Machine learning (ML) is transforming the finance sector, as a growing number of businesses begin to adopt machine learning technology to automate processes, increase their productivity, and improve decision-making.
Banks, Fintechs, insurance brokers, and other companies offering financial services, are using machine learning algorithms to predict financial risk, automate repetitive tasks, and receive real-time investment guidance.
By nature, financial institutions handle vast amounts of complex data — setting the perfect scenario for machine learning, which requires large datasets for machines to learn from.
In recent years, machine learning has become even more accessible, thanks to a variety of no-code, low-code tools on the market. MonkeyLearn, for example, allows companies to easily analyze text data using pre-trained models or creating customized solutions in a user-friendly interface.
Below, learn how machine learning technology is boosting the finance industry, and explore some of its current applications:
Machine learning is a subset of artificial intelligence that allows machines to learn from patterns in data and improve over time.
By feeding machine learning algorithms data samples, a model automatically processes this information using natural language processing techniques, then learns to recognize patterns and make predictions when faced with similar but unseen data.
In an industry that generates a lot of valuable data, machine learning has a huge amount of potential. A recent AI in finance survey shows that AI is fast becoming mainstream in financial services, with 85% of all respondents currently using some form of AI, equipped with machine learning algorithms.
The benefits of using machine learning in finance include:
When it comes to implementing a machine learning solution, companies can opt to use open-source libraries to build machine learning software or buy a SaaS tool.
Building machine learning software can take several months, and requires huge upfront investments (like hiring a team of data science experts and developers). Opting to buy cloud-based AI tools like MonkeyLearn is a lot faster, and the platform provides a suite of pre-trained tools for you to get started right away.
In short, SaaS solutions are cost-effective and can be implemented within weeks, allowing financial services to gain valuable insights from unstructured data as soon as possible.
In finance and insurance, employees spend more than half their time collecting and processing data.
By implementing machine learning tools, companies can automate a large part of routine and time-consuming processes, increase productivity, save costs, and free up employees so they can focus on higher value-added tasks.
For example, financial services are turning towards AI and machine learning to automate client onboarding, a complex and lengthy process that usually involves collecting, revising, and processing a high number of documents across different departments. The Standard Bank in South Africa, for instance, was able to reduce account opening times from 23 days to under 5 minutes through intelligent automation.
Archer, a financial services company that simplifies processes for investment managers, also uses machine learning. By automatically categorizing incoming support tickets, most of which contain time-sensitive requests, Archer can instantly route queries to the right agents, speeding up response times by 65%.
Text analysis tools use machine learning to make sense of unstructured data. These tools are helping companies in the finance industry gain value from their data in a fast and cost-effective way while reducing human error. Applications range from automatically classifying data in emails, contracts, and reports, to extracting relevant information from legal documents, statements, and bills.
In 2007, JP Morgan Chase implemented a machine learning-based program named COIN to shorten the time it takes to review documents and decrease its number of loan-servicing mistakes in new wholesale contracts.
The software extracts repeated clauses within contracts and classifies them into one of about 150 different categories.
Robo-advisors are one of the most popular applications of machine learning in finance.
A robo-advisor is an intelligent system that uses machine learning algorithms and statistics. Robo-advisors are often used to provide investment advice and portfolio management services to clients. By processing large amounts of data in a short space of time, robo-advisors can help customers stay ahead and make smart and well-informed investment decisions.
Japanese firm Nomura Asset Management implemented AI for portfolio management and is already seeing increased returns from algorithm-driven financial advice. Using machine learning to recognize patterns in large amounts of data sources (like news sites, blogs, and real-time financial data), the firm automatically creates portfolios.
Algorithmic trading helps businesses make fast and highly accurate trading decisions. Machine learning algorithms are trained to identify trading opportunities, by recognizing patterns and behaviors in historical data.
This gives businesses a competitive advantage since it allows them to simultaneously monitor and analyze enormous amounts of data in real time – something that exceeds human capabilities.
Using algorithms also helps reduce human error. Humans are often driven by emotions when it comes to making investments. Machine learning algorithms, on the contrary, are free of any bias, making them a powerful ally in finance.
During the coronavirus pandemic, investment banks reported a rise in the adoption of algorithms for trading: as the crisis spread, machines were able to successfully adjust to the changing and volatile environment.
The use of machine learning bots is gaining momentum in the banking industry, helping companies create better experiences in customer service while saving money on call centers.
Chatbots, for instance, are equipped with machine learning algorithms and trained to handle common and non-critical customer queries around the clock, scaling support, and improving customer satisfaction.
Virtual assistants are also being used to automate tasks like gathering client contact information or searching for historical transaction data.
The Bank of New York Mellon Corp implemented more than 200 bots across different operations, with the goal of reducing the time agents spend on manual tasks. One bot, trained to reply to customer requests on financial statements, helped them cut down response times from between 6 and 10 days to just 24 hours.
There is a huge amount of risk involved in the finance sector: market risk, credit risk, operational risk, regulatory risk, and so on.
In the last few years, financial companies have increasingly been adopting AI and machine learning to improve risk management, helping them to detect and quantify risks, and make the right decisions.
Machine learning algorithms can constantly monitor and analyze large sets of data, in order to spot trends and patterns and deliver critical information in real-time.
A recent report shows that 47% of companies experienced fraud in the past 24 months. Security threats are a serious concern in finance, with fraud costing companies 1.78% of revenue.
Machine learning is now a key player in the constant battle against fraudulent transactions and money laundering. This technology can detect anomalies in large sets of historical data, and monitor operations in real-time for suspicious behavior, alerting financial services to security threats and illegal activities in real time.
Stripe Radar is a collection of machine learning tools that help businesses detect and prevent fraud. It scans every card payment, aggregates information into behavioral signals that are predictive of fraud, and blocks payments if there’s a high probability of payment being fraudulent.
Machine learning is fast becoming mainstream in the finance world, as companies start to realize its huge potential.
Thinking of implementing machine learning into your finance operations?
MonkeyLearn's no-code low-code machine learning tools are fast to implement and easy to use. Learn how we’re already helping financial services automate their processes, save time, and act fast.
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August 21st, 2020