Machine Learning and Deep Learning in Financial Services

Machine Learning and Deep Learning in Financial Services

Robert M. Robert M.

Robert M.

VP Marketing Technology, CDP & Analytics

发布日期: 2019年2月23日
+ 关注

Machine Learning and Deep Learning in Financial Services

Today the financial services industry is facing new challenges. Institutions are trying to figure out next-generation digital platforms, mobile transactions, and online security. They are looking for cost-effective solutions. Machine learning and deep learning, both subsets of artificial intelligence, are emerging technologies that can help.

Artificial intelligence (AI) has been a hot area of research since the 1950s. However, the advances in machine learning (ML) and deep learning (DL) have made artificial intelligence cost-effective and practical. Industries like manufacturing, health care, public sector, and retail have already started to use AI. Around 90 percent of business leaders expect AI to have a positive impact. It seems prudent for the financial industry to adopt machine learning and deep learning.

Definitions of AI, ML and DL

When talking about artificial intelligence, machine learning, and deep learning, the terms are often used interchangeably. For most people in the financial industry, the hard-line definitions are not important. But a basic understanding of the terms can help put everything in context.

Artificial intelligence is the larger discipline that encompasses subcategories like computer vision, natural language processing, robotics, virtual intelligence, machine learning, and more. The purpose of artificial intelligence is to create human-like thinking machines.

Machine learning is a subset of AI. It uses organized training data to create models that can make intelligent decisions. Deep learning can be considered a subset of machine learning where the models require less human intervention. However, it also means deep learning requires more data than machine learning to create viable models.

Application of Machine Learning and Deep Learning in Financial Services

Financial services collect a lot of data about their customers. Both machine learning and deep learning depend on big data. The data helps the algorithms create the right models that can make intelligent decisions. So, naturally, the financial services industry is a great fit for ML/DL algorithms.

Here are some ways financial services are using machine learning and deep learning:

Digital Assistance and Automation

The financial services industry still deals with a large number of manual tasks every day. Human labor for transaction recording or customer support is expensive. So there is a lot of opportunity for automation. Businesses were often reluctant to use automation tools due to their limitations. For example, financial institutions have tried using chatbots to answer customer questions in the past. But the answers were limited. Also, the chatbots could only answer straightforward questions. With machine learning and deep learning, the new-generation of intelligent bots can address more nuanced financial queries. A recent example is Wells Fargo’s Facebook Messenger chatbot that is capable of complex conversations with its customers.

Cybersecurity and Fraud Detection

Online financial transactions are fraught with cybersecurity threats and possibilities of fraud. It’s impossible to monitor all the transactions to detect an anomaly. Machine learning and deep learning algorithms can provide institutions with both network security and financial monitoring. For example, crimes like money laundering have evolved to use automated processes. Cybercriminals use micro-payment bots to avoid getting caught; AI-based techniques are helping automate financial crime detection.

Financial Forecasting

Financial forecasting is part of any modern business. Yet it’s an extremely difficult science to master. Financial services use past information to predict future growth possibilities. Forecasters depend on industry trends. Older industries have decades of information. However, for new industries with fast growth rates, it’s difficult to create reliable models. Forecasters haven’t accumulated enough knowledge and experience to be effective. But machine learning and deep learning algorithms can pick up even slight variations in data to create more reliable models. For example, Uber uses machine learning for financial forecasting.

Algorithmic Trading

Algorithmic trading is basically automated trading. Machine learning and deep learning have ushered in a new age of algorithmic trading. Quantitative hedge funds are using machine learning and deep learning to make more accurate decisions.

Mobile Payments

Machine learning and deep learning are helping the growth of mobile payment technology like Apple Pay. Over the last decade, mobile devices have spread all over the globe. Now with the help of AI-based algorithms those devices can turn into financial transaction machines. Machine learning and deep learning algorithms make mobile payments smoother than credit cards or other forms of payments. There is less chance of errors because mobile payment is using ML/DL-based fraud detection to prevent unauthorized access which is much better than traditional methods credit card companies use.

Loans and Insurance

Artificial intelligence tools are giving banks, credit card companies, and insurance companies a new way to measure the risk profiles of their customers. Previously, these organizations had to make decisions on a limited number of data points. They had the information on their customers. But there was no good way to parse that information into meaningful knowledge. Machine learning and deep learning algorithms are better at using the data to create better risk models

Pros and Cons for ML/DL in Financial Services

Financial services can benefit from using machine learning and deep learning. But there are some risks too. Below are the pros and cons:

Pros

  • Cost-Effective: Machine learning and deep learning reduces labor costs through automation of human labor. So financial services can save significant money.
  • Operational Efficiency: Machine learning and deep learning streamlines processes and increases productivity and efficiency of any financial operation.
  • Security: Machine learning and deep learning are providing both network security and fraud prevention capabilities for financial institutions.

Cons

  • Fraud: Currently, machine learning and deep learning are being used to prevent frauds. But it’s possible that cybercriminals can use ML/DL-based tools to actually defraud people in the future.
  • Legal Issues: When a person in a financial institute makes a mistake, the institute is held legally responsible. So, the legal question is whether the same applies to the mistake of an algorithm.
  • Data Privacy: Machine learning and deep learning algorithms are dependent on data. But financial data is private information. So machine learning and deep learning tools will raise privacy concerns.

Future of Machine Learning and Deep Learning in Financial Services

The financial services industry will continue to use machine learning and deep learning. However, privacy concerns are important to a lot of people. So the financial services industry will have to address those issues to gain wider acceptance of their AI-based services.

References:


要查看或添加评论，请登录

Robert M.的更多文章

社区洞察

其他会员也浏览了