Modernise Payment Reconciliations and Investigations to reduce operational cost and increase efficiencies

It is critical for financial institutions to have an efficient Reconciliation and Investigation (R&I) capability to minimise financial risk by identifying potential payment errors, discrepancies, or irregularities promptly and fixing them early in the payment lifecycle.

Reconciliation process ensures data integrity and accuracy of financial position of the bank by comparing and matching two sets of records to make sure payments made or received are accurate and consistent with what is recorded in the bank’s accounting system. The Investigation process involves procedures to handle reconciliation breaks (incomplete accounting entries), payment exceptions (repair and reprocessing) and queries (additional information on payments).

The biggest challenges faced by banks today for Reconciliation and Investigation services include higher operational costs and poor operational efficiencies. The challenges increase even more as the global payment landscape is transforming rapidly. As more countries introduce instant payment schemes, migrate to ISO 20022 standards, and incorporate Swift GPI standards for cross-border payments, traditional payment reconciliation and investigation processing faces increasing regulatory and cost pressures. So, it is more critical than ever to have robust capabilities to settle payments rapidly and accurately. This blog summarises the main causes for the challenges faced by R&I services today and potential solutions to address these challenges.

Inefficiencies in data management

One of the key considerations for an effective Reconciliation and Investigation is the need for a strong data management strategy. The challenges existing in R&I operations are not due to complicated business rules and workflows, but the data platforms and services used for data integration and pre-processing. Some of the key challenges involve:

• Data Ingestion – Capturing data from a wide and growing range of data sources including Core banking, Payments, Anti Financial Crime, Schemes, Branches, etc
• Data Processing – Managing Extract, Transform and Load (ETLs) jobs to run reliably and making them resilient to upstream schema changes as data is sourced through legacy mainframe systems, isolated data stores, file systems, etc
• Data Quality – Ensuring accuracy and completeness of data to handle missing fields, invalid payment references, etc
• Data Standardisation – Complexity and effort required in normalising and standardising data as per the required business models (correspondent banking, payment scheme settlements, stock positions etc.)

The Reconciliation effectiveness relies on the data from multiple sources and availability of standardised data for matching operation. Also, the data must be instantly accessible round the clock for the Investigation process to quickly act upon the reconciliation breaks and payment enquiries.

The fundamental capability for a strong data management requires:

• A centralised data solution with automated data movements from a wide range of sources
• The use of ELT (Extract, Load and Transform) architecture rather than ETL for data movements to enable scalability of the R&I systems for transformations
• A flexible architecture with the ability to cope with source system schema changes, minimise need for configuration, replay failed ETLs for syncs without duplication, optimisations for pipeline, and network performance
• The use of interface connectors designed to accommodate a wide range of data models at the source with less engineering efforts to maintain them
• Standardised data models at the R&I system end by aligning closely to the business operating models (product specific) enabling less bespoke changes to reconciliation and investigation applications
• Data Storage strategies around normalisation/denormalisation depending on the use case of Reconciliation and Investigation
• A powerful data warehouse solution to store processed data from R&I systems and effective business intelligence tools for analytics and MI reporting

The above best practices would streamline data management activities and accelerate on-boarding of new products or accommodation of changes for Reconciliations and Investigations. The business and technical SME’s can focus on core business processes (rules and workflows) rather than spending time on data preparation and modelling.

Poor operational efficiency

One of the critical challenges for the operations team is to reconcile transactions that are captured in accounting systems with inadequate data or incorrect references. Sometimes despite having an efficient data strategy, there would be data quality challenges which cannot be addressed at the source. The matching rules cannot easily match or group two related transactions due to inadequate or incorrect references leading to low matching rates and an increase in manual efforts to investigate and resolve the breaks.

The RPA, AI and ML technologies can be leveraged to address the challenges related to manual efforts by reducing the need for a manual workflow:

• RPA (Robotics process automation) can be used to automate some of the repetitive and rule based manual activities (validation and fixing gaps) involved during resolution of breaks
• AI/ML powered suggestions can be used to match the transactions. AI/ML can identify trends and patterns in the data and apply those trends to future reconciliations to resolve breaks or transactions with inadequate or incorrect references to be matched or grouped

AI and ML together with RPA will reduce the overall breaks that need to be investigated daily by the operations team, drastically reduce the need of manual workflow, increase speed, and minimise the risk of errors that occur in manual reconciliations.

The payment investigation efficiency can also be improved by leveraging the AI/ML technologies to automate some of the other operational processes. For example, the investigation process involves

• Logging a case for payment enquiries
• Classifying and prioritising them
• Attaching the relevant transaction history and
• Performing resolution actions to resolve the enquiries

The AI/ML models can automate the manual workflow involved during classification and resolution actions by suggesting suitable investigation workflows to be applied for the cases based on the trends and patterns in the enquiry data. This will reduce the manual efforts involved in the investigation process which in turn increase the straight through processing rates.

Use of legacy technology

Most of the banks still use traditional legacy applications (monolithic) for reconciliation and investigation (R&I) capabilities with on-premises deployment model. The interdependent nature of monolithic coupled with legacy technologies limits agility of R&I operations. It makes more difficult to integrate and accommodate changes quickly to introduce new business functionalities and banks struggle to meet the implementation timelines for industry driven regulatory changes and innovations. Also, the on-premises deployment model is expensive for bank from an infrastructure hardware maintenance perspective and to manage periodic upgrades as part of quarterly or yearly product releases from vendor to be compliant with industry recommended standards.

To keep the operational cost low and enable agility, the R&I platforms can incorporate cloud native approach for building, deploying and managing applications. The below are some of the best practices that can be adopted as part of move towards cloud native approach

• Microservice based architecture framework to build, change or replace components of Reconciliation and Investigation systems for rapid innovation
• Domain driven design can be leveraged to decompose large complex Reconciliation and Investigation business logic into smaller services based on business capabilities with well-defined interfaces to communicate with each other
• API based infrastructure to enhance business capabilities supporting both RESTful and GraphQL API use cases
• Kafka for data distribution and big data technologies for data persistence (frequently accessed data and long-term data)
• Strong CI/CD pipelines to strengthen DevOps process. The containerised deployment pattern can be adopted for easy and faster deployments (across multiple environments) or release cycles

The containerisation pattern will also enable banks to migrate to the cloud quickly to take advantage of scalability and cost optimisation. The first move towards cloud can involve migrating all the data capabilities followed by core applications.

Most of the R&I product vendors offer software as a service (SaaS) capability as well to provide easy access to the latest features and capabilities with vendors managing the deployments and periodic upgrades seamlessly. This operating model may or may not fit for all banks depending on the business need and compliance challenges.

Conclusion

Payments Reconciliation and Investigation are key parts of the overall Payments ecosystem and one banks need to focus on as part of their wider Payments Transformation initiative. This blog covers some of the key challenges, causes and practical solutions that banks can adopt to reduce operational cost and increase efficiencies for Reconciliation and Investigation services (R&I). Most banks do not have robust and scalable R&I capabilities and are not fully ready to accommodate regulatory or industry driven changes in Payment landscape. Based on its expertise, Icon can help banks in defining the strategy and implementation of a modern Reconciliation and Investigation platform to bring in operational efficiencies and make the services scalable to adopt new changes in the payment landscape.