There is no doubt that the financial industry is undergoing a revolution. It will have devastating effects for the entire industries yet provide opportunities for those who manage to adapt and not evolve.
Those adapting will:
Those not adapting will die!
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning create a paradigm shift in virtually every sector of the tech industry.
Machine Learning (ML) works by extracting meaningful insights from raw sets of data and provides accurate results. This information is then used to solve complex and data-rich problems critical to the banking & finance sector. Further, machine learning algorithms are equipped to learn from data, processes, and techniques to find different insights.
Until recently, only the hedge funds used AI and ML, but in the last few years, smaller companies have adopted these techniques. Robo-advisors offer fast underwriting, portfolio composition, strategy development, modeling, robo-fining, market impact and alternative credit report measurement, and portfolio reporting use cases.
To strive to meet the demands of their customers and outperform the competition, financial institutions rely on state-of-of-the-the-the-art technologies to meet those requirements.
This is now mandatory for large corporations and for smaller ones who benefit from AI and ML.
Maruti Techlabs has classified 12 use cases for AI and ML in finance. Here I illustrate them shortly.
The use of machine learning algorithms will significantly improve network security. Data scientists are constantly developing training programs to detect red flags such as money laundering tactics, which can be avoided by financial tracking. Machine learning technologies will be used to control the most sophisticated cybersecurity networks in the future.
It’s machine learning-enabled investment models that allow the experienced fund managers to see specific market changes ahead of time.
The disruption in the investment banking industry is well documented, as noted firms such as Morgan Stanley and Bank of America are developing automated investment advisors.
These machine learning-powered solutions enable businesses to entirely do away with manual work by allowing for increased productivity. Chatbots, paperwork, and employee training are several of the machine learning processes in finance. It will enable finance companies to cut costs while also increasing the number of customers.
Additionally, Machine Learning can find patterns, interpret behaviors, and recognize them, too. This can be used to create robust customer support systems that can handle any potential customer problem.
For this example, Wells Fargo uses a Facebook chatbot through Messenger. You get all the important account and password information by using the chatbot.
Anomalies in human behavior can be easily detected by using machine learning algorithms. More importantly, ML also reduces the number of false negatives and speeds up real-time processing. These models are based on the client’s online behavior and transaction history.
Another advantage of machine learning-powered fraud detection is that it can identify and stop fraudulent activity before it occurs, instead of waiting to catch it after the fact.
Using machine learning techniques, banks and financial institutions can significantly lower the risk levels by analyzing a massive volume of data sources. Unlike the traditional methods, which are usually limited to essential information such as credit score, ML can analyze significant volumes of personal information to reduce their risk.
Various insights gathered by machine learning technology also provide banking and financial services organizations with actionable intelligence to help them make subsequent decisions. An example of this could be machine learning programs tapping into different data sources to apply for loans and assign risk scores to them. ML algorithms could then easily predict the customers who are at risk for defaulting on their loans to help companies rethink or adjust terms for each customer.
Machine Learning in trading is another excellent example of a practical use case in the finance industry. Algorithmic Trading (AT) has become a dominant force in global financial markets.
ML-based solutions and models allow trading companies to make better trading decisions by closely monitoring the trade results and news in real-time to detect patterns that can enable stock prices to go up or down.
Machine learning algorithms can also simultaneously analyze hundreds of data sources, giving the traders a distinct advantage over the market average. Some of the other benefits of Algorithm Trading include –
There are various machine learning-powered budgeting apps that can offer customers highly specialized and targeted financial advice. Machine Learning algorithms allow customers to track their spending daily as well as pinpoint where they can save money.
One of the other rapidly developing concepts is Robo-Advisors. Individual and small to medium-sized business investors can do just as well with an advisory. Robo-advisors can utilize machine learning to create financial portfolios and strategies like trading, retirement plans, etc.
When it comes to banks and financial institutions, efficient data management is critical to growth and business operation. The large volume and wide range of financial details presentment diversity make it nearly impossible to comprehend even trained personnel.
Using machine learning to analyze large volumes of data brings both process efficiencies and intelligence. NLP (technologies such as data analytics, data mining, and natural language processing) derive valuable business intelligence.
Another example of this might be machine learning algorithms being applied to analyze how market and financial trends affect financial customers.
Banking and financial institutions can use Machine Learning algorithms to analyze both structured and unstructured data. E.g., customer requests, social media interactions, and various business processes internal to the company, and discover trends (both beneficial and potentially dangerous) to assess risk and help customers make informed decisions accurately.
Customers will get all their questions about their monthly expenditures, loan eligibility, affordable insurance plans, and more answered using an insightful chatbot.
Furthermore, many machine learning-based applications can analyze accounts and enable customers to save and develop their money when linked to a payment system. Advanced machine learning algorithms can be used to evaluate user behavior and create personalized offers. A customer looking to invest in a financial plan, for example, will receive a customized investment offer after the ML algorithm analyzes his or her current financial situation.
Credit card companies can use ML technology to predict at-risk customers and specifically retain selected ones out of these. Based on user demographic data and transaction activity, they can easily predict user behavior and design offers specifically for them.
The application here includes a predictive, binary classification model to find out the customers at risk, followed by utilizing a recommender model to determine best-suited card offers that can help to retain these customers.
The ability of AI and Machine Learning models to make accurate predictions based on past behavior makes them a great marketing tool. From analyzing the mobile app usage, web activity, and responses to previous ad campaigns, machine learning algorithms can help create a robust marketing strategy for finance companies.
Machine Learning is now used in various areas of the financial environment, including asset management, risk assessment, investment advice, financial fraud detection, document authentication, and much more.
Although ML algorithms deal with a wide range of tasks, they continuously learn from massive amounts of data and close the gap by getting the world closer to a fully automated financial system.
Viola Meacci, student of Biomedical Engineering at the University of Pisa, has always been interested in the world of journalism. In her job, she explored the world of blockchain, cryptocurrencies, NFT and metaverse which she now very is passionate about it .