Improving the Reconciliation Lifecycle with Better Data

Improving the Reconciliation Lifecycle with Better Data

Posted on July 01, 2019 0 Comments

If you’re part of a business that’s involved in performing reconciliation or offering reconciliation services, you certainly understand the importance of automation on productivity, efficiency and client servicing. In part one of this series, we examined the challenges associated with implementing automation and the value of improved data-reconciliation processes. In this post, we look at the role of data integrity as a foundation for optimizing the reconciliation lifecycle and how MarkLogic customers are using a data hub to improve reconciliation processes and create business value.

Making improvements to existing reconciliation systems and processes involves recognizing the nature of the challenge. This is usually not an application-level problem. While applications are ultimately part of the solution, automated reconciliation requires the right data. Building better data systems requires the best possible data integrity and logic that governs its use.

Data Integrity Is the Fuel for Smooth, Accurate Reconciliation

Data is one of your biggest assets—perhaps the biggest. This is your policy information, billing records, claims data, trade records and account activity. Big data is ingested from internal and external sources. It can be structured, semi-structured or unstructured, and it originates from various sources such as messages, documents, spreadsheets and images.

Data used in reconciliation is also getting more complex. Growth in volumes of big data combined with more complex asset classes and unstructured data are increasing the demand for data-management solutions that can handle more complex data scenarios. The ability to rapidly integrate, update and master new data sources and models is critically important to keeping your reconciliation process running smoothly. When reconciliation applications are built on traditional relational databases, the ability to quickly and efficiently handle changes to underlying data and models can limit automation improvements.

Your reconciliation process takes data from these different sources, enriches with the reference data and other identifiers, validates, then sets matching criteria. From there, you match records, identify unmatched transactions and raise exceptions (see image below). The more exceptions you discover, the more time you spend on resolution, re-matching and application of adjustments. To reduce discrepancy rates and create automated reconciliation processes, all of the data needs to be complete, up-to-date and searchable as it moves through the reconciliation lifecycle.

Basic Data Reconciliation Lifecycle

If your business is manually reconciling data, you have a technology or process problem. For organizations that use technology applications to perform automated reconciliation and still have lower match rates or high exception process costs, a data-management problem exists. If you want to improve match rates and move toward a fully automated reconciliation process, you must address the integrity of your data sources.

So how do you get there? At a high level, this is the process for helping to ensure data integrity and establishing better data management required to improve reconciliation processes:

  • Using a data hub framework, harmonize and master siloed data—centrally managing data across all business units within your organization.
  • Address data-integrity issues for improved straight-through reconciliation by applying custom logic to validate and enrich data.
  • Apply semantics to capture and store data and metadata relationships and for building smarter reconciliation applications using that integrated, highly connected data.
  • Use data lineage and bi-temporality tools to trace and audit source data to ensure the data integrity required to improve reconciliation match rates.
  • Accelerate resolution of exceptions with sophisticated search functionality.

Using this approach to improve the integrity of data for reconciliation also creates the foundation for more effective data governance and development of advanced applications, such as an intelligent data matching and merging system built using machine learning methods.

Illustrating Real-World Solutions—Better Reconciliation in Action

MarkLogic offers the data management platform that enables finserv and insurance organizations to improve the integrity of their enterprise data. The MarkLogic® Data Hub, running in the cloud or on-premises, sits on top of the MarkLogic multi-model database—a NoSQL data platform that offers agility and scalability, without sacrificing the security and data consistency that enterprises require to improve data integrity, increase match rates and accelerate exception resolution.

Here are some examples of how we’ve helped companies digitally transform their data, streamlining their operations and enable more automated reconciliation processes.

Better Data Integration and Reconciliation Leads to Improved Customer Data Management at Large P&C Insurer

At a Fortune 500 property and casualty insurance carrier, MarkLogic was instrumental in enabling the modernization and digitization of their processes and downstream customer engagement. The insurer had been relying on massive Extract-Transform-Load (ETL) processes to match and merge customer data from multiple sources. Their existing Master Data Management (MDM) system and relational database platform was rigid and cumbersome, creating too many bottlenecks for achieving a comprehensive view of customer data in a reasonable timeframe.

Leveraging MarkLogic’s multi-model data-integration capabilities, the insurer can now more easily ingest, harmonize and reconcile its billing, customer, policy and claims data. MarkLogic’s flexible schema enables the insurer to store and access other data types, including CSV files, without the need for significant data transformation processes upfront. This helped the insurer clean up its customer data, ensure that it is perpetually up-to-date when in the hands of its service agents and achieve a comprehensive 360-degree customer view—one that fuels faster, more effective data reconciliation for improved customer engagement.

Using a Trade Data Hub Platform to Provide a Better Processing Solution for Banks

At a leading financial solutions platform provider, the costs of regulatory compliance had tripled in the years since the financial crisis. As a back-office operations and processing solution for financial institutions and wealth managers, the rigid structure of their legacy database model made it difficult to keep pace with regulatory changes and high data-reconciliation volumes.

Previously shackled by a rigid database schema, the provider built its trade management solution on MarkLogic to modernize its data architecture. As a result, the provider has improved risk management and compliance, increased operational and cost efficiency and enhanced client services and customer experience.

These examples highlight the benefits of improving data integration and integrity to enable automated reconciliation. Operational efficiency improves along with the client experience, so not only are you serving clients better and more cost effectively, you can leverage the enhanced user experience to generate loyalty and grow the client base.

Learn More

To learn more about how MarkLogic can help you simplify data integration to automate and accelerate your reconciliation process, visit us at

If you would like to find out how we can help your organization quickly and easily deploy a data hub to improve data integration and integrity for increased automation of operational and transactional processes, please contact us by clicking here.

Ed Downs

Ed Downs is responsible for customer solutions marketing at MarkLogic. He draws on his considerable experience, having delivered large-scale big data projects and operational and analytical solutions for public and private sector organizations, to drive awareness and accelerate adoption of the MarkLogic platform.


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