Automation in financial data processing – history and solutions

When starting your first job in finance, one might expect everything runs perfectly, by machines and computers, and with absolute accuracy. While there is a certain truth to the claim that automatization has helped the financial industry and tasks around financial teams become more productive, the reality is that there is still tremendous manual labor involved.

Let’s look into the history of automation and find out where do we stand today and which automation tools can help you and your team become more productive.

The evolution of data processing in finance

Finance functions today do not operate materially differently than a hundred years ago when ledgers and accounts were handwritten in books – we are still recording, reconciling, and reporting financial transactions.

However, the part of data analysis was until recently very limited to basic summaries generated through manual tabulations and computations. Now,  automation, advanced analytics, and artificial intelligence capabilities allow for much more efficient filtering through millions of records.

But back to history and how we came from hand-written ledgers to Excel and SAP domination:

The rise of mainframe computers

In the 1950s and 1960s, financial institutions first began adopting mainframe computers to automate basic accounting and reporting processes. Mainframes allowed large data storage and simplified tasks like payroll processing and customer billing.

Advent of Enterprise Resource Planning (ERP) systems

By the 1990s, the client-server computing model enabled new enterprise-wide systems, such as SAP, to integrate the management of core business processes, including finance and accounting. ERP systems consolidated data across departments and improved accessibility, but analytics functionality was still elementary.

Exponential growth in data and the Internet

Rapid digital adoption since 2000 led to an explosion of data in both structured and unstructured formats. However, with the increased volume of data, many firms found that massive amounts of data and financial proceeds were added to overhead and lacked the capability to analyze or generate insights from them.

Rise of Big Data, AI, and advanced analytics

The growth of big data, artificial intelligence, machine learning, and cloud computing has now allowed organizations and their financial teams to analyze large amounts of data. This helps them find trends, risks, and new chances to grow.

Benefits of automating financial data tasks

So, what are really the advantages of AI-supported automation workflows? You and your team can automate repetitive, manual financial tasks and gain time and cost benefits:

• Enhanced efficiency: Automation drastically reduces time spent on mundane activities like data entry, letting finance teams handle more value-added work.
• Improved accuracy: Manual processes are prone to human error. Automated systems minimize mistakes in financial data.
• Real-time visibility: Automated reporting provides up-to-date insights into financial performance.
• Better risk management: Automated controls and audit trails enhance fraud prevention and compliance.
• Increased scalability: Systems easily scale to accommodate growth in transaction volumes and complexity.
• Cost savings: Automation reduces manual labor costs and expenses from errors.
• Standardization: Automated processes promote consistency in financial workflows.

According to a report by KMPG, companies can reduce costs by up to 75% by automating routine activities using Robotic Process Automation (RPA).

However, the real power lies in enabling finance teams to focus on decision-making rather than typing in data.

Key areas where automation is making a difference

If RPA and AI-assisted workflow can save 75% of costs, where shall I employ it? There are several workflows in financial teams where Reiterate or similar solutions can seamlessly be integrated:

Accounting operations

• Accounts payable: Optical character recognition and natural language processing extract key details from invoices and match them to purchase orders. Approvals are automated based on custom rules.
• Accounts receivable: Systems send customized payment reminders and process receipts automatically, improving collections.
• Expense reporting: Automation extracts details from receipts and integrates seamlessly with corporate cards. Managers can review and approve via mobile.
• General ledger: Journals and ledgers are automatically updated in real-time, minimizing month-end closing efforts.
• Billing and revenue recognition: Usage data is analyzed to calculate billing amounts automatically as per customized models. Revenue recognition is automated.
• Payroll processing: Employee payroll details, taxes, withholdings, and payments are managed automatically during every pay cycle.
• Fixed assets: Systems track asset lifecycles, calculate depreciation automatically, and keep ledgers up to date.
• Payment reconciliation: Automated transaction reconciliation, from data ingestion and standardization with the help of LLMs, and AI-based tools for transaction matching.

Financial planning and analysis

• Budgeting: Templates and collaborative workflows streamline data collection from business units and automate budget consolidation.
• Forecasting: Systems analyze historical data, trends, and drivers to generate accurate forecasts rapidly. Modeling capabilities allow scenario analysis.
• Reporting and dashboards: Custom reports and interactive dashboards provide real-time analytics into all aspects of financial performance.
• Profitability analysis: By integrating revenue and cost data across business lines, automated tools provide granular profitability analysis.

Treasury management

• Cash flow forecasting: Advanced algorithms accurately predict future cash positions based on invoices, payments and expense schedules.
• Cash management: Machine learning matches cash supply with anticipated needs, optimizing liquidity while minimizing borrowing costs.
• Debt and investment management: Systems use data on market conditions to inform strategies for managing debt and excess cash.
• Risk management: Automated risk analysis across multiple dimensions, including currencies, interest rates, counterparties, etc., minimizes market volatility risks.

Overcoming implementation challenges

Decided to implement a finance automation solution? Make sure you’re looking out for the following operational challenges before you start the project:

• Legacy systems: Integrating data across aging platforms with limited compatibility can be complex.
• Adoption resistance: Personnel accustomed to legacy processes may resist learning new tools. Change management with training and leadership alignment is key.
• Unstructured data: Documents like invoices and contracts need to be digitized and converted to machine-readable data through OCR and NLP.
• Cybersecurity: Managing access controls, encryption, and system security is vital with increased reliance on automation technologies.
• Customization: Each organization has unique requirements that necessitate customization based on industry, size, and existing platforms. Vendors with specialized expertise are ideal.

With the right approach focused on user-centric design and change management, these challenges can be effectively addressed by you.

Get future-ready with automation

The future belongs to finance leaders who make automation a strategic pillar to enhance decision-making, control risks, and provide business partnerships.

Purpose-built for modern finance complexities, Reiterate integrates automated reconciliations, seamless data, powerful analytics, and flexible workflows. This holistic approach will prepare your finance function for the future.

Are you ready to improve reconciliation efficiency? Book a demo now and embrace it on your terms! Automating reconciliation is your springboard to becoming a strategic advisor driving business growth rather than getting bogged down in mundane spreadsheets.