AI and machine learning have revolutionized the working of almost every industrial sector today. Most recently, the application of machine learning in finance has become an integral part of its ecosystem. The financial industry is perhaps one of the most suitable fields where machine-learning use cases are plenty.
This is due to the quantitative nature of this industry. AI technologies are making their presence felt more and more in the finance industry, precisely due to highly accessible computing power and easy availability of ML tools.
Why is Machine learning in Finance necessary?
Machine learning models learn by themselves without human intervention. As the number of data increases, so does the accuracy of the models. Hence, machine learning in finance is all about working with huge volumes of data and executing tasks like fraud detection, model validation, market impact analysis, among many.
Machine learning algorithms are implemented to automate and speed-up daily time-consuming processes in order to streamline and provide enhanced customer experience. So, ML applications have become an integral part of banks, fintech firms, insurance companies, etc.
The benefits of the application of machine learning in finance are many. Chiefly, they include:
· Minimised operational costs due to the automation of time-consuming processes.
· Imparts strategic efficiency to the organizations
· Enhancement in the profit margins arising because of an increase in productiveness and better consumer experiences.
· Adherence to compliance regulations and better security of transactions.
Machine-learning use cases in the finance industry
Machine learning algorithms facilitate organizations to make better trading decisions. It is done by the continuous, real-time monitoring of the trade results and the subsequent detection of patterns that governs the movement of stock prices upwards or downwards. Its predictions enable it to make decisions regarding selling, holding, or purchasing stock.
Human traders can never analyze the countless data sources as effectively as ML algorithms do. It provides traders a competitive edge as they automate processes related to trading. Moreover, using ML models, traders can operate in multiple markets and increase their opportunities.
Automation of processes
Chatbots, call-center automation, customer onboarding, account opening, and closing, and loan processing automation are just some of the few Machine-learning use cases in the financial sector. By automating these manual, cumbersome and lengthy processes, ML solutions allow financial institutions to enhance efficiency, minimize personnel costs, mitigate risks, and expand their capacity.
For example, noted banking companies like BNY Mello and Wells Fargo, by incorporating process automation through ML, were able to increase their savings and provide better customer engagement significantly.
Financial monitoring for better security
A United Nations report estimates that around $800 billion – $2 trillion money is laundered each year globally. With the growing number of security threats in finance, it has become imperative that this sector incorporates ML technologies to combat transactional security threats.
ML systems’ ability to scan massive datasets, detect anomalies, and raise red flags makes them an ideal solution to the problem of fraudulent financial transactions. This financial monitoring is of the machine-learning use cases for maintaining better security.
Better investments decisions
One of the major applications of Machine learning in finance relates to the investment landscape. As datasets get more complicated, ML-based sophisticated data analysis techniques are being used to analyze data and develop suitable strategies.
By effectively predicting future market performance, these technologies will surely give investment managers an edge over their competitors.
Enhanced customer service through sound financial advice
Apps powered by ML help customers to monitor and analyze their expenditures, thereby enabling them to increase savings. More recently, ML-based, Robo-advisors are being utilized for processes like portfolio management and financial product suggestions.
Portfolio management involves algorithms that enable customers to manage their wealth effectively. At the same time, more and more people are relying on robo-advisors to provide them an insurance plan tailored according to their needs. Customer service thus gets a boost through such ML-based applications.
Extracting insights from customer data
Today, data from countless sources inundates financial institutions. Although this data is crucial for an organization’s progress, its vast quantities make its processing nearly impossible manually.
The application of Machine learning in finance through its tools like data analytics, natural language processing, and data mining enables analysts to obtain data-driven insights quickly from such vast stores of information. These insights help drive the organization’s profits and opportunities.
The future scope of Machine learning in finance
ML technology has been growing at a rapid pace. Apart from its prevailing use cases, Machine learning in finance has much to offer in the near future. Some of these innovations can be seen in:
1. Conversational platforms
After AI-powered chatbots, smooth conversational interfaces in financial services will combine natural language processing and speech recognition. Its example can already be seen in Google Duplex that enables a human-computer interaction that mimics real-world conversations. The future holds prospects of the development of many such innovative ML-enabled conversational interfaces.
2. Analysing customer sentiments
Stock prices are influenced by various factors like news, rumors, even weather forecast, to some extent. ML is already in use for facilitating better decisions by analyzing enormous data sources. In the future, these algorithms are set to examine multiple disparate data sources by mimicking human ‘intuition.’ The primary purpose is to monitor the movement of stock prices accurately by detecting patterns in it.
Machine learning in finance will undoubtedly overhaul customer security for the better. Voice and facial recognition and other similar technologies will enable financial institutions to do away with user security norms like password, username, etc
Machine learning in finance has a promising future. However, some challenges persist in their successful adoption. Factors like lack of business KPIs and ambiguities regarding ML’s working inhibit most businesses from fully harnessing it.
The technical finesse of Machine learning company experts is needed to formulate a realistic plan to implement this technology efficiently. Nonetheless, machine learning is and will remain a crucial part of various applications used in the finance sector.