From manual to machine learning: approaching reconciliation

At the start of 2020, before the global coronavirus pandemic, financial industry experts recognised that this would become the ‘decade of data’.

One of the many effects the current 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. 

However, despite the rush to digitalise many manual systems, automating reconciliations is still one of the toughest areas to crack.  Even pre-pandemic, automating this essential control function in financial services - which can help eliminate operational risk that can lead to fraud, fines, or in the worst case, the failure of a firm - was proving elusive for many organisations. Why? 

Many organisations are facing a situation where there is a multitude of systems, different processes, technology types and computing.  Within that, there are three key reasons that make automation difficult - a lack of standardisation, increasingly complex financial instruments, and poor data quality.  However, 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. So, how do firms deal with this influx of data - that is fast becoming business-critical - in the most intelligent way? 

We recently 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 guides 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.

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The five stages of reconciliation maturity are:

1. Manual - All reconciliations are carried out manually, using spreadsheets, or via homemade applications.  There is a high risk of error and lack of auditability.

2.  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.

3.  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.

4. Improving - Additional data quality controls are active throughout the data lifecycle. The simplification of processes is possible, leading to system decommissioning and consolidation.

5. 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.

While stage five is the ‘holy grail’ that all financial organisations should be aspiring to, many firms are at the ‘hybrid’ stage, and making the leap to ‘automated’ is the most challenging step.  However, once at stage three, firms are more able to move up the process to ‘self-optimising’.  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.  

So, while we know that moving from manual to machine learning is not an overnight process, The Reconciliation Maturity Model provides a blueprint to getting there.

The Reconciliation Maturity Model is available for download here https://content.du.co/reconciliation-maturity-model-whitepaper

This article was contributed by Christian Nentwich, CEO at Duco

For more information on business topics in the United States, please take a look at the latest edition of Business Chief North America