Overview

Intercompany reconciliation normally needs to deal with the dual challenge of practical issues; trade volumes, disparate systems and data and the reconciliation logic itself. The former can often take up more resource than the latter.

Typical Intercompany Reconciliation Process

Existing matching approaches are typically predicated on matching field values between trades and repeating across the various fields which are contained in the trades file. Field types often include dates; Notionals; Currencies; PVs; Product types; Source systems and Counterparty information.

Matching tests can be ‘intra-field’ (e.g. Trade datetrade1 = Trade datetrade2) or ‘inter-field’ (e.g. Pay Fixing DateTrade1 = Receive Fixing DateTrade2).

Issues:

  • Limited by poor data quality of key fields and inconsistent data reporting between products and systems
  • Choosing between potential match trades is difficult and often based on non-transparent rules
  • Accuracy is poor and does not easily adapt to changes in data set
  • Often emphasise match uniqueness which can be misleading

CPRA Reconciliation Application

Using CPRA’s data analytics platform, we can address both typical issues. The engine can easily acquire, clean and associate data allowing us to focus on the analytics.

An intelligent reconciliation where the business rules are built as modular blocks, allows the process to be layered in a clear manner. The matching engine is transparent to users showing the ‘best match’ as well as the matching confidence.

Contact CPRA Intercompany Reconciliation

Advantages

  • High accuracy of matches based on match quality not match uniqueness
  • Transparency and visibility over the match logic used to derive a given matched pair, increasing ease of audit and error tracing
  • Simple and intuitive application to operate
  • Very fast application run time (by a factor of five versus some client systems)
  • Flexible parameters but logic is under change control
  • Operational risk mitigation by reducing requirement for manual processes
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Case Study

Challenge

A European Banking Client required an urgent solution to its intercompany reconciliation process that dealt with 1.5 mm trades per month in 70+ systems. The existing intercompany reconciliation process was a combination of automated reconciliations and manual intervention which was slow, costly and unreliable. Manual processes had to be used on over a third of the trades. In addition, the existing automated process was both opaque and inflexible

Solution

Flexible and transparent analytics matching engine created to replace the client’s existing processes. Analytics logic built as modular blocks, allowing the process to be layered in a clear manner. Four phase matching and scoring process, with sequential relaxation of three control thresholds

Result

  • Match rate improved, match accuracy improved
  • Transparency and visibility over the match logic used to derive a given matched pair, increasing ease of audit and error tracing
  • Operational risk deficiencies resolved by significantly reducing requirement for manual processes
  • Reduced sensitivity to input data
  • Simpler and more intuitive application to use
  • Significantly improved overall application run time (by a factor of five)
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