You see the words “AI” and “ML” all the time now whenever you scroll through your LinkedIn news feed, and they seem to be trending search terms across search engines and popular courses for up-skilling. But are they just buzz words or fads? Or do they play a greater role in various sectors, especially the Finance industry?
Disruptive technologies such as artificial intelligence (AI) and machine learning (ML) have been making waves across industries in the past years, and Finance is one of the industries that can achieve a lot of operational gains from embracing and adopting these technologies. By definition, machine learning is a branch of artificial intelligence that uses statistical models to make predictions. Machine learning has the ability to analyze millions of data sets within a short time to improve the outcomes without being explicitly programmed.
The Finance industry has seen a steep rise in the number of use cases of AI and ML applications to create better outcomes for both consumers and businesses. From lead generation, customer onboarding, and optimizing portfolio composition and management, to speeding up the underwriting process, performing model validation, detecting fraud, and assist the credit operations team with reporting, operational management within Finance has a lot to gain from AI and ML. Want to learn more about the different use cases? Read on!
Use Cases of AI and ML in Finance Operational Management
Customer onboarding is usually the first operational process that new clients go through when they first sign up at a bank or financial institution. The onboarding experience is important because it gives new clients a first initial impression of the relationship they will have with the organization. Onboarding increasingly takes place digitally, so it is important to have the means to measure and aggregate data points collected before, during, and after customer onboarding. For example, collecting data on how many times a client views a particular onboarding video or view a particular webpage can be used to improve the onboarding process. Imagine all the rich, useful insights that could be gained from having that data at your fingertips!
AI and ML can be used to study patterns of user activities on the company website and across social media platforms, telephone conversation analytics, and in-person branch visit metrics. These data can then be aggregated to create models to predict new client behaviours, such as the finding that new clients prefer to complete onboarding online or using mobile phone as opposed to in person, so website and mobile tools should be enhanced to ensure a smooth onboarding process.
Fraud Monitoring, Detection, and Prevention
Do you ever wonder how banks determine which activities appear to be fraudulent, as you receive those “potential fraud” alerts on your mobile phone from time to time? Financial institutions routinely collect data on customer transactions. Machine learning algorithms can be used to monitor, detect, and prevent fraud. It does this by examining historical payment data and every action performed by each cardholder. Any deviation or sudden anomaly would be marked as suspicious, and an automated hold on a credit card transaction or large amount of withdrawal could be made without any manual effort. These machine learning models have been used by most large financial institutions around the world, and they have been proven to be able to detect suspicious behavior with high precision. So rest assured, your bank likely has the tools and technology in place to keep you safe from possible fraudsters and hackers (although you still need to practice good habits to protect your data to avoid having your financial and personal information compromised).
Portfolio Optimization and Management
Portfolio management is another big use of AI and ML in finance. In the past, managing clients’ portfolios required a great deal of manual effort and operational manpower. With AI and ML, portfolio managers can use statistical points and automated algorithms to optimize the performance of clients’ assets. How this works has also been simplified. Customers fill in their financial goals, such as to save for retirement. The robot advisor (using AI and ML) then calculates the number of years to retirement and income goal, takes into account current income and future cash flows, and automatically assigns current assets to investment variants and opportunities such as mutual funds, stocks, bonds, and savings accounts.
Portfolio management involves creating and overseeing selected investments that align with the investor’s long-term financial goals and risk tolerance. This can save you time from needing to set appointments and meet with a real-life financial advisor, as the portfolio balancing and recommendations can be done automatically and digitally.
Process Automation and Customer Interactions
Solutions powered by AI and ML can replace manual work by automating repetitive tasks in operations management. These include chat bots that interact with both external customers and internal employees to help with everyday tasks and questions, paperwork automation, and employee training gamification. Using AI and ML enables finance companies to improve their customer experience, reduce costs, and scale up their services. This is because AI and ML technologies can easily access data, interpret behaviors, follow and recognize the patterns. This could be readily used for customer support systems that can work similar to a real human and solve all of the customers’ unique queries.
An example of this is the use of ML-driven chat bot through the Facebook Messenger to communicate with its users effectively, including resetting of password and retrieval of account information from the system. The next time you interact with a company online through their website or chat window, decide for yourself if the agent you’re chatting with is a real-life human or a bot!
Risk Management and Credit Decisioning
Machine learning can help banks and financial institutions to lower risk levels by analyzing massive volumes of data sources. In contrast, traditional methods of collecting and analyzing data might be limited to only essential information such as credit scores. The various insights gathered by machine learning technology can provide banking and financial services institutions with actionable intelligence to help them make subsequent decisions. For example, a machine learning program can tap into different data sources for customers applying for loans and assign risk scores to them. ML algorithms can then easily predict the customers who are at risk for defaulting on their loans to help companies rethink or adjust terms for each customer.
Customer Retention Program and Marketing
Another operational management area that can gain from AI and ML is customer retention. Credit card companies can use ML technology to predict low-risk customers and specifically retain selected ones out of these. Based on user demographic data and transaction activity, they can easily predict user behaviour and design offers specifically for these customers.
Re-marketing is when a customer leaves the bank, and efforts are made to get them to return as a customer. Targeting which customers would be good for re-marketing efforts can be done using AI and ML, such as predicting which customers would bring the most value or use the most products and services.
Marketing can use the data analyzed by AI and data models created using ML to make accurate predictions based on past customer behaviours. From analyzing mobile app usage, web activity, and responses to previous ad campaigns, machine learning algorithms can help to create a robust marketing strategy for Finance companies.
AI and ML technologies have a lot of uses and potential in improving the speed, efficiency, and accuracy of most aspects of operations management in Finance. Using AI for customer onboarding can save a lot of time and manual work, and it can also improve customer satisfaction.
The time gained and costs saved from automating time-consuming and manual process can be substantial. In addition, using AI and ML have the benefits of offering a more streamlined and personalized customer experience, while reducing risks. AI and ML have been widely used in monitoring customers’ activities for potential fraud incidents, and these technologies have been able to accurately detect and prevent fraud. How many times have you received the automated text message alerting you that a certain credit card transaction has been made, and prompted you to review its legitimacy (by pressing “Y” or “N” on your mobile phone)? This is just one of the many examples of AI and ML’s benefits in financial operational management.
From portfolio optimization and management, to risk management, credit deaccessioning, and customer retention operations, AI and ML underpin a lot of use cases in operational management in the Finance industry. The next time you see these buzz words on your LinkedIn feed or news article, know that they carry a lot more weight!
Corporate Finance Institute – https://corporatefinanceinstitute.com/resources/data-science/machine-learning-in-finance
Maruthi Tech Labs – https://marutitech.com/ai-and-ml-in-finance
RecoSense Labs – https://recosenselabs.com/blog/the-role-of-ai-and-ml-in-banking