An In-Depth Overview: How Finfone's Communications Surveillance Solution Operates

 

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comms surveillance architecture

The latest Approach - AI Powered Unified Architecture

The Evolution of Communication Compliance: Why the Current Surveillance Must Shift

 
  • Communication channels

  • Integration layer

  • Unified data foundation

  • Rule and analysis

  • Compliance operations

    While many organizations successfully capture communication data, they often struggle to reconstruct it for evidentiary purposes. When business interactions span multiple channels—including messaging, voice, email, and meetings—establishing a clear narrative becomes increasingly difficult. Critical information is frequently fragmented or siloed, only revealing its full context when unified. Consequently, compliance teams face a recurring challenge: although the data exists, it cannot be easily synthesized into a comprehensive, auditable view.

How it works

Identify both internal and external parties
To address this, the platform implements a comprehensive identity model encompassing both internal and external participants. Internal identities are mapped directly to the organizational structure—incorporating departments, roles, and employment status—to ensure precise accountability. External identities are consolidated from system integrations, diverse communication streams, and manual entries, supported by robust merging and deduplication logic. By linking these disparate identifiers to specific individuals across all channels, the platform facilitates granular, person-centric analysis of all communication data.
Where It Applies
The unified identity model provides a standardized framework across all compliance functions, including rule application, surveillance, inspection, investigation, and querying. By consolidating fragmented identifiers, it ensures that communication records are filtered, analyzed, and interpreted through a person-centric lens, enhancing accuracy and oversight.
Outcomes
This enables clear and consistent attribution of communication activities,improves the accuracy of investigation and analysis, and ensures that compliance outcomes can be clearly explained and supported during audit and regulatory review.

How it works

Archive and strategy
The platform guarantees robust data retention by integrating rigorous data integrity validation with comprehensive retention governance. Communication data is captured continuously across all channels and systematically validated to ensure total completeness. Simultaneously, highly configurable retention policies empower organizations to define precise parameters for data preservation, archiving, and deletion. These settings can be managed at the individual channel level, ensuring seamless alignment with diverse and evolving regulatory requirements.
Outcomes
This approach ensures that communication records remain comprehensive and reliable for auditing and investigative purposes. Furthermore, it facilitates the precise reconstruction of events while maintaining strict adherence to regulatory retention, audit, and record-keeping standards.

How it works

Context
Reconstructing individual records within full conversations and events, establishing meaningful relationships
Consistency
Applying unified standards to interpret communication behaviors across channels and scenarios
Interpretability
Ensuring that all analytical outcomes can be clearly explained and supported
Outcomes
Once structured, communication data becomes the foundation for rule evaluation, surveillance, and inspection. It allows risk detection and analysis to operate consistently and at scale, while also supporting business-side insights such as behavioral trends and communication patterns.

How it works

Keyword Identification
Detects predefined keywords and sensitive terms within communication content
Pattern Identification
Identifies structured information such as contact details, identity data, and transaction-related elements
Tagging
Applies classification labels to communication records based on predefined conditions, supporting subsequent filtering and analysis
Semantic Recognition
Uses defined risk indicators to identify risk expressions that are not easily captured through rule-based matching It allows risk detection and analysis to operate consistently and at scale, while also supporting business-side insights such as behavioral trends and communication patterns.
Outcomes
Through continuous rule-based identification, compliance teams can efficiently locate risk-relevant communication records within large datasets. This improves operational efficiency while ensuring consistency and explainability in the identification process.

It empowers your regulator

数据结构化
AI plays a key role in transforming unstructured communication data into usable information. For example: • Speech recognition (ASR) converts voice recordings into searchable text • Optical character recognition (OCR) extracts text from images and attachments These capabilities make previously inaccessible data available for analysis
Behavioral Recognition and Monitoring
In certain scenarios, AI is used to identify behavioral patterns and communication characteristics. For instance, in video surveillance contexts, AI can assist in detecting specific behaviors or anomalies, helping to surface situations that may require further attention.
Semantic Analysis in Inspection
This helps identify more complex or implicit risk scenarios that may not be captured through rule-based methods alone. AI provides context and insight, but does not determine compliance outcomes.
Analytical Support in Investigation
In investigation, AI is used to support more advanced analysis, including: • multi-dimensional association and relevance analysis • identification of key stages within an event • early identification of potential risk signals These capabilities help narrow down large datasets and assist in reconstructing how an event developed over time It allows risk detection and analysis to operate consistently and at scale, while also supporting business-side insights such as behavioral trends and communication patterns.
Human-Led Decision Making
Across all stages, AI serves as an analytical aid rather than a decision-maker. Final judgments—particularly those related to risk or compliance—are always made by human professionals. This ensures that outcomes are: • context-aware • explainable to regulators • aligned with internal policies and governance standards This improves operational efficiency while ensuring consistency and explainability in the identification process.

Trade Recontruction


Fifone’s Trade Reconstruction solution automates the creation of comprehensive audit trails by unifying transactions, communications, market data, and news. Using AI and machine learning, our platform consolidates structured and unstructured data across all asset classes into a single view, streamlining compliance with just a few clicks.

  • Structured and Unstructured Data Ingestion including

    Voice and text communications, emails, meetings and 3rd party surveillance data
  • Communication Filtering

    Filter irrelevant data basing predefined criteria
  • Communication Matching

    Match specific transaction with the unique ID (condition)
  • Reconstruction adjustment

    Manually fine tuning the final outcome

 

Report


Every regulatory analysis task generates a structured report that comprehensively documents the scope, review methodology, and final outcomes. These reports enable organizations to reconstruct the review process, facilitate internal validation, and address regulatory inquiries with transparent, defensible evidence.


 
 

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