Machine Learning in Finance

The financial and banking sectors are incredibly data-rich, with millions of transactions and transfers occurring every day. Data-led decisions are an integral part of the financial sector, whether in banks, insurance providers, lenders, or stock traders. Machine learning models are already leveraged in the financial sector to automate manual processes, help inform decision making, and enhance customer experience. More than ever, financial organisations are leveraging huge arrays of historical data to train new machine learning models. 

The tasks performed by models in finance play to the strengths of machine learning. Models are used to gain insight from complex datasets, whether automating credit scores and risk profiles or recognising emerging trends in the stock market. By streamlining services with machine learning techniques, organisations can save resources and clients can save time. In many cases such as fraud detection, machine learning models are much more effective than human performance 

This guide explores machine learning in finance, the different ways it’s used, and the benefits it brings to the sector.  

The benefits of machine learning in finance

Whether automating manual processes or predicting market fluctuations, machine learning models are increasingly used across the financial sector. Machine learning models are generally used to classify data or to make data-led decisions and predictions. The financial sector is incredibly data-rich, with a range of information that can be utilised to train machine learning models. Organisations can leverage value from this data by training and deploying models, automating complex processes. 

One of the main benefits of using machine learning in finance is in the automation of manual tasks. Customer service, underwriting, and fraud detection are examples of the different areas machine learning can enhance and improve. Models perform the menial elements of tasks such as underwriting or fraud detection with much more efficiency compared to humans. Datasets can often be complex, but machine learning models can process this information efficiently and in real time. The result is a more effective use of resources, lowering the need for humans to perform inefficient tasks.  

The benefits of machine learning in finance includes: 

  • More efficient use of resources by automating processes which can be resource-intensive for humans to perform. 
  • Machine learning models can be trained effectively by leveraging the huge arrays of historical data. 
  • Better customer experience processes through chatbots and automated advisors. Models can effectively deal with basic issues and queries, with human involvement included when needed. 
  • Machine learning models are also inherently scalable, so can meet a surge in demand.  

Machine learning applications in finance

Finance is a data-rich sector, and machine learning is used to automate and perform a range of tasks. A popular use of machine learning in finance is to automate manual or menial tasks. Increasingly, machine learning is powering services like call centres, customer chatbots, and administrative tasks. Machine learning in banking can be leveraged to detect fraud and suspicious account behaviour, and automate the underwriting process for mortgages and loans.  

Applications of machine learning in finance includes: 

  • Customer service processes including machine-learning driven chatbots and advisors. 
  • Automated fraud detection. 
  • Automated underwriting for insurers and lenders. 
  • Automated contract review through natural language processing techniques. 
  • Market trend prediction and automated stock trading. 

Machine-learning driven chatbots and advisors

Customer service is increasingly being improved by machine learning models through the use of chatbots and messaging services. Machine learning is powering advances in natural language processing that improve interactions between humans and systems. Machine learning models are increasingly used to answer basic customer questions or perform tasks like user identification. This streamlines the customer service process, as customers are able to solve often complex issues without speaking to a human.  

Models can be used to answer frequently asked questions, but also helps users with their accounts. If human interaction is needed, the machine learning model has covered much of the basic steps already. The result is a more efficient use of human resources.  

Beyond chatbots, machine learning models can be used to recommend financial products to users and manage portfolios. Using parameters such as level of risk, investment aims, and amount of assets, a machine learning model can be trained to optimise a user’s portfolio. The models can diversify investments to achieve user-specified goals. Other models can be used to personalise products to meet a user’s situation or requirements. Models can recommend products based on the details inputted in an application form for example.  

Automated fraud detection

A valuable role of machine learning models is in outlier detection and analysis, or the identification of anomalous data. Machine learning models are trained to understand the patterns and trends in a set of data, so can therefore detect outliers. These algorithms can be used to automatically detect suspicious activity within bank accounts, or unusual payment behaviour. The model will understand what is classed as normal behaviour by a user or client.  

Machine learning in banking can be used to assess transactions as they happen, actively blocking any the model deem to be fraudulent. Anything that is deemed outside of normal behaviour can be flagged and acted on. For example, a credit card used to make an unusual purchase, in a different time zone or international location. The payment card can be frozen in real time, in case the account is being used for fraudulent activities. 

Deep learning techniques are often used to create fraud detection models. Deep learning models have a multi-layered architecture which can process many different features of a transaction simultaneously. This means a transaction can be analysed for multiple features of a fraudulent activity in real time. 

Automated underwriting for mortgages and loans

Insurance, loans, and mortgages all require an underwriting process, a major part of which is understanding the degree of risk for the insurer or lender. Machine learning models are used to streamline the underwriting process. Models can be trained to take into account a range of different datasets to measure the degree of risk. This could include historic bill data or credit scores. 

Models can be trained on the huge array of historic data held by a lender or insurer. The aim is to make the process as efficient and automated as possible. 

Credit scores are a key element of the underwriting process, and can be used to understand the risk profile of a potential client. The scores draw on personal data and historic financial behaviour, such as loans, payments to utility providers, or use of credit cards. Machine learning models can be trained to perform credit scoring, fast tracking the process.  

Natural language processing techniques 

Machine learning models are an important part of natural language processing, as the model can be trained on language to improve iteratively. The result is software that can recognise and understand human language, powered by machine learning. Beyond personal assistants and chatbots, natural language processing is useful for automating many menial tasks in the financial and banking sector. 

Reviewing legal documents, corporate contracts and agreements can be a labour-intensive process when done by a human. Machine learning models can be trained to process these documents, streamlining the process of understanding legal agreements.  

Predict market trends and automate stock trading

Machine learning models are utilised to predict trends and patterns that may affect stock prices, and as a result automate stock trading through algorithms. Machine learning in finance is leveraged to analyse and understand a huge array of financial and consumer data in real time. Models can then be used to understand emerging and underlying trends. In this way a machine learning model can be used to gain advantage in the financial sector. 

Deployment of machine learning in finance

Seldon moves machine learning from POC to production to scale, reducing time-to-value so models can get to work up to 85% quicker. In this rapidly changing environment, Seldon can give you the edge you need to supercharge your performance.

With Seldon Deploy, your financial organisation can efficiently manage and monitor machine learning, minimise risk, and understand how machine learning models impact decisions and business processes. Meaning you know your team has done its due diligence in creating a more equitable system while boosting performance.

Deploy machine learning in your organisations effectively and efficiently. Talk to our team about machine learning solutions today.