Welcome to the Future: Automation and the Finance Professional

Hello there! Welcome to one of the most exciting parts of the E1 Managing Finance in a Digital World syllabus. In this chapter, we are exploring Automation and the Future of Work.

If you have ever worried that "robots are going to take my job," take a deep breath! This chapter isn't about the end of finance careers; it's about how technology augments (enhances) our work. We will look at how Robotic Process Automation (RPA) and Artificial Intelligence (AI) are changing what we do every day, shifting our focus from routine data entry to high-value decision-making. Let’s dive in!

1. Robotic Process Automation (RPA)

The first thing to understand is that "Robotic" does not mean a physical metal robot sitting at a desk. In the world of finance, a "robot" is actually a software program designed to perform repetitive tasks.

What is RPA?

RPA is software that can be programmed to perform high-volume, repeatable tasks that were previously done by humans. Think of it as a "digital worker" that follows a very specific set of rules.

Common tasks for RPA include:

  • Opening emails and attachments.
  • Copying and pasting data between systems (e.g., from an Excel sheet to an ERP like SAP).
  • Filling in forms and processing invoices.
  • Comparing two documents to see if the numbers match (reconciliations).

The Benefits of RPA

Why are companies so keen on RPA? Here is a simple breakdown:

  • Accuracy: Robots don't get tired or bored. They do not make manual clerical typos.
  • Speed: They work 24/7 without needing a break.
  • Cost-Effective: Once the software is set up, it is significantly cheaper than paying for repetitive manual data entry.
  • Compliance: Every move a robot makes is logged, creating a clear audit trail.

Analogy: Imagine you have to move 1,000 bricks from one side of a yard to the other. You could do it yourself (manual work), or you could set up a conveyor belt (RPA). The conveyor belt is faster, doesn't get tired, and always puts the bricks in the exact same spot.

Quick Review: RPA is best for tasks that are rule-based, repetitive, and involve structured data (data that fits neatly into predefined rows and columns).

2. Cognitive Computing and Artificial Intelligence (AI)

If RPA is the "brawn" (the hands), then Cognitive Computing is the "brain." The main difference is judgment.

Moving Beyond Rules

While RPA follows strict "If/Then" rules, Cognitive systems can handle unstructured data (like customer feedback, emails, or contract wording) and learn from experience using Machine Learning.

Key features of Cognitive Computing:

  • Natural Language Processing (NLP): The ability for a computer to understand and interpret human language.
  • Pattern Recognition: Spotting trends and correlations in massive datasets that a human would miss.
  • Adaptive Learning: The system refines its accuracy the more data it processes.
Did you know?

In finance, AI is widely used to detect fraud. The system learns what a "normal" transaction looks like for a customer or supplier and automatically flags anything anomalous or suspicious in real time.

Key Takeaway: RPA follows rules; AI/Cognitive Computing mimics human reasoning and pattern evaluation.

3. How Automation Affects the Components of the Finance Function

A central requirement of the E1 syllabus is understanding how increased automation impacts the four core components of the finance function: Financial Reporting, Management Accounting, Treasury, and Internal Audit.

1. Financial Reporting

Financial reporting focuses on preparing statutory financial statements and ensuring regulatory compliance.

  • Impact of Automation: Routine bookkeeping, journal entries, intercompany reconciliations, and period-end ledger closes can be automated using RPA. Systems can automatically extract ledger figures into standardised reporting formats and apply digital tags (such as XBRL).
  • Shift in Role: Rather than spending days manually compiling trial balances, financial accountants shift their time to reviewing complex accounting treatments, exercising professional judgment, and managing disclosure quality.

2. Management Accounting

Management accounting focuses on planning, budgeting, cost control, and supporting operational decision-making.

  • Impact of Automation: Routine variance calculations and monthly reporting packs are generated automatically in real time through automated business intelligence dashboards. Advanced analytics and machine learning enable rolling forecasts and predictive budgeting.
  • Shift in Role: Management accountants spend less time gathering variance data and more time acting as finance business partners—interpreting what the data means, diagnosing operational root causes, and advising commercial teams.

3. Treasury

Treasury manages an organisation's cash, liquidity, funding, foreign exchange (FX) exposure, and financial risk.

  • Impact of Automation: Routine cash balancing, bank account reconciliations, cash pooling, and sweeping between accounts are fully automated. Algorithms can forecast daily liquidity positions and execute automated hedging transactions within pre-set parameters.
  • Shift in Role: Treasury professionals shift from executing manual transfers to optimising capital structures, managing counterparty risks, and strategising long-term funding arrangements.

4. Internal Audit

Internal audit provides independent assurance that an organisation's risk management, governance, and internal controls are operating effectively.

  • Impact of Automation: Traditional internal audit relied heavily on periodic, sample-based testing after transactions took place. With automated audit tools, auditors can achieve continuous auditing—testing 100% of transactions as they happen and automatically flagging exceptions or control breaches.
  • Shift in Role: Internal auditors spend less time ticking sample transactions and more time assessing system configurations, testing automated controls, evaluating cyber-risk, and advising on governance.

4. How Automation Changes the Finance Function Overall

Across the wider function, automation changes the balance of activities in the finance value chain. Traditionally, finance teams spent roughly 80% of their effort gathering data and only 20% analysing it. Automation inverts this dynamic.

The Shifting Emphasis

The role of finance professionals evolves through three progressive levels:

  1. Information Production: Collecting, cleaning, calculating, and reporting numbers. This is heavily automated.
  2. Information Insight: Interpreting patterns, explaining variances, and identifying trends.
  3. Influence and Impact: Partnering with business leaders to translate insight into strategic decisions and measurable value.

New Roles and Capabilities

Automation fosters new and evolving specialisms within and alongside finance:

  • Data Specialists: Professionals who build and maintain financial data pipelines and analytical models.
  • Scenario Planners: Finance professionals who use advanced predictive tools to model potential strategic outcomes.
  • Systems and Controls Custodians: Professionals who ensure automated workflows, algorithms, and RPA scripts maintain data integrity, security, and compliance.

Common Mistake to Avoid: Many students assume automation replaces the need for finance professionals. Incorrect! Automation removes routine processing; human professionals are essential for ethical scrutiny, contextual reasoning, stakeholder persuasion, and strategic leadership.

5. The Skills of the Future

To operate effectively in an automated environment, finance professionals draw on balanced competencies across key areas:

  • Technical Skills: Understanding accounting standards, internal controls, and taxation remains essential. Technology changes how data is processed, not the underlying principles of accountability.
  • Business Skills: Understanding the organisation's commercial model, industry dynamics, and competitive environment to give automation outputs meaningful context.
  • People Skills: Communicating complex financial insights clearly, building cross-functional relationships, and negotiating with stakeholders.
  • Leadership Skills: Leading teams through digital transitions, managing change, and upholding governance.

6. Augmentation: Working Together

Instead of viewing technology purely as "replacement," the current model is augmentation—technology and human professionals collaborating to deliver greater value.

Example: An automated system checks 100,000 supplier invoices, matching them against purchase orders and goods-received notes. It processes 99,800 without human intervention and flags 200 exceptions with mismatched terms or unusual banking details. A finance professional then investigates those 200 exceptions, applying judgment, verifying authenticity, and resolving disputes. The machine provides scale and speed; the professional provides judgment and oversight.

Quick Summary Box:

  • RPA: Rule-based, handles repetitive tasks, works with structured data.
  • AI / Cognitive: Judgment-based, identifies patterns, works with unstructured data.
  • Financial Reporting: Automates ledgers and close processes; shifts focus to complex accounting judgments.
  • Management Accounting: Automates reporting packs; shifts focus to commercial partnering and predictive insights.
  • Treasury: Automates cash management and liquidity tracking; shifts focus to risk strategy.
  • Internal Audit: Shifts from sample-based checks to continuous 100% auditing and control assessment.