Reconciliation is a crucial control function in financial services that aims to mitigate operational risk that can lead to fraud, fines, or even the failure of a whole firm. While automation was introduced to parts of the system in the early 2000s, innovation in this area has stalled, leaving operations reliant on manual processes and spreadsheets. With the advancements in technology, it is essential to explore how firms can streamline their reconciliation function and update their systems to improve automation, efficiency, and data quality. Different stages of reconciliation maturity can help financial institutions benchmark their reconciliation best practices and identify necessary steps to achieve the best possible usage model of automatic error detection and correction, and the elimination of inter-system reconciliation.
Why is Reconciliation So Hard?
Despite the extensive digital transformation of financial institutions, many find reconciliation to be a challenging task. In most organizations, multiple point solutions are used for specific reconciliation tasks, resulting in a patchwork of disparate processes stitched together via spreadsheets, manual work, or home-made applications. This process is highly inefficient, lacks consolidation, and every task is prone to errors and fragmentation.
Some reasons why reconciliation is still challenging are:
- A lack of standardization: In financial services, there are often no strict data standards, requiring bespoke reconciliation processes or expensive data normalization.
- Increasing complexity: More complex products require more information, which current systems may not be able to handle. This challenge is further compounded by regulatory reporting and associated reconciliations required.
- Poor data quality: Missing fields, inconsistent coding schemes, and unavailability of common keys make automation difficult when using current solutions.
- Data inconsistency and lineage issues: Data passing through the organization can cause errors and inconsistencies, introducing a variety of systems, processes, and reconciliation techniques across the business.
However, with the right tools and outlook, firms can overcome these problems and automate and optimize their entire reconciliation function. This can be achieved by consolidating processes, eliminating manual and point solutions, and enabling practitioners to analyze and drive additional value from their data. Once all data is normalized onto one system, machine learning technology can be used to optimize every step of the process. With enough training data, machine learning can spot errors, outliers, and poor data quality, reducing the number of reconciliations required and making inter-system reconciliations unnecessary.
Stages of Reconciliation Automation Maturity
- Manual: Reconciliation carried out manually on spreadsheet or other similar applications. They pose a high risk due to human error and also make it difficult to audit.
- Hybrid: Systems are in place for different data types and other reconciliation out of the system is done manually. This reduces the labour, but since the processes are disparate has high chances of duplication.
- Automated: Where reconciliations are consolidated onto one or more automated systems and teams are responsible only to onboard data and investigation of exception handling. This model reduces risk and optimizes efficiency.
- Intelligent: A central system that collects and reconciles all data types, monitors and controls data quality and consolidates them throughout the data lifecycle. This provides a model that uses ML and AI models on reconciliation data, keeps improving itself and involves minimal human intervention.
Machine Learning - Does it Live up to the Hype?
Machine learning is a technology that has replaced blockchain at the top of the hype curve in terms of revolutionizing financial services. The majority of vendors in the reconciliation space are shouting about it, and it’s easy to see why. Machine learning can predict correct configurations for matching and data normalization, parse unstructured data, predict root causes of breaks, and cluster related exceptions. However, it is essential to keep in mind that machine learning is only as good as the data it’s trained on. Without access to a large and varied set of training data, the benefits will be limited. This is especially crucial when considering machine learning deployed on an installed system versus a cloud-based system. In the installed version, the system can only learn from local data, while the cloud version can train on a much wider set. Imagine using a traffic app on your phone. The cloud-based version can give you information based on a huge range of anonymized data, whereas the installed version only has access to data in your local area. In addition, machine learning is as good as its training models and the data being used. As reconciliation is a mission-critical function, keeping a human in the loop becomes important. Vendors delivering these solutions need to be careful how to carefully model reconciliation matches and declines as it will impact the recommendations given in the future.
FSS Smart Recon - The Tool of Choice for Modern Day Reconciliation
The future of automation in banking lies in smart reconciliation. FSS Smart Recon is built on new-age data platforms and microservices based cloud native architecture to provide your financial institution with a next-gen reconciliation solution that automates and simplifies multi-party data reconciliation and reduces manual efforts by up to 70%. Learn more about FSS Smart Recon here.
