BlocRecon – Vincilium

A data-first
approach to reconciliations

BlocRecon makes reconciliations far more strategic than it seems…

Transform business processes Create a golden-copy of data Build a real-time data backbone Harvest reconciled data sets

Generate

Generate new business opportunities

Decide

Improve decision making

Save

Significantly reduce the cost of operations

Speed Up

Enable faster information delivery

Comply

Improve regulatory and compliance

Solving the reconciliation problems is not just about the matching.

Transformation is required across the Whole Recon Value Chain.

Pre-recon Data Services

Extensive Data Servicing Capability including ELT and rule configuration

Matching

Sophisticated matching engine with multiple matching types

Post Recon Workflow

Robust post-recon workflow management and exception handling

Pre-recon Data Services

Extensive Data Servicing Capability including ELT and rule configuration

Solving For

Unclear/missing information for matching
Differences in data presentation
Delays in getting information
Receiving backdated transactions
Manual data verification Non-standard data formats

Matching

Sophisticated matching engine with multiple matching types

Solving For

Lack of auto-matching capability
No common identifiers
Complex matching scenarios like one to many, many to many
Matching in tolerances and fuzzy matching requirements
Duplicates in data sets
Multi-way recons needs

Post Recon Workflow

Robust post-recon workflow management and exception handling

Solving For

Manual break investigation and resolution
Inefficiencies due to lack of ability to automatically categorize, label, and assign ownership
Need of real-time distribution of reconciliation outputs (e.g. status, adjustments, reconciled data set)

Solving the reconciliation problems is not just about the matching

Transformation is required across the recon value chain

Solving For...

Unclear / missing information for matching
Differences in data presentation
Delays in getting information
Receiving backdated transactions
Manual data verification

Non-standard data formats
Lack of auto-matching capability
No common identifiers
Complex matching scenarios like one to many, many to many
Matching in tolerances and fuzzy matching requirements

Duplicates in data sets
Multi-way recons needs
Manual break investigation and resolution
Inefficiencies due to lack of ability to automatically categorize, label, and assign ownership
Need of real-time distribution of reconciliation outputs (e.g. status, adjustments, reconciled data set)

Revolutionizing Reconciliations with A Data-First Approach

Our Unique “Data-First” Approach To Reconciliations Guarantees Lower Operational Costs Through Extensive Pre-Recon Data Cleansing, A Sophisticated Matching Engine And A Highly Configurable Post Recon Workflow And Exception Handling

Rapid Customization Enabled By A Microservices-Based Architecture

Inbuilt Elt And Sophisticated Matching Engine For Standard And Multi-Way Reconciliations

Real-Time Reconciliation Of Dynamic Data Sets

Blockchain Powered Data Harvesting, Golden Record Creation And Immutable Audit

Post-Recon Workflow Automation To Assist With Investigations, Breaks Resolution, Adjustments Postings, And Outbound Communication

Revolutionizing Reconciliations with A Data-First Approach

Rapid Customization Enabled By A Microservices-Based Architecture

Inbuilt ELT And Sophisticated Matching Engine For Standard And Multi-Way Reconciliations

Real-Time Reconciliation Of Dynamic Data Sets

Blockchain Powered Data Harvesting, Golden Record Creation And Immutable Audit

Post-Recon Workflow Automation To Assist With Investigations, Breaks Resolution, Adjustments Postings, And Outbound Communication

Reconciliation process starts when data is available in Source Systems

Data Extraction & Ingestion

Data cleansing and standardization is vital part of any efficient reconciliation

Data Standardization

Supplementary data from external sources increases the auto-match rate, help with breaks investigation and allows automation of post-recon flows

Data Enrichment

Fully configurable business rules for data enrichment
Ability to lookup data across multiple sources
Live updates of recon outputs with latest available information
External data can be used for transformations, matching and post-recon workflows
Easy linkage of additional data sources to existing live processes

A highly sophisticated matching engine is capable of handling any-to-any reconciliations and multi-way data comparisons

Advanced Matching

Complex matching rules like one-to-many, many-to-many, duplicates identification, matching in tolerance etc
Multi-way reconciliation for comparing more than two sides
Real-time reconciliations of dynamic data sets with cancels and updates supported
Fully configurable rule hierarchies triggered by schedulers and data events

Manual effort is minimised with highly configurable post-recon workflows and exception management capabilities

Exceptions Management

Fully configurable exceptions management
Automated workflows rules can be triggered on whole recon or individual transaction level
Auto-labeling, assignment, and prioritization of exceptions based on pre-defined business rules
Predefined workflow actions conducting multiple tasks with a single mouse click
Maker-checker and audit on all manual actions

A data-first
approach to reconciliations

BlocRecon makes reconciliations far more strategic than it seems…