The Covid-19 pandemic was the start of a massive shift in thinking for all financial services organizations. With digitization becoming a necessity rather than a luxury, many firms had to re-evaluate their current processes and adopt new ways of working practically overnight.

And while this fast approach to digitization was a step forward in some respects, it also highlighted the inflexibility and inefficiency of certain processes. Nowhere has this been more apparent than in terms of the systems firms have in place to manage their data’s integrity.

The Importance of Data

Data is a financial services firm’s most abundant and important asset. It pervades every facet of the industry, from its role in staple tasks such as reconciling invoices with payments, to more complex undertakings such as risk analysis and long-term business projections. 

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Yet, despite this crucial reliance on data, there are still significant challenges within financial circles when it comes to measuring and prioritizing its integrity and accuracy. Some of this is due to legacy systems, which can be difficult and costly to replace or update. Another interlinked issue is the reliance on manual processes instead of data automation. However, one of the most complex problems to solve in ensuring good data integrity is the huge number of systems existing across financial service organizations that are touched by the same data.

Our “State of Reconciliation” report  –which surveyed 300 heads of global reconciliation utilities, COOs, heads of financial control and heads of finance transformation working in large financial services organizations across the UK – revealed that nearly one in five (17%) financial services organizations rely solely on manual processes for their data reconciliation. And the majority (87%) of firms have between 11 and 40 manual controls or spreadsheets for reconciliation tasks.

Relying on manual reconciliation brings a whole host of problems to any operation, the foremost being that manual tasks often become tedious to a human brain leaving room for error. Due to the restrictions of legacy systems, most complex reconciliation processes are undertaken manually or through a EUDA, which complicates the process and hinders scaling efficiency.

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It’s also not easy to scale manual reconciliation processes, with firms generally only hiring a workforce large enough for the expected workload, not for any future developments.

This means that when external factors such as a market crisis, new product launch, or even a global pandemic cause data spikes, it’s harder to meet the demand. In fact, our survey revealed that 37% of financial firms say they would have suffered less as a business if they used intelligent data automation at the start of the pandemic, reinforcing the need for flexible, scalable systems.

Moving Away From Manual Reconciliation: The Argument for Automation

With the pandemic highlighting the pitfalls of manual data reconciliation, more financial institutions are prioritizing data reconciliation through automated processes to improve operational agility. This reduces the risk of fraud and provides greater operational resilience. Nearly half (46%) say that data reconciliation is more important to their business in 2021 than ever before.

We are at a tipping point in data integrity as the pandemic has placed pressure on financial services firms to be more operationally efficient. Technology, especially data automation, has developed to better help organizations achieve this. Today, there is only one way to realistically achieve the level of data quality required by financial service organizations — Intelligent Data Automation (IDA)

There are three key factors that make automation important in data reconciliation: the reduction of errors, the ability to scale quickly the efficiency and simplicity of operations.

Automation takes away the human error element from data reconciliation. Repeatable tasks can be delegated to a computer to handle more efficiently and with a lower error rate. This frees up the workforce to do jobs that add more value to the business, such as new product offerings or adapting to regulatory changes.

And, when companies rely on automation over manual processes, the platforms can scale to suit the business need and the original workforce is free to deal solely with the data exceptions from the platform. This increases the efficiency of operations and simplifies role definitions.

Integrating Machine Learning

Although data automation platforms reduce the margin for error when compared with manual processes, there will still be an occasional discrepancy in the data. This means that when there is a break in the data or an exception it needs a human in the loop to investigate the problem.

Machine learning and artificial intelligence-enabled data automation platforms can reduce the time spent resolving exceptions and data breaks. They ‘learn’ the solution to any given issue and automatically correct the problem when it happens again, meaning the only human intervention required is a one-off review of the issue.

This auto resolution functionality reduces the number of manual investigations over time, saving companies resourcing hours and consequently money.

And with machine learning-enabled intelligent data automation platforms, the more data you put into the system, the more it learns and therefore the more efficient your data reconciliation becomes – as well as scaling rapidly alongside the growing size and variety of data as digital transformation continues at speed. It’s not simply about having or managing more data, but collating intelligence from a wide range of customer datasets and applying this insight into your operations.

Looking Forward

With the integrity of data forming the foundation for financial services, we will continue to see organizations shift their approach to managing their data from a reactive to a proactive one.

There will be a renewed focus on data automation, as more and more firms recognize the untapped potential of frictionless data sets that unlock efficiencies and insights.

But, this change needs to happen swiftly. As the finance industry’s reliance on digitization increases, it’s only a matter of time before the issue of data integrity becomes front and center for tech teams within every financial institution.

[To share your insights with us, please write to sghosh@martechseries.com]