Machine learning (ML) is a component of artificial intelligence (AI) that allows computer algorithms to make accurate predictions when exposed to new data.
Data scientists create these machine learning models by training them on existing or newly created data sets.
It’s possible for AI to learn to interpret new data and to dramatically adapt to changing stimuli via deep neural networks, natural language processing, image processing or traditional machine learning techniques.
ML represents a much-needed technological evolution in a world where data plays a pivotal role in both businesses, lifestyle and society.
In this article, you’ll learn the the benefits and main use cases for machine learning within the finance industry.
ML In the Finance Industry
ML’s ability to solve logical problems is well suited to the finance industry where numerical datasets and the quantity and quality of data reign supreme.
Machine learning blends with a range of processes in the finance industry to gather, organise, interpret and implement colossal volumes of data, learning from changes to adapt financial services in a sensitive and timely fashion.
The Benefits of Machine Learning In The Finance Industry
The benefits of ML in the finance industry fundamentally revolve around its ability to work with huge datasets quickly and without error.
Working with Big Data
Major competitors in the finance industry are scrambling for ways to gain an edge over each other using data and this is a lucrative business.
Whilst traditional financial statistics may have been confined to a fairly narrow range of data points with high computing times/costs, we can now easily make accurate predictions for financial processes across trading, credit and lending, banking, security and process optimisation.
Reducing Human Error
Human error within the finance and banking industries was a big problem during the 1950s and 1960s. Analogue instruments and paperwork has now been replaced by computerised systems significantly reducing human error.
Additionally, effective machine learning models trained on large quantities of data can yield lower error rates compared to humans performing the same tasks.
A Well Established Data Infrastructure
In the finance industry, there are extensively documented APIs and the established data infrastructure provides data scientists & machine learning practitioners an abundance of real-time markets to apply modelling and machine learning techniques on.
Reducing Work Loads
AI performs exceptionally well at high volume labour intensive or repetitive tasks such as formatting and cleaning data sets or performing millions of relatively simple predictions in a short period of time.
Machine learning focused businesses are more efficient, have less operational costs and therefore human resources can be directed to areas of the business where they can add greater value.
These areas include:
- Customer-facing roles.
- Creative tasks.
- Business strategy.
Transparent and Bias-Free (Sometimes)
Judgements being made by ML algorithms have the potential to be more transparent than human judgement, however this depends on:
- Whether the training data is truly a representative sample of the existing population.
- How biased/unbiased the training data is.
- The size of the training data (generally the more, the better).
- Whether the model has overfitted on the training data is able to generalise beyond the training samples, you might hear it being called “a generalisable model”.
- Whether data leakage has occurred during model training or whether the training features truly represent the entire the environment’s context.
However, there is renewed pressure to remove biases in ML algorithms and where judgement calls are not being made about humans, e.g. in stock equations, ML has the potential to be more transparent – ML cannot fiddle the balance books of a business in the same way as humans can.
Creating Value With Greater Predictive Power
Machine learning models can create more value for financial institutions and their clients, for example:
- Investment portfolios can react quicker to market forces to increase their ROI. This has been especially prevalent with the rise of robo-investing services.
- Lenders can more accurately tailor financial products to clients with recommendation systems via the Surprise library.
- Banks can better predict which transactions are fraudulent vs not fraudulent transactions with machine learning classification.
- Loan companies can predict which customers would or would not be able to repay their loans, allowing them to solely lend money to customers that are statistically likely to pay the lender back.
How Is Machine Learning Used In The Finance Industry?
Financial trading relies on accurately assessing stock market dynamics and signals. Traditionally carried out manually by traders or hedge-fund managers, etc, algorithmic trading programs that use deep learning and predictive analysis are being used to sift through historical price data, market sentiments and other data to trade automatically with superhuman precision.
Not only can algorithmic trading take into account a greater volume of variables when making a trading decision, but it can also place orders at extremely time-sensitive intervals to maximise transient trading opportunities.
Algorithmic trading further circumvents human error and unnecessary risk-taking, or gambling, when trading in a volatile environment.Tweet
Trading + Time Series Analysis Python Packages
Research and Deep Learning
To build comprehensive and accurate predictive models, they need to be trained and cross validated on a large amount of historical data.
Deep learning has become a recent trend within time series analysis, you’ll find several python packages below which utilise this technology:
DeepStock is a series of technical experiments built-in Python for learning from historical price data and market sentiments. Developed in coordination with the Google Brain team, this program can analyse daily news headlines and articles and combine data with company prices and stocks to predict future stock values.
This neural network is designed to teach the basics of how ML can be used to predict stock prices and enhance trading strategy. It is not so much designed for automating trades but it does provide a valuable resource into the predictive power of ML neural networks.
This GitHub project contains tools for grouping and curating quantitative financial papers for research.
A set of tools for analysing high-frequency trades from the Nasdaq market. Also contains tools to cluster stock analysis protocols, allowing users to simultaneously analyse thousands of ITCH files – descriptions of changes to the Nasdaq market.
Market sentiments play a crucial role in financial markets and stocks and may be contained in:
- News reports.
- Quarterly company reviews.
- Financial Statements.
- Social Media platforms such as Twitter, YouTube or TikTok.
Formerly, it’d be the trader’s job to manually track market sentiments and adjust trading strategies accordingly. ML can assist in extracting sentiment from textual data via natural language processing.
Textual data can then be provided to machine learning models in the form of vectorised text or vector embeddings, removing the need for humans to trawl through financial news and reports manually.
This GitHub project uses data mining and sentiment analysis to compile and measure the volume of negative buzzwords that surround companies both online and within their own financial statements. Words like ‘loss’, ‘decreasing’, ‘sued’, ‘problems’ and ‘adversity’ can be analysed alongside their context to automatically build a picture of the sentiments surrounding a business or industry.
Process automation via ML can fundamentally change the way the workforce is structured in the financial industry. Businesses can lean out labour-intensive rudimentary tasks, instead using AI to take up the slack. This provides a huge opportunity for cost savings, allowing businesses to retrain and redirect staff whilst compacting the labour force.
The chatbot market is set to grow by 30% by 2024 as more than 80% of businesses are forecast to use them in one way or another. Chatbots are programmed using natural language processing (NLP) techniques allowing them to learn from the ways humans converse. Chatbots may eventually replace call centre workers too, DeepMind’s WaveNet neural network is using deep learning to learn how to listen and speak like a human.
Document Creation / Paperwork
Business documents have always been a major business gripe and although data entry and reporting has been streamlined by standard computers, processing the information has still largely been a human task. AI that uses NLP can process documents at unbelievable speeds and without error.
Recently, JPMorgan famously used an ML AI named COiN to process 360,000 hours worth of paperwork in mere seconds. Similarly, financial services company BNY Mellon deployed AI to learn from typical human mistakes in payment processing and automatically check and clean datasets, cutting processing times by 30% and cutting costs by $300,000 in year 1.
Fraud and Security
Fraud is an enormous ongoing problem in banking and finance with credit card losses in the US alone reaching $12 billion by 2020. Out of all the applications of AI and ML in finance, fraud and security are at the top of the agenda for both multinationals, SMEs and startups.
The nature of fraud is that it’s an ongoing challenge, as technology advances, so does security, but also so does the sophistication of scamming techniques. Not only can ML systems efficiently eliminate fraudulent actions much faster than any human can, but it can also route out false positives that prevent legitimate payments from being made.
The first key aspect of fraud detection is detecting anomalies. As scammers improve their methods, they are finding ways to get around classic security measures, e.g. 3D security and 2-factor authentication. ML can learn and adapt to new anomalies in data left behind by scammers.
- It’s possible to spot anomalies (discords) within time series data using a matrix profile.
- Also its possible to automatically detect motifs/similar patterns within time series data using a matrix profile.
Anomaly detection can also be implemented retrospectively, e.g. HSBC used an AI called Quantexa to analyse billions of financial records to discover the character of money-laundering events to then train models to detect them in the future using predictive analysis.
GitHub Repositories on Anomaly Detection:
Whilst anomaly detection surfaces anomalies in real-time, predictive analysis can help flag actions that alert a system and help it predict whether or not it is acting fraudulently before a payment is made. The system can then either freeze the account or ask for extra verification, etc, before proceeding.
This lowers the chance as false positives, anomaly detection may simply stop a payment that looks risky whereas predictive analysis may simply flag it up for higher security.
Shape Security is a major company that leverages ML in predictive analysis to clamp down on credit fraud. Their Credential Blackfish Network also helps protect against account takeover and helped protect a major US bank against 1 million credit stuffing attacks in the first weeks of deployment.
This repository provides predictive analysis models with ML that can effectively discern between anomalous fraudulent payments and those that display some but not all the characteristics of an anomalous payment and actually turn out to be legitimate.
A very interesting project, this repository centres around the US company Enron, one of America’s largest companies, that collapsed totally in 2002 following ongoing corporate fraud. This project attempted to build an algorithm that could use public company information, e.g. reports and emails, to determine which employees could have played a hand in the events that lead to Enron’s collapse.
Machine learning is naturally a good fit for finance due to the overwhelming availability of well-structured data. Not only can ML models work with data inducted in real time, but they can also scrape and transform vast amounts of historical data to learn about processes and financial events. With the power of data and ML, finance companies have been able to streamline their services and pass value onto customers and clients.
Risk is reduced, lowering the prevalence of fraud whilst permitting legitimate payments to go ahead without hindrance or delay.
In the stock market, machine learning is automating the trading process, enabling extremely sensitive and efficient high-frequency trading that blows manual trading methods out of the water.
As computing power increases, it won’t just be multinationals and other big businesses that can take part in this technological renaissance, but SMEs and startups too.
Machine Learning In Finance FAQ
How Do You Use AI In Finance?
AI is used both in the backend of banking systems and financial services and in the frontend of apps and customer service systems, e.g. in the form of chatbots and robo-advisors. By eliminating human error and bias, AI is perfectly suited to an industry where logic and objectivity are top priorities.
What Is Sentiment Analysis?
Sentiment analysis uses natural language processing techniques (NLP) to read and identify the characteristics of textual information. This can help uncover trending buzzwords surrounding topics, whether positive or negative. For example, a failing company may often be described with words such as ‘problematic’, ‘falling’, ‘troublesome’, ‘turbulent ‘,’ ‘adverse’, ‘fraud’, ‘loss’, etc. These types of words can be analysed alongside their contexts to discern the sentiments surrounding the company, thus informing trading decisions. It is much quicker than manually trawling news articles and statements.
Is Machine Learning Useful in Finance?
Machine learning provides a powerful and scalable means to work with huge financial datasets without constant human supervision or labour. AI using machine learning algorithms can read and interpret big data and adapt accordingly to take into account any patterns it discovers. ML can be used to detect fraud anomalies, trading signals, market moves and market sentiments.