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Why Machine Learning?
Machine learning (ML) is reshaping industries by enabling computers to analyse data and make predictions without explicit programming. For auditors and finance teams who handle extensive data, ML offers powerful ways to streamline complex analyses, detect patterns, and uncover insights that would otherwise be challenging to achieve. As more audit platforms integrate ML features, the opportunity to enhance decision-making and efficiency is within reach, even for those without a deep technical background.
Understanding Machine Learning
At its core, ML is a subset of artificial intelligence that enables systems to learn from data patterns and improve over time. Unlike traditional programming, where a system follows predefined rules, ML allows a computer to recognise patterns in historical data and make informed decisions, adapting as it learns. Traditionally, computers need to be programmed to do exactly what they are told in any given circumstance. ML is different. Think of it as training a computer to identify risky transactions or forecast expenses based on past trends. The computer learns from these past trends and uses these trends to predict future values. For auditors and finance teams, ML can simplify tasks like anomaly detection and predictive analysis, providing valuable insights quickly and efficiently.
Types of Machine Learning and Model Classes
Machine learning can be broadly grouped into three main types, each suitable for different tasks in audit and finance: supervised learning, unsupervised learning, and semi-supervised learning. These types are used in conjunction with various model classes: regression, classification, and clustering, to perform specific types of analyses.
In supervised learning, the model learns from labelled data, where each data point is paired with an outcome. This approach underpins regression and classification tasks. Regression models, for example, are ideal for predicting numerical values, such as financial metrics or future expenses. Remember your ‘line of best fit’ at school? Thats a linear regression.
Classification models categorise data into defined groups, such as distinguishing between high-risk and low-risk transactions or identifying fraudulent versus legitimate activities. In audit, classification models can identify valuable trends in data, supporting design effectiveness testing by pinpointing which attributes (e.g., customer demographics or transaction types) are most associated with specific outcomes like customer default or high-risk activity. Such insights can help auditors focus on the most influential factors during testing.
Unsupervised learning takes a different approach, working with unlabelled data without predefined outcomes. Instead, it identifies structures and patterns within any data it is given. A common model used in unsupervised learning is clustering, which groups data points based on similarity. Clustering is invaluable for identifying outliers or unusual patterns, such as spotting transactions that don’t align with typical behaviour, which may indicate fraud. In finance, clustering can help group customers or accounts based on spending patterns, risk profiles, or transaction behaviours, allowing for more targeted reviews or personalised strategies.
Semi-supervised learning combines supervised and unsupervised methods, using a small amount of labelled data alongside a larger unlabelled dataset. By combining both known cases and a wider unlabelled pool, semi-supervised models can make more accurate predictions, offering insights even with limited labelled data. For auditors, this hybrid approach can reveal important trends without requiring extensive manual labelling.
How Machine Learning Works: A Step-by-Step Overview
The machine learning process begins with data preparation. You need to get some data for the model to learn from, or to analyse directly. Before training or using data in a model, it’s essential to ensure the data is accurate, consistent, and free from errors or duplicates. For auditors, this might mean verifying transaction data, addressing missing values, or standardising categories. Good data preparation is crucial, as poor data quality can lead to misleading results and reduce the model’s reliability.
Next is data partitioning, where the dataset is divided into training and testing sets. The model learns from the training data and is later tested on unseen data to see how well it generalises. This helps auditors determine if the model’s predictions are reliable. This step is straightforward with data sampling tools, typically splitting the data 80/20, with 80% used for training and 20% for testing. However, a data scientist will choose the threshold that leads to the best results, 70:30 is not uncommon for example. In platforms like KNIME, partitioning the data is really easy to do.
Feature engineering is the next phase, where relevant features (variables) are selected or created to improve the model’s performance. For auditors, this might mean deriving new features, such as calculating expense variances or normalising transaction amounts to detect anomalies. This step often requires domain knowledge, as selecting the right features significantly enhances the model’s accuracy and relevance to audit tasks.
Using Advanced Classification Models Like Neural Networks
Once data is ready, the model training phase begins. In platforms like KNIME, model training uses “learner” nodes where the model learns patterns, “predictor” nodes to apply the model to new data, and “scorer” nodes to evaluate performance through metrics like accuracy and precision. For more complex tasks, auditors can use neural networks, a form of advanced classification model designed to recognise intricate patterns in data. Neural networks, also the foundation of large language models (LLMs), consist of interconnected nodes (or “neurons”) that process information in multiple layers, very similar in the way that the brain does. This structure allows neural networks to handle complex, high-dimensional data, making them suitable for tasks like fraud detection, where subtle patterns need to be identified. In finance, neural networks can automatically and reliably classify transactions, sorting them by categories such as revenue, expenses, or risk levels, thus simplifying routine classification tasks.
LLMs, a type of neural network specialised in processing text, have become highly valuable in audit and finance. These models can interpret unstructured text data, classify it into relevant categories, or even summarise lengthy documents. In practice, an LLM could help auditors by analysing contracts, identifying high-risk terms, or flagging potential compliance issues, allowing teams to focus on review rather than manual data extraction. These are the models that will change our working world. KNIME allows you to access LLMs like OpenAI GPT-4 and Anthropics Claude directly via API nodes.
Evaluating Model Performance and Addressing Limitations
Once a model is trained, it’s vital to evaluate its performance. Key metrics include accuracy, precision, and recall, each providing insight into the model’s effectiveness. Accuracy measures how often predictions are correct, precision reveals the proportion of true positives among positive predictions, and recall shows the proportion of true positives among all actual positives. For auditors, these metrics help assess whether the model is reliably identifying risks or fraudulent activities, helping them choose the right balance between catching issues and minimising false alarms.
However, machine learning has limitations that must be considered. Bias can occur if the training data is skewed, which may lead to a model that doesn’t generalise well to other data. For example, a model trained only on transactions from a specific department may not perform well across other departments. Overfitting is another common issue, where the model learns the training data too closely, leading to poor performance on new data. There are techniques however that can help reduce overfitting and improve robustness.
Use Cases for Machine Learning in Finance and Audit
Machine learning provides numerous practical applications in finance and audit. For example, auto-classifying transactions helps finance teams quickly categorise large volumes of transactions, saving time and reducing human error. Models trained on historical data can sort transactions into categories like income, expenses, or capital expenditure, and can even flag transactions that deviate from expected patterns, assisting with compliance.
Another powerful use case is design effectiveness testing. By training a classification model on data related to customer defaults, an auditor can identify which customer attributes (e.g., credit history, income level) are most associated with high default risk. This insight can inform controls and risk management strategies, allowing for targeted interventions to mitigate potential issues.
Additionally, anomaly detection is invaluable for spotting outliers, particularly in fraud prevention. Clustering techniques can help identify transactions that don’t fit normal patterns, signalling possible fraudulent activity. In audit, these outlier detection capabilities provide a data-driven approach to identifying areas that may require deeper investigation.
Embracing Machine Learning in Audit and Finance
Machine learning is becoming increasingly accessible, even for non-technical users, thanks to platforms like KNIME that offer intuitive tools for building models. For auditors and finance teams, starting with simpler models and progressing to more sophisticated techniques can unlock powerful insights, enhancing the quality and efficiency of audit processes. By understanding these core concepts and applications, finance professionals can harness the potential of machine learning to make better-informed decisions, detect anomalies more effectively, and ultimately add value through data-driven insights.
Jamie is founder of Bloch.ai : The Applied Innovation Specialists, and a visiting fellow in Enterprise AI at Manchester Metropolitan University. Follow Jamie here and on LinkedIn: Jamie Crossman-Smith | LinkedIn
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