funds designed to obscure origin. It details the algorithms for peeling back transactional layers and presenting simplified visualizations to investigators.
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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