Across industries, the use of machine learning is advancing rapidly. Financial institutions, including banks, have long relied on computers to run complex calculations, but new systems and algorithms turn these tasks into more automated processes. For example, in the field of risk management, machine learning can help experts identify risks, conserve manpower, and plan better. Fraud detection systems can analyze patterns of buying behaviour, flag suspicious activity, and predict the future direction of a market.
Machine learning can be used in chatbots and mobile banking applications. These innovative applications of machine learning can draw conclusions from historical data about customers, predict future events, and offer recommendations that would otherwise be impossible to predict. ML can also help traditional banks and other financial institutions develop more personalized services. Some of these applications are already in the works today, while others are still in the nascent stage.
Following are the main performance boost applications in the finance industry:
Reinforcement learning (RL): As bots powered by RL interact with the trading and stock market environment, they can 'learn' about the stock market and the trading environment. They refine their learning strategy through trial and error and accounting for characteristics of the individual stocks listed on the market. Thus, RL optimizes investment return, avoids risk, and counters the bias of professional traders by automating stock trading strategies.
Natural language processing (NLP): NLP powered bots can teach machines how to read, comprehend and analyze the complexities of human language. For instance, financial documents like the 10-k form to predict stock movements. Also, it helps with bias elimination.
Moreover, sentiment analysis, a sub-field of natural language processing, helps investors quickly know if the tone of a given report is positive or negative.
Following are the salient applications of machine learning in the finance industry:
1. Analyze unstructured data
A large portion of data in finance is written, so machine learning algorithms can be used in the finance industry to analyze unstructured data. For example, the technology can analyze customer requests, online social interactions, and internal business processes. By analyzing these data sets, these algorithms can predict the future of a particular company. This can help businesses determine risk and make decisions accordingly. Additionally, artificially intelligent chatbots can be implemented to answer customer queries and provide real-time information.
For instance, ML algorithms can be used in banks to identify people in photographs and videos. Although this type of AI is controversial, it has numerous uses in the finance industry. One application is in insurance, where it can identify fraudulent transactions and log-in attempts. By analyzing a huge amount of data, big banks can train an algorithm to predict risk and identify fraudulent activity. Furthermore, machine learning algorithms can help prevent the laundering of money and other illicit activities.
2. Address fraud and security issues
The finance industry is ripe for machine learning. The technology can detect fraud, which is a thorny problem for every service provider. And while the future of financial services may be bright, many companies need to address fraud and security issues. The biggest issue is how much it costs to implement the software and how it will work with existing software.
3. Affect the efficiency of the entire industry
Machines may eventually replace humans in the finance industry. The technology will continue to improve our ability to analyze data and predict outcomes. For instance, investment banking operations are already relying on machine learning algorithms to fine-tune algorithms and reduce risk. Retailers are already using predictive analytics to understand consumer preferences better and anticipate how consumers will buy products and services. While this isn't a complete replacement, it will significantly affect the efficiency of the entire industry.
4. Improve the bottom line
While the financial industry is still focusing on building its bottom line, AI is making a huge impact on the top line. It is already impacting banking and other industries. While many are skeptical about the benefits of AI, it is still transforming the industry. And it is already influencing the way we do business. For instance, the use of machine learning in financial services is already proving to be an invaluable addition to the field of finance.
The way forward
There are many industries where machine learning can prove to be beneficial. Among them, AI can improve risk management by using sentiment analysis and analyzing unstructured content. It can also help improve customer experiences. Despite the high costs of fraud, it has the potential to improve the quality of financial service. An exemplary implementation of this technology can increase productivity and reduce costs.