Efficient reconciliations: An underappreciated area

The reconciliations process in capital markets deserves additional focus, writes Dayle Scher, research principal at Celent, who identifies how robust reconciliation tooling can help meet new market needs for visibility into operations’ process efficiencies.

What happens when incorrect tradable cash balances and/or positions are reflected in portfolio accounting systems and order management systems? Errors, financial losses, and even regulatory penalties.

The reconciliations process in the capital markets industry safeguards against errors and inefficiencies that are possible post-trade. An efficient reconciliations process detects a multitude of issues related to corporate actions processing, reference data, securities lending, and transaction processing and settlement. Long seen as back-office problems, the process and the technologies used to support reconciliations merit fresh attention. Reconciliations are transforming in response to new technologies and new market needs for visibility into operations’ process efficiencies.

Financial institutions have an opportunity to embrace a new approach to reconciliations. Investing in robust reconciliation tooling offers capital markets firms the opportunity to rectify long-standing accounting or system errors, such as unsynchronised internal applications or incorrect reference data. To understand what will differentiate the laggards from the leaders, consider the factors currently impacting this critical accounting function.

Reconciliations overview

A reconciliation, as defined in the Celent report Reconcilable Differences, is “the action of comparing two or more sets of records, or data, to ensure they are in alignment, and further identifying (and resolving) any discrepancies between them.” Specifically:

  • The reconciliation typically matches an internal data set (e.g., the portfolio of assets held by an asset manager) to one more external source(s) of the same data (e.g., assets held by an outside broker or custodian bank).
  • Reconciliations may be performed for any characteristic of an account. Examples include cash balances, cost bases, holdings, payables and receivables, prices, and/or transactions.
  • The frequency of reconciliations depends on the type of portfolio or account, frequency of trades, and when an account closes its books. Typically, reconciliations are performed daily.

The reconciliations process is not a client-facing function. As such, it can be too easy for traders and portfolio managers to take for granted that their daily activities are accurate and up-to-date. The traditional reconciliations model relies on legacy architecture and manual processes that limit innovation and expose operations risks, including failed trades, incorrect reference data, and corporate actions activity that doesn’t agree between parties. Pain points include: challenges to receipt of accurate data, data capture, formatting requirements of the reconciliation tool, comparison and matching of normalised internal and external data, exceptions management, and investigation and mitigation of exceptions. Further automation is necessary to ensure that foundational data is correct daily.

Future-focused reconciliations

Thanks to the surging trading volumes, remote working trends, and staffing shortages that marked the Covid-19 pandemic, capital markets saw accelerated digitalisation. As a result, and in recognition of their potential to mitigate operational risk, reconciliations gained attention and investment.

Reconciliations past, present, and future

Multiple drivers are helping reconciliations gain fresh attention. These include the needs for:

  • Compliance, in light of a regulatory landscape that is increasingly focused on reducing the number of failing trades.
  • Cost optimisation, to help increase margins, by reducing costs and full-time employees who focus on non-revenue generating functions.
  • Diversification of asset classes, requiring new risk monitoring approaches.
  • Handling growing transaction volume, scaling as transactions grow across asset classes and as they cause explosive growth of data (structured and unstructured).
  • Legacy modernisation, to handle contemporary processing and settlement volumes.
  • Platform consolidation, to streamline reconciliation technologies and processes that are siloed (e.g., across business lines or regions).

FIs that are modernising their reconciliations process prefer solutions that are quick to implement, easy for business users to run and manage (without relying heavily on IT resources), and easy to maintain. With these parameters in mind, adoption of future-focused reconciliation technologies is growing. New technologies keep up with FIs’ evolving requirements by using cloud technology, shifting away from on-premises deployments to cloud-based software-as-a-service (SaaS) deployments, which help minimise upfront investments and hardware costs; offering self-servicing capabilities, allowing business users to onboard and maintain the reconciliation process without reliance on internal IT teams or vendor partners; and becoming asset class agnostic, handling reconciliations regardless of security type (including crypto-currencies).

Intelligent automation and DLT in reconciliations

Intelligent automation techniques—artificial intelligence, machine learning, and robotic process automation—are positioned to revolutionise data operations and reconciliations solutions. AI, the science of training computers to perform tasks mimicking human  reasoning, can efficiently and accurately analyse large volumes and different types of data, applying results to narrow, well-defined problems that are computation-intensive. ML, a subset of AI that learns and improves with experience, can apply past findings to new data to predict future events or to discover hidden patterns. RPA is software that can mimic human actions; easy to configure and implement, it is particularly useful for automating workflows.

Applied across the reconciliation process, intelligent automation can help with on-boarding new reconciliation (performing data quality checks, mapping data fields, setting up match rules), improving match rates (optimising existing match rules, identifying and suggesting new match criteria), and resolving breaks (automating break resolution processes with RPA, performing root cause analysis). Presently, AI and ML are being incorporated into reconciliation solutions as basic offerings or as premium functionalities.

Blockchain/distributed ledger technology also presents significant promise for reconciliations in capital markets. While legacy solutions depend on centralised technologies and external parties for the identification and resolution of exceptions, blockchain’s decentralised, distributed ledger can track and record financial transactions. Complex cases may still require additional reconciliation work, but DLT can aid the overall reconciliations process by promoting transparency, getting rid of the human role in verification, providing authenticity of a transaction at the time of recording by the relevant network, and more. Smart contracts, for example, may be triggered automatically when a programmed event (e.g., dividend, interest, and principal payments) and specific conditions are met, reducing the risk of exceptions. DLT is already in use for interbank reconciliations.

Leaders v. laggards

Innovation in reconciliations is moving ahead and proving its value. Financial institutions and solution providers alike should continue their transformative initiatives to revolutionise this industry.

Leaders in this space have an operational approach that prioritises shared services and a center of excellence, a technology approach that uses a single (or limited) third-party software solutions, a reconciliation rate of 90% or above, a fully automated alerting and notifications systems, a clearly-defined exception handling management process, automated daily reports with detailed status tracking for monitoring, and well-documented and archived reporting and documentation. Other FIs, perhaps less mature or smaller, may find their processes mired in old, manual, costly, and risky habits. As reconciliation automation technologies develop and become more efficient, so must financial institutions—or face the likelihood of being a laggard in this space.