Financial services companies are now data factories, but as data volumes rise and the process becomes more complex, discovering, preparing, and blending disparate data sources can become increasingly difficult. That’s where automation can help by shortening data analytics journeys.
Automated data analytics offers a solution to the unique challenges faced by financial service leaders to deliver more accurate insights and get to value faster. Firms can also empower their domain experts through automated data analytics to uncover insights from data, rather than relying on IT or hiring specialists to establish a single source of truth for compliance, improve efficiency, manage risk, and achieve more predictable financial results.
Establishing a centralised data source
Finance leaders have a massive opportunity to delve into the specific industry applications of automation in data analytics. Combining automated analytics solutions with rich technology integration options, embedded genAI features, and accessible data science offers the perfect capability for decision-makers with no data science skills to deliver insights via a natural language prompt.
Financial data can be stored, accessed and analysed in many different places, whether on-premise, private, public, or multi-cloud. Organisations must focus on having tools in place to find the data across silos and bring it into their analytic process. Consolidating siloed financial data in a governed environment provides stakeholders access to quality data pipelines for a holistic view of accurate financial metrics. This goal is well worth pursuing, but industry leaders need to be conscious of the various challenges of organising financial data. This is where an accessible, self-service platform that boasts ease of use, scalability, and flexibility proves its worth with the capability to pull raw data from various disparate sources.
For example, advisory tax firm Baker Tilly has realised the benefits of automated analytics and is leveraging it to consolidate massive volumes of unclaimed property data from disparate sources like bank accounts and company payroll. This slashes report processing time by 50% and significantly reduces risk to operations.
Financial leaders can start automating data consolidation and analytics efforts by mapping internal and external financial data sources and preparing them for final integration.
Eliminating repetitive data tasks
Financial process automation can eliminate repetitive tasks like data entry, invoice processing, and regulatory testing. Allowing technologies to take on these manual tasks can reduce errors, improve forecast accuracy, and enable finance teams to focus on more strategic initiatives in the business.
The financial services industry knows the negative impact of manual spreadsheet work; Gartner estimates that human data entry errors in finance processes alone add roughly 25,000 hours of avoidable rework at $878,000 per year.
Dependence on manual spreadsheets isn’t just a financial risk—it’s a drag on productivity. To take an example from another industry vertical, Siemens Energy was buried in error-prone manual tasks using Excel spreadsheets. The company implemented a code-free, automated platform, saving thousands of hours and generating 200+ new use cases for automation.
Organisations should scope out small but high-impact areas for automation in highly manual and repetitive finance processes to kick off data collection automation. This will yield a quick return on investment, helping to prove the worth of automation initiatives and expand their subsequent use throughout the business.
Increasing accuracy and lowering costs
Once data has been centralised and initial analytic process automation use cases are initiated, finance leaders will start to feel the benefits of quicker reporting, increased data accuracy, and lower operational costs.
Real-time data can also reduce exposure and improve responsiveness to new business opportunities. AI capabilities can be leveraged across the organisation for auto-generated narratives in extensive management reports.
Telecom giant BT used 140 legacy Excel models to run regulatory compliance reports, which took up to 4 weeks during compliance cycles. To reduce risk and improve efficiencies, the company re-platformed the models to an automated analytics solution, reducing time to insight by 75% and improving the accuracy of regulatory reporting.
Organisations must start by defining a well-structured business case for automation. Once completed and signed off, they can improve the financial close process and gain immediate wins for additional use cases throughout different processes.
No room for error when it comes to compliance
In finance, if something goes wrong, it has a knock-on effect on the rest of the company, which can be a key trigger in losing clients, prospects, and business reputation. Discrepancies from manual spreadsheet manipulation can expose companies and financial clients to unnecessary exposure and money losses.
With the rise in analytics transformation in the finance industry, the manual burden of executing audits is relieved. Regulation can be complied with regardless of changes or extra requirements, and the compliance process helps drive accurate financial insights.
At Bank of America, manually prepping and cleansing data for millions of transactions took up to two months, exposing the bank to costly regulatory fines. The bank now uses a cloud automation solution to reduce processing time to one hour, giving the regulatory team time to take corrective action.
Reaping the rewards
Automation can bring many advantages to data analytics in financial organisations, from faster forecasts to quicker decision-making, all while consistently enhancing the accuracy of outputs. By automating data and reporting, leaders can fuel the digital transformation required to reap rewards, including improved efficiencies, reduced costs, and boosted profitability.
Data analytics automation is the future of financial services and no organisation can afford to be left behind.
About the Author
Steve Brodrick is the chief transformation officer of Alteryx and is responsible for advising and leading its customers on their digital transformation journeys. He leads a team that engages with customers to maximise their analytics investments and generate positive impact at the global, regional and local levels.
