Duco Launches Reconciliation Best Practice Model To Help Firms Manage Cost Pressures And Improve Operational Resilience
The ‘Reconciliation Maturity Model’ is a blueprint to consolidate, automate and drive efficiency across reconciliations.
London, 14 May 2020 – Duco, the global provider of self-service data integrity and reconciliation services, has launched ‘The Reconciliation Maturity Model’ – a new roadmap that will help financial firms improve the automation, efficiency and integrity of data across all reconciliation and data matching tasks. The model – which outlines the steps firms can take to reach optimum efficiency in their reconciliation processes – will also help them to manage cost pressures, reduce risk and improve operational resilience.
In a world where the quantity and complexity of data that firms need to handle is set to increase exponentially, relying on manual systems and processes is no longer feasible. Dealing with this influx of data in the most intelligent way is fast becoming business critical – particularly in the current climate.
The Reconciliation Maturity Model will guide reconciliation practitioners through five key stages of reconciliation maturity, from ‘manual’ through to ‘automated’ and eventually ‘self-optimising’ – where machine-learning technology automates nearly the entire process, and where intersystem reconciliations are all but eliminated.
Importantly, a more progressive approach to reconciliation automation will not only result in greater operational efficiency, it will also dramatically boost operational resilience, and put forward-thinking financial institutions in a better position to benefit from new technology and the added insight that intelligent systems bring.
Christian Nentwich, CEO of Duco, comments:
“At the start of 2020, even before the global pandemic changed the world, financial industry experts recognised that this would become the ‘decade of data’, with firms inundated with trillions of lines of data from a multitude of sources.
“One of the many effects the Coronavirus crisis has had is to amplify the need for resilient, connected systems and more robust processes. With business continuity front of mind, many organisations are looking for more efficient ways to manage huge swathes of data from multiple, disparate sources quickly and accurately. Data integrity is a key concern, and many are asking how they can automate their most critical processes.
“Finding a way to improve the reconciliation process is now even more crucial. We developed The Reconciliation Maturity Model in response to these issues. It can help to manage cost pressures at a time of increased scrutiny, automate processes dependent on manual work so improving resilience, and significantly reduce operational risk that has the potential to lead to fraud, fines or in the worst case, the failure of a whole firm.”
Duco’s five stages of reconciliation maturity are:
- Manual – All reconciliations are carried out manually, using spreadsheets, or via homemade applications. There is a high risk of error and lack of auditability.
- Hybrid – Point system(s) are in place for specific data types, while other reconciliations are carried out on spreadsheets or manually. Teams/processes are disparate, reconciliation as a function is fragmented and duplicate work is likely.
- Automated – All reconciliations are consolidated onto automated systems. Small teams build and onboard reconciliations, and oversee exception investigation. There are significant efficiency improvements and risk is reduced.
- Improving – Additional data quality controls are active throughout the data lifecycle. The simplification of processes is possible, leading to system decommissioning and consolidation.
- Self-optimising – Full automation is deployed across the entire lifecycle of reconciliation, from onboarding to exception resolution. There is very little involvement from staff and continuous improvement is possible via a machine-learning enhanced system. Internal reconciliations are removed, leading to major reduction in cost and complexity.
Christian Nentwich continues:
“Stage Five is the ‘holy grail’ that all financial organisations should be aspiring to. At this point, with enough training data, machine learning can spot errors, outliers and poor data quality at source, reducing the number of reconciliations required. While we know that this is not an overnight process, The Reconciliation Maturity Model provides a blueprint to getting there.”
The Reconciliation Maturity Model is available for download below.