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Financial Operations Automation Suite

Finance —Investment Operations / Back-Office Automation

Problem Description

Fintechs must continuously detect, investigate, and report suspicious activity across high-volume transactions. Manual rule checks, sanctions screening, and narrative-quality SAR drafting are slow and inconsistent. A coordinated network of Syncloop-powered agents automates end-to-end AML: risk signals from activity streams, sanctions/PEP/adverse-media screening, network analysis, case triage, and SAR narrations—while maintaining a clean audit trail.

Working of Template

  • Intake unstructured documents → structured extraction.
  • Validate extracted values against golden sources.
  • Reconcile transactions across internal and vendor systems; learn from exceptions.
  • Compile regulatory outputs and generate formats required by regulators.
  • Produce immutable, searchable audit trails for every action.

Benefits

  • Large reduction in manual handling and processing times.
  • Fewer downstreamerrors and regulatory mis-filings.
  • Faster exception resolution and self-improving recon rules.
  • Audit-ready evidence for regulators and internal stakeholders.

Agents Required

Agent Name: Document Intelligence Agent
Role & Capabilities: OCR, table detection, NER, confidence scoring, push structured payloads to /data/extracted.
Agent Name: Data Validation Agent (New)
Role & Capabilities: rules, cross-checks IDs, thresholds, and reference data; returns validation status and anomaly reasons.
Agent Name: Reconciliation & Exception Management Agen
Role & Capabilities: Rule-based + ML matching, exception categorization, root-cause tagging, API for human review.
Agent Name: Regulatory Reporting Agent
Role & Capabilities: Map validated data to regulatory templates, run schema validations, create submission bundles.
Agent Name: Audit Trail & Governance Agent (New)
Role & Capabilities: Append event ledger, generate audit packages for auditors, manage retention policies.
Agent Name: Orchestrator Agent (Syncloop Workflow)
Role & Capabilities: Trigger pipelines, maintain run state, route human approvals,

Syncloop API Usage

Endpoint Method Input Parameters Output Format
/data/upload POST { "sourceId", "documentType", "binaryBase64", "metadata" } { "docId", "status", "receivedAt" }
/data/extracted POST { "docId", "fields": { ... }, "confidence", "pageMeta" } { "extractionId", "docId", "validationNeeded": bool
/validate/record Posts { "extractionId", "fieldChecks": [...], "referenceKeys": {...} } `{ "validationId", "status": "valid
/reconcile/submit POST { "records": [ { "id","account","amount","date","source" } ], "reconProfileId" } { "reconId", "matchesCreated", "exceptions": [exceptionId,...] }
/reconcile/match Get ?reconId=...&threshold=0.8 { "matches":[{ "left","right","score" }], "exceptions":[...] }
/exceptions/lis GET ?status=pending&limit=50 { "exceptions": [ { "exceptionId","reason","evidence" } ] }
/exceptions/action POST `{ "exceptionId","action": "resolve escalate
/report/generate POST "reportType","period","sources":[...], "format":"xml csv
/report/validateSchema POST { "reportId","schemaVersion" } { "reportId","valid":bool, "errors": [] }
/audit/log POST "entityId","eventType","payload","actor","timestamp" } { "auditId","status" }
/workflow/start POST { "workflowId","inputs":{...} } "runId","status","startedAt" }
/workflow/status GET ?runId=... "runId","status","steps":[{ "step","status","startedAt","endedAt" }] }
/kb/query POST { "query", "context":[...] } { "results":[{"source","snip","score" }] }

Flow Summary

  1. Client uploads document to /data/upload. Orchestrator triggers Document Intelligence Agent.
  2. Document Agent extracts structured fields and posts to /data/extracted.
  3. Data Validation Agent calls /validate/record against Snowflake/Oracle golden sources; flags anomalies.
  4. Validated records are posted to /reconcile/submit. Reconciliation Agent finds matches; exceptions are created for uncertain matches
  5. Exceptions appear in /exceptions/list and are presented to Human Review Assistant UI with evidence. Operator resolvesvia /exceptions/action.
  6. Once reconciled and validated, Regulatory Reporting Agent is invoked via /report/generate, validates schemas, and prepares submissions.
  7. All events and decisions are appended via /audit/log. Orchestrator monitors via /workflow/status and enforces SLAs.
  8. Feedback from human resolutions updates Reconciliation Agent's learning model and mapping rules in Syncloop KB.

Optional Enhancements

  • Add an Anomaly Detection Agentusing time-series models for unusual trades/outliers.
  • Integrate Bank/Vendor APIsfor real-time settlement confirmations.
  • Add a Privacy/PII Redaction Agentto automatically redact transcripts for non-privileged viewers
  • Plug an Analytics Agentto track trends, exception root causes, and cost savings dashboards.
  • Use ensemble LLMs for improved contextual extraction fallback

Ideal (Key Performance Indicator) KPIs to Measure Success

  • Extraction Accuracy:% of fields correctly extracted (target: >98%).
  • Reconciliation Auto-match Rate:% transactions auto-matched (target: 85–95%).
  • Exception Resolution Time:median time from exception creation to resolution (target: <4 hours).
  • Regulatory Submission Errors: number of schema/validation failures per period (target: 0).
  • Manual Touch Reduction: % decrease in manual processing steps (target: 70–80%).
  • Downstream Error Reduction: % fewer data errors after validation (target: 90%)
Deploy This Template

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Who Can Use This
    • Custody and fund accounting teams.
    • Middle/back office operations in asset managers and custodians.
    • Reg-tech and compliance teams supporting reporting requirements.
    • Shared services or BPOs handling financial reconciliation.
Key Benefits
  • Speed:

    Dramatically reduces end-to-end processing time through automation.

  • Accuracy:

    Validation and reconciliation agents minimize human error and mis-filings.

  • Auditability:

    Immutable logs and evidence packages simplify audits and investigations.

  • Scalability:

    Modular agents scale horizontally as volumes grow.

  • Cost Efficiency:

    Lower labor costs and reduced regulatory remediation spend.

  • Continuous Improvement:

    Learning components reduce exceptions over time.

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