How is machine learning used in finance?

How is Machine Learning Used in Finance

With artificial intelligence (AI), robotic process automation (RPA) and smart analytics powering the future of finance and accounting, manual bookkeeping and spreadsheet-based solutions will soon be a thing of the past.

Under the AI umbrella is machine learning (ML), a concept that uses statistical models and sample data, learns from experience, gains new knowledge, and performs specific tasks without being explicitly programmed to do so.

From fraud detection and optimizing customer care to making informed predictions based on statistical models, machine learning (ML) is reshaping the financial world. In this article, we will define machine learning and discuss how the financial sector leverages machine learning models to streamline processes, improve risk management and compliance, reduce operational costs, and boost productivity.

What is Machine Learning?

Machine learning (ML) is a subset of data science and an application of artificial intelligence. As a data analytics technique, it “learns” from experience using a data analysis process, algorithms, and computational methods without explicit programming. Not only does machine learning recognize and apply patterns, but it uses those patterns to derive its own algorithms, retrieves feedback, and uses the feedback to refine those algorithms.

ML has various applications across different industries. Examples of machine learning in sales include simplifying product marketing and improving the precision of sales forecasts. In industries that perform time-intensive documentation, machine learning simplifies data entry tasks using predictive modeling and algorithms, decreasing data duplication and inaccuracies. The marketing sector uses machine learning to gather behavioral patterns of customers and predict the probability of conversion more accurately.

The Ways Machine Learning is Used in Finance

In finance and accounting, machine learning increases the accuracy or financial models and rules, automates data analysis, and interprets financial activities. Machine learning for finance is also most suitable for activities that require handling massive volumes of complex information, such as historical financial data. Here’s a closer look at the applications of machine learning in finance.

Reduce Operating Costs Through Process Automation

Machine learning, artificial intelligence, and automation are the technologies transforming the finance sector. While machine learning works to understand financial data and drive future intelligence, automation streamlines tasks to speed up workflows. In the finance sector, applications of machine learning and process automation include chatbots, paperwork automations, fraud detection, financial monitoring, and network security.

Automating financial processes, such as accounts payable, tax accounting, and reconciliations, promotes efficiency and reduces errors. Routine processes, such as invoice collection and verification, outbound payment scheduling, invoice creation, and transactional data entry, are also all good candidates for automation. By automating these activities, financial companies can reduce operating costs associated with time-consuming tasks such as these. The technology relieves the company’s internal resources of burdensome work and frees employees to focus on high-value work.

Alternatively, automation can also be used to reduce costs by allowing companies to maintain a smaller in-house finance department and outsource other finance and accounting activities. Accounting work will always require a certain degree of emotional intelligence. Therefore, contrary to myth, AI and machine learning will not replace accountants entirely but will be a strategic tool used to relieve them of tedious data preparation and manual transactional activities.

Machine learning also helps with the classification of transactions by using inductive reasoning. Historical transactions become the source data that helps the computer make predictions and apply them to future transactions as they are recorded.

Improve Financial Planning and Analysis (FP&A)

Machine learning and advanced analytics can be used in financial planning and analysis to define the data models used to make financial forecasts. FP&A teams can take these predictions and use their talents and critical-thinking skills to measure and compare the forecasts.

Compared to traditional methods, machine learning, data mining, and predictive analytics result in more accurate finance predictions, which drive insightful decision-making. Because there are always inherent biases that may affect the quality of the predictions, FP&A professionals still need to apply internal controls and ensure proper governance. To build a strong foundation on which to build financial decisions, ensure the data is clean, maintain data integrity, and adopt technology that facilitates business process collaboration.

Enhance Fraud Detection and Risk Management Capabilities

The traditional process of fraud detection is time-consuming, involving manual interaction and evaluation of massive numbers of transactions in real time. With machine learning, algorithms and models remain effective despite increasing data sets. While traditional fraud detection approaches, such as using rules or query transactions, are still being used today, ML can help detect the probability of fraud faster and at scale.

In banking and insurance risk management, machine learning algorithms are poised to replace statistical risk management to determine the level of risk associated with customers. ML gathers insight from analyzing spending behavior and patterns to provide actionable intelligence that can be used to make better decisions. Utilizing complex algorithms that use a neural network and deep learning, ML analyzes large data sets for patterns, collecting and categorizing them at superspeed to learn how to respond to different situations.

In the financial services sector, machine learning can be used to speed up credit scoring to determine creditworthiness, assign risk scores to loan applicants, and predict which potential loan clients are at risk of defaulting. For an insurance company, ML can be used to predict future probability of generating losses and how to calculate premium rates. Machine learning technology can also be used in algorithmic trading to identify stock market opportunities and trading strategies.

Achieve Regulatory Compliance

Changes in regulations can be labor-intensive, making achieving compliance the traditional way burdensome. With regulatory rules changing constantly, compliance professionals need to keep up. Artificial intelligence, machine learning, and automation improve regulatory and compliance processes by streamlining processes, providing more accurate reporting, identifying systematic issues that increase risk, and detecting anomalies that indicate money laundering or fraud.

Reducing the need for human intervention also means reducing the risk of human error. AI, ML, and automation help take much of the manual processing activities away from compliance teams, allowing them to focus on detecting suspicious and fraudulent events. Automating compliance protocols also leads to bigger costs savings and more accurate analysis of big data. And thanks to real time risk detection, organizations become more efficient at achieving regulatory compliance. 

Conclusion

Date science continues to evolve, allowing technology like artificial intelligence and machine learning to transform sectors such as finance and accounting. More applications of ML continue to be discovered in finance, and the value of machine learning becomes more recognized within the financial ecosystem. When used wisely, leveraging machine learning in finance can lead to better decision-making, improved output accuracy, faster analysis, elimination of human errors, and increased consistency. The key is to select processes that can be enhanced by the ability of AI and ML to learn from financial data and use it to execute future processes more effectively.


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