As open banking and instant payments become increasingly mainstream, back office voting systems for businesses need to keep up. Traditionally, transactions were typically processed in batch mode and it took hours, if not days, to process, clear, and settle payments. Now the coordination and processing cycles have been compressed. This puts the back office of an institute under enormous pressure to support several intraday settlement cycles and to reconcile data almost in real time.
For this reason, financial institutions are looking for automated, enterprise-level reconciliation processes that can help them handle the large influx of transactional data, improve speed, manage operational risk, and meet compliance requirements.
According to Sathish N., Deputy Chief Product Officer, FSS, that’s the promise of AI and machine learning. “By using machine learning at key data reconciliation points, reconcilers can unlock many times the value in terms of time, operating costs and avoidance of regulatory sanctions,” he said in an interview with Technical observerIn addition, advanced ML algorithms can improve process efficiency across multiple coordination points.
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How does the automation of voting systems help improve the efficiency of transaction processing?
As digital payment grows exponentially, millions of transactions are exchanged between multiple components of the payment ecosystem every day. Payment or transaction processing cycles vary depending on the combination of stakeholders and different applications being used, and the accounting records kept by these multiple processing systems need to be in sync at different stages of the transaction. The accuracy of the financial close process is critical to maintaining the financial integrity of the ecosystem, reducing risk and building customer trust.
As open banking and instant payments become mainstream, back office voting systems for businesses need to keep up. Traditionally, transactions were typically processed in batch mode and it took hours, if not days, to process, clear, and settle payments. Now the coordination and processing cycles have been compressed. This puts the back office of an institute under enormous pressure to support several intraday settlement cycles and to reconcile data almost in real time. Current manual or semi-automated processes simply cannot be scaled to meet new business requirements.
End-to-end, automated reconciliation processes at the company level can help financial institutions and companies to cope with the large influx of transaction data, improve speed, manage operational risk and meet compliance requirements.
Improve accuracy and reduce the risk of errors
A single exception can result in significant losses, and the reconciliation teams handle a large number of exceptions on a daily basis. By automating the reconciliation and certification processes throughout the financial life cycle, the risk of errors is reduced.
Lower exemptions and depreciation
With automated reconciliation processes, accounting differences can be proactively identified and corrected before customers even register a complaint. For example, customers may have canceled a transaction but the appropriate credit may not have been received due to a technical or system failure or actual fraud. With detailed audit trails, such discrepancies can be easily identified, allowing banks to reduce the processing time for exception investigations by 90% and optimize dispute resolution costs, which in turn helps reduce risk
Reduce compliance risk
With improved data management and improved audit trails, financial institutions reduce compliance risk and ensure compliance with audit and regulatory requirements.
increase productivity
Automate time-consuming manual processes in reconciliation processes, save time staff spend on reconciliation processes, and free up resources to focus on strategic value-added work including risk reduction and operational improvements
How could banks use AI and ML to cope with the challenges in reconciliation systems?
A growing number of channels, instrument complexity and activities spread across multiple service providers, and increased transaction frequency by consumers add to the complexity of the voting process. AI and machine learning will have a significant benefit in making the voting process more efficient. By using machine learning at key data reconciliation points, reconcilers can unlock many times the value in terms of time, operating costs and avoidance of regulatory sanctions.
Advanced ML algorithms can improve process efficiency across multiple voting points. The reconciliation process typically includes tasks such as including payment classes, extracting and normalizing data from non-standardized file formats, defining rules of conformity, and posting entries for billing accounts.
Traditional systems are based on a static preconfigured “rule-based framework” for payment reconciliation. However, these tools can become inefficient when adding new data sources. If new entries are added to a particular reconciliation file, they must be identified manually. Additional reconciliation teams need to create, test, and implement new rules while balancing the impact on existing rules, thereby increasing the reconciliation cycle time. For ML-enabled processes, the system automatically “learns” the data sources and patterns, analyzes them for likely matches across multiple data sets, highlights reconciliation exceptions / mismatches, and displays actionable task lists for solving data problems.
Using Robotic Process Automation can automate routine, manually intensive tasks. Let me give you an example. Even today banks with automated reconciliation processes use dedicated staff to retrieve files from an exchange portal or a dispute settlement system, to download the files and to place them in the right place so that the reconciliation system can react to the data. Such tasks can be automated using bots to maximize the value of employee time.
Payment reconciliations have become extremely complex. Multiple payment options, channels, the combination of product processors for different payment methods in different business areas, and the need for quick and accurate reconciliation are critical to businesses. FSS Smart Recon provides an AI-based solution for reconciliation management across payment workflows with built-in support for reconciliation scenarios with multiple sources and multiple files. With FSS Smart Recon, customers can improve time-to-market for greenfield implementations by 40%, improve coordination time cycles by 30% and reduce direct costs by 25% compared to partially automated processes. FSS Smart Recon offers added value in the following ways:
- A unified platform for the provision of a modern, fully web-based reconciliation platform system for end-to-end reconciliation, which includes data import, transformation and enrichment, data reconciliation and exception management
- Wide application – supports all classes of digital payments with a single system – ledger reconciliation, ATM reconciliation, card reconciliation, online payments, wallets, instant payments (IMPS and UPI), NEFT, RTGS and QR code payments – with integrated system flexibility to quickly integrate new payment channels and systems
- Universal Data Wizard: Simplifies setting up the reconciliation process using a template-based data mapping framework. This optimizes the go-live time for greenfield implementations by 30 percent
- Detailed Audit Trail: Provides a detailed audit trail that enables users to understand the reasons for a disruption or a match case and treat them appropriately.
- Extended exception identification and analysis to advise on timely measures and follow-up measures to enable their closure
- AI-based settlement processes FSS Smart Recon uses machine learning (ML) and algorithms. FSS Smart Recon continuously learns file patterns and can automatically identify new records so that staff can predict exceptions and take action to take action without the need for constant support or professional services.
- Dispute Resolution – Supporting the dispute and chargeback lifecycle so banks can respond to disputes in much shorter time frames – which improves efficiency and customer service.
- Flexible Business Models: FSS offers Recon Services as a licensed and SaaS model to give customers greater deployment flexibility so that no upfront investment is required
What are the main technology trends you see in the area of reconciliation?
The rapid development of payments, competition in the market and technological advances are driving the further development and modernization of the reconciliation processes. Technology trends that are gaining momentum include:
- Stronger adoption of SaaS and cloud-based models to adapt to growing transactional workloads and lower the total cost of ownership
- Blockchain is a perfect choice for complex reconciliations and would be the next differentiating inclusion in world leading products
- Improved use of AI and machine learning AI-based algorithms for self-monitored and self-optimizing awareness-raising processes
- Use data intelligently by designing the right data layer or recording layer system to improve performance, accuracy of reconciliation, operations and fraud control
What would be the upcoming priorities for FSS?
Our next big start is about analytics and data science. The abundance of data in most large organizations today is being pushed to a data lake or warehouse, and very little is done to use that insight to impact your customers or your business. The product was developed to address this special big data possibility in the payment area. The product is a complete people-based analysis suite that provides pre-defined insights by business product area. The matrix continues to grow and will shortly map the entire payment ecosystem. The product helps banks to make data-driven business decisions and to increase productivity and business efficiency.
