Financial transaction

Securing reconciliation — finally — with a systematic approach

Reconciliation ensures that all parties have consistent and accurate information about a financial transaction and corrects any anomalies in that data. Systematically automating the reconciliation process can reduce costs, improve accuracy and minimize regulatory risk. It can also facilitate the adoption of new technologies, such as the tokenization of assets and the use of distributed ledgers.

Banks can, and we believe they should, achieve truly automated reconciliation rates of 90-95% in their reconciliations by creating new ecosystems of tools, processes and skills. This will significantly reduce their costs and, more importantly, improve accuracy and provide the real-time reconciliation and compliance that customers and regulators need while paving the way for continued digital transformation.

Opportunities for improvement

Problems with current reconciliation processes span technology, corporate culture, and organizational charts.

Fragmented legacy systems make it difficult to collect and analyze required data, as well as ensure data accuracy. Many banks see improving reconciliation as an IT project rather than a top-down strategic project, making it difficult to gain management support and user buy-in. Because they don’t know or have significantly underestimated how much they are spending on reconciliation, it can be difficult for banks to make a business case for reducing these expenses.

Business leaders are also reluctant to invest more in resolving reconciliation issues when previous attempts have failed. They may lack the skills or tools to manage the required data or bridge siled reconciliation processes or fear that normal transaction processing will be interrupted during the improvement action.

Many banks have chosen to implement workarounds such as robotic process automation (RPA) to increase their straight through processing (STP) rates. However, this only automates unnecessarily high reconciliation rates, without reducing the risk of errors or the manual effort required to identify the right team to resolve a conflict.

Automated reconciliations can be costly, take considerable time to resolve discrepancies, and can delay compliance with new and pending regulatory changes. They require more scrutiny than current processes, make it more difficult to meet future demands for real-time monitoring and compliance, and may prove to be a significant impediment to the adoption of T+1 settlement of transactions in the United States. United.

Building a better ecosystem

With so many challenges to overcome, a true overhaul of reconciliation requires a systematic approach. This includes end-to-end planning, improved management of more structured and unstructured data types, and streamlining and standardization of processes and controls. It also requires better orchestration of all stakeholders to evolve into a business ecosystem that can learn faster and from more cases on how to automate the reconciliation process.

Over time, we expect financial services organizations to increasingly use artificial intelligence to quickly recognize mismatches and learn from new cases (i.e., “fix once and fix forever”) to eliminate future occurrences and leverage distributed ledger technology to share the same view of truth among relying parties.

Five Steps to Improved Reconciliation

In our work with banks around the world, we have found the following useful in making significant improvements to levels of reconciliation automation.

First, create a data transformation layer that standardizes and enriches the data streams and data attributes used in reconciliation. Be sure to allocate adequate budget and development staff to address the inevitable data quality issues that will affect upstream users if left unaddressed. Also consider using cloud data platforms to allow for scalability and flexibility.

Second, build consensus among business users and IT teams around a standardized end-to-end target operating model that spans technology and process.

Third, apply intelligent process automation (IPA) to manual processes throughout the reconciliation process. Don’t forget to also apply this automated orchestration to the exception management workflow, which is a major driver of cost and effort in today’s reconciliation processes.

Fourth, implement real-time risk management through a reconciliation dashboard and reporting tools that ensure the efficiency of the new process, streamline regulatory compliance, and reduce the risk of operational losses and the cost of reconciliation. Analyzing data on the root cause of failures at a detailed level will help resolve problems at the source or automatically suggest solutions for them.

Finally, refine a plan to deliver the target operating model on time and within budget. This plan should specify critical details, such as reducing parallel execution times and decommissioning legacy systems, as well as automation, process, and reporting plans. Providing the desired long-term solution is as important as reducing the initial investment and meeting short-term cost reduction goals.

This process will have been successful if it provides an automated and flexible factory model to support automated reconciliations as business and regulatory requirements change.

A standardized, redesigned and straightforward reconciliation process does more than just reduce cost and risk. It provides a foundation for growth and improved competitiveness by providing better visibility into reconciliation success rates and regulatory status.


By Jerome Dumaine, Vice President, Banking and Financial Services and Global Head of Capital Markets Solutions Cognizant Technology Solutions