Reconciliation

Reconciliation software automates the comparison of financial data across different systems to identify discrepancies and exceptions. It eliminates the need for manual, spreadsheet-based reconciliation that traditionally consumed significant back-office time.

The software pulls data from trading systems, banks, custodians, and other sources, then applies business rules to match transactions and flag misalignments. Advanced platforms can handle real-time data feeds from unlimited sources, process millions of records daily, and use intelligent matching algorithms that work even with poor data quality.

Core functionality includes automatically gathering data, matching transactions using algorithms, and managing exception workflows. Modern platforms also learn from user decisions to improve accuracy over time while reducing false alarms.

For financial firms, reconciliation software is essential for risk management and regulatory compliance, ensuring that positions and obligations accurately reflect reality across all counterparties and systems.

Cost and time savings: Automated reconciliation eliminates manual spreadsheet work and can reduce reconciliation times by over 50%, turning week-long processes into tasks completed in hours or days.

Higher accuracy: Automated systems apply consistent logic across millions of transactions, eliminating human errors and inconsistencies from manual processing.

Real-time monitoring: Issues are flagged immediately rather than discovered days later through batch processing, enabling faster resolution of potential problems.

Improved compliance: Built-in audit trails and automated reporting make regulatory examinations easier by automatically documenting all reconciliation activities.

Better risk management: Real-time visibility allows teams to identify and fix potential settlement failures before they impact the business.

Scalability: Automated platforms handle dramatic increases in transaction volume without proportional cost increases, enabling faster innovation and new product launches without waiting for control processes to catch up.

Gresham specialises in real-time reconciliation software designed to handle complex scenarios across all asset types. Our solutions support both buy-side and sell-side firms. 

Bank reconciliation: Matches internal cash positions against bank statements and nostro accounts using multi-algorithm approaches that deliver up to 35% higher match rates than traditional methods.

Account reconciliation: Manages securities positions and complex derivatives exposures, supporting investment managers from straightforward position matching to sophisticated multi-custodian reconciliations.

Transaction matching: Compares trade details across counterparties and internal systems through N-way reconciliation that can integrate unlimited data sources in any format within a single interface.

Control Cloud: Centralizes investment management workflows by integrating positions, transactions, cash, collateral, and reporting in a unified platform that eliminates departmental silos.

Regulatory reconciliation: Ensures internal records align precisely with reports submitted to regulators and trade repositories.

Data quality issues cause major headaches for reconciliation teams. Common problems include incomplete records arriving from different systems, timing differences causing transactions to appear on mismatched dates, and formatting inconsistencies that block automated matching.

Volume and complexity create major scalability problems. Approaches that work fine for hundreds of daily transactions fall apart when volumes hit hundreds of thousands. New asset classes and trading venues multiply the complexity beyond what manual processes can handle.

Most firms run trading platforms, settlement systems, custody platforms and risk tools that don't talk to each other effectively. Each system maintains its own version of the truth, forcing reconciliation teams to manually bridge the gaps.

Real-time requirements have become essential, especially with T+1 settlement cycles. Traditional overnight batch processing can't accommodate compressed timeframes where problems need to be identified and fixed within hours.

Regulatory complexity adds another layer of difficulty. Firms must reconcile not just their internal positions but also what they've reported to various regulatory authorities across different jurisdictions with different formats and timing requirements.

A typical scenario involves reconciling securities positions between an investment firm's portfolio management system and their prime broker's records. The internal system shows 50,000 shares of Apple, while the prime broker shows 49,950 shares.

The reconciliation team investigates and finds a 100-share trade executed late in the day appears differently across the two systems. The internal system shows it as settled, while the prime broker shows it as pending. They also discover a 50-share dividend payment appears in the internal system but hasn't been reflected in the prime broker's records yet.

Further investigation reveals timing differences in how the systems handle corporate actions. The internal system applied the stock dividend immediately, while the prime broker waits for physical receipt of the shares. This explains part of the discrepancy, but the late-day trade represents genuine settlement risk requiring immediate attention.

The resolution process involves coordinating with multiple parties: confirming settlement status with the executing broker, verifying corporate action entitlements and establishing agreed-upon pricing sources. A manual investigation process shows why automated reconciliation has become so important for managing complex portfolios.

Automated workflow orchestration: Transforms manual processes into streamlined operations that automatically route exceptions to the right teams, eliminating the need for analysts to manually check multiple systems and maintain spreadsheets.

Exception prioritization: Categorizes exceptions by risk, value, or regulatory impact, flagging high-priority issues immediately while routing routine ones through standard workflows.

Root cause analysis: High-quality reporting highlights recurring issues from counterparties or upstream systems, enabling teams to address problems at their source rather than repeatedly handling downstream effects.

Integration automation: Eliminates manual data movement by connecting directly to source systems and automatically updating downstream systems without human intervention.

Real-time processing: Surfaces issues immediately rather than waiting for overnight batch cycles, enabling proactive resolution before problems impact deadlines.

Machine learning improvements: Learns from user decisions over time to improve matching accuracy and reduce false exceptions, creating ongoing efficiency gains where accuracy increases while operational overhead decreases.