Reconciliation ensures the accuracy of a bank's financial records by comparing and matching two sets of records. This process confirms that payments made or received align with the bank's accounting system. Investigation involves handling reconciliation breaks, payment exceptions, and queries to ensure completeness and accuracy in accounting entries.

High costs and poor operational efficiency are the biggest challenges that banks face today, and they intensify as the global payment landscape evolves rapidly.

With the introduction of instant payment schemes, migration to ISO 20022 standards, and adoption of Swift GPI standards for cross-border payments, traditional reconciliation and investigation processes face increasing regulatory and cost pressures. Therefore, having robust capabilities to settle payments quickly and accurately is crucial.

Data management challenges

The challenges in reconciliation and investigation operations stem from the data platforms themselves not from complicated business rules or workflows. Some key challenges include:

• Data Ingestion: Gathering data from various sources like Core banking system, Payments, schemes, etc.
• Data Processing: Managing tasks like Extract, Transform, and Load (ETL) reliably, especially when dealing with legacy systems.
• Data Quality: Ensuring data accuracy and completeness, handling missing or invalid data.
• Data Standardization: Normalizing and standardizing data to fit required business models.

Effective reconciliation relies on having access to standardized data from multiple sources. Data should be readily available for investigation to quickly address reconciliation breaks and payment inquiries.

To manage data effectively, you need:

• A centralized data solution for automated data movement from diverse sources.
• Using ELT architecture for scalability and flexibility.
• A flexible architecture that adapts to schema changes with minimal configuration.
• Interface connectors designed to handle various data models with ease.
• Standardized data models aligned with business operations.
• Effective data storage strategies based on the reconciliation and investigation use case.
• A robust data warehouse and business intelligence tools for analytics and reporting.
• Implementing these practices streamlines data management, making it easier to onboard new products or adapt to changes. This allows business and technical experts to focus on core processes rather than data preparation.

Enhancing Operational Effectiveness in Reconciliation and Investigation with AI

At times, even with a good data strategy in place, there might be issues with the quality of the data that cannot be fixed at the source.

The matching rules cannot easily match or group two transactions that are related because the references are wrong or missing. This makes it harder to match transactions accurately and leads to more work needing to be done manually to fix the breaks.

AI and Machine Learning can help reduce the manual effort reduce the number of breaks that need to be investigated by the back-office team, and reduce the risk of errors that occur in manual reconciliations. It can free up valuable human resources as it allows for higher transaction volumes with a lower headcount.

Moving away from outdated monolithic systems

The use of legacy monolithic applications for reconciliation and investigations limits the agility and scalability and makes it hard for banks to keep up with new regulations and changes in the industry. Also, the on-premises deployment model keeps the costs high due to infrastructure and maintenance.

Cloud and software as a service (SaaS) solutions provide scalability, cost optimization and easy access to the latest features and capabilities with vendors managing the deployments and upgrades.

The benefits of AI

AI and ML bring a new dimension to reconciliations. Unlike traditional methods, AI is able to look at huge volumes of data, rapidly identify problems, make suggestions and turn those transactions over to analyst teams that are better suited to deal with them in-depth. In short, it takes on a task, humans are not well suited for, leaving them to add value where it’s most needed.

Advanced data matching algorithms can compare large volumes of financial data from different sources, identify patterns, detect anomalies, and reconcile transactions accurately, even in the most complex scenarios, enabling the system to learn from historical data, continuously improving matching accuracy and adaptability over time.

The primary advantage of using AI and ML technology for reconciliation is dramatically reducing or even eliminating the need for manual workflow, which can save effort, time and money. Additionally, it can reduce the risk of errors that occur in manual reconciliations, resulting in fewer financial losses.