FSS | AI, ML and RPA can Strengthen Reconciliation Systems for BFSI sector

Q&A with Rishi Pillay, Managing Executive: Africa, FSS

With open banking and instant payments increasingly becoming mainstream, back-office enterprise reconciliation systems need to keep pace. Conventionally, transactions typically were processed in a batch mode and payments took hours, if not days, to process, clear and settle. Now, reconciliation and settlement cycles have been compressed. This puts tremendous pressure on any institution’s back office to support multiple intraday settlement cycles and reconcile data in near real-time. Payments Afrika speaks to Rishi Pillay Region Head Africa FSS on need for modernization of backend systems

How automating reconciliation systems helps in improving the efficiency of processing transactions?

With digital payments growing exponentially, millions of transactions are exchanged daily between multiple payment ecosystem constituents. The payment or transaction settlement cycles varies basis the combination of stakeholder and different applications that are used and accounting records maintained by these multiple processing systems need to be in sync at different stages of the transaction. The accuracy of the financial close process is crucial to maintaining the financial integrity of the ecosystem, mitigating risk, and fostering trust amongst customers.

Further with open banking and instant payments increasingly becoming mainstream, back-office enterprise reconciliation systems need to keep pace.  Conventionally, transactions typically were processed in a batch mode and payments took hours, if not days, to process, clear and settle. Now, reconciliation and settlement cycles have been compressed. This puts tremendous pressure on any institution’s back office to support multiple intraday settlement cycles and reconcile data in near real-time. Current manual or semi-automated processes simply cannot scale to accommodate new business needs.

End-to-end enterprise level automated reconciliation processes can help financial institutions and corporates scale to handle large influx of transaction data, improve speed, manage operational risk, and address compliance needs.

  • Improve Accuracy and Lower Risk of Error  

A single exception can result in significant losses and reconciliation teams handle a large number of exceptions every day Automating reconciliation and certification processes throughout the entire financial close lifecycle, reduces the risk of errors.

  • Lower Exceptions and Write-Offs

With automated reconciliation processes accounting discrepancies can be proactively identified and corrected before customers even register a complaint.  As an example, the customers could have cancelled a transaction, but the corresponding credit may have not been received due to a technical glitch or a system error or an actual fraud that has occurred.   With detailed audit trails such discrepancies can be easily identified, enabling banks to reduce exception investigation handling  time by 90%,  optimize dispute handling costs which in turn aids with risk mitigation

  • Mitigate Compliance Risk

With improved data management and audit trails, financial institutions reduce compliance risk and ensure compliance with audit and regulatory requirements.

  • Enhance Productivity

Automate time-consuming manual processes in reconciliation operations, saves time staff spends on reconciliation processes, freeing resources to focus on strategic added value work including risk mitigation, and operational improvements

How AI and ML could be used by banks to overcome the challenges in reconciliation systems?

A growing number of channels, instrument complexity, and activity spread across multiple service providers and increased transaction frequency by consumers adds to the complexity of the reconciliation process. AI and Machine Learning will have a significant upside on the efficiency of the reconciliation process. By employing machine learning at key data reconciliation points, reconcilers can unlock multiples of value in terms of time, operating cost and avoiding regulatory penalties,

Advanced ML algorithms can improve process efficiency across multiple reconciliation points. The reconciliation process typically entails tasks such as onboarding payment classes, extracting, and normalizing data from non-standardized file formats, defining matching rules and posting entries for settling accounts.

Conventional systems rely on a static pre-configured “rule-based framework” for payments reconciliation. However, these tools can become inefficient while adding new data sources or if new entries are introduced in a particular reconciliation file, these need to be identified manually. Further reconciliation teams need to create, test, and implement new rules whilst balancing the impact on existing rules which prolongs the reconciliation cycle time. With ML-enabled processes, the  system automatically “learns” the data sources and patterns, analyzes it for likely matches across multiple data sets, highlights reconciliation exceptions / mismatches, and presents actionable “to do” lists to resolve data issues.

The use of Robotic Process Automation can automate routine, manually  intensive tasks.  Let me give you an example.  Even today banks with automated reconciliation processes deploy dedicated personnel to fetch files from an interchange portal or a  dispute management system, download the files and place them in the right location for the reconciliation system to act on the data.  Such tasks can be automated by use of bots, maximizing value of employee time.

Payment reconciliations have become exceedingly complex, with multiple payment options, channels, combination of product processors for different payment method across line of business and the need for speed and accuracy of reconciliation is crucial for businesses. FSS Smart Recon offers an AI-based solution for reconciliation management across payment workflows, with built in support for, multi-source, multi-file many-to-many reconciliation scenarios. With FSS Smart Recon customers can achieve  a 40% improvement in time to market for greenfield implementations, a sizable 30% betterment in reconciliation time cycles, and an overall 25% lessening in direct costs as compared to partially automated processes

What are key technology trends are you observing in reconciliation space?

Rapid payments evolution, market competition, and advancements in technology continue drive evolution and modernization of reconciliation processes. Technology trends that are  gaining momentum include

  • Greater adoption of SaaS and cloud-based models to accommodate growing transaction workloads and  to lower total cost of ownership
  • Blockchain is a perfect choice for complex reconciliation and would be the next differentiating inclusion in global leading products
  • Enhanced use of AI and Machine Learning AI-based algorithms for self- supervised and self-optimizing recon processes
  • Smart use of data by designing the right data layer or system of record layer to to improve  performance, precision of matching , operations, and fraud controls

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