Machine Learning Use Cases in Financial Industry

Financial use cases and opportunities

Source: Image by Roberto Júnior on Unsplash

In the last decade, the world has seen tremendous growth in Bigdata Analytics and Artificial Intelligence. Many industries are transformed and AI has become a growth paradigm. The financial industry is no exception. Big financial firms and Hedge funds were the early adaptors of Artificial Intelligence & Machine Learning and now all major banks, trading companies, investment & insurance companies, fintech, and regulatory companies everyone is using the power of AI/ML to transform the way they work.

Some people say that AI is overrated and hyped and it will fade soon. But we can already see AI/ML transforming the industry from managing money to risk management to auto-approval of credit cards & loans to chatbots. And the improvement of decisions over time is itself giving AI an edge. These transformations are not only giving banks and financial institutes a technical savvy approach to address the regular (repetitive) tasks, it will save banks huge money in the long run. In this post, we will see real-time use cases where AI/ML is playing a crucial role in the finance industry.

Machine Learning Use Cases

1. Algorithmic Trading — This was probably the first use case that big financial institutes and hedge funds have introduced using AI/ML techniques. But nowadays algorithmic trading has evolved so much that systems make extremely fast trades multiple times in a second on a very large volume of data. Algorithmic trading has an advantage over human traders, as it can analyze a large volume of complex data and make faster decisions based on current and historic patterns without emotional bias and personal opinions.
2. Fraud Detection — We are living in a digital age, especially while I am writing this post we are in a time of the COVID pandemic and almost every business is running digitally. Digitalization has its own cons and one of them is online fraudulent activities. AI/ML plays a very smart role here to determine suspicious activities. With the advancement in security footprint, we are anyways ahead to improve security authentication with 2-step verifications and facial recognition but machine learning is one more step in preventing fraudulent transactions and online theft. Machine learning can detect anomalies in transactions, raise security threats, and flag for further investigations. Over time ML systems can learn and can automatically approve, decline, or raise human intervention for online transactions in real time. Nowadays many banks are using ML to detect fraud activities using this approach.
3. Churn Prediction — We are living in an era where every business has to face competition in the open market. This competition gives customers the power to choose the best products but it creates a burden on businesses to retain their existing customers. Studies show getting a new customer is 5–6% more expensive than retaining existing customers. Customer retention is also crucial for sustainable long-term business planning, expansion, and building a reputation in the market; hence machine learning comes to the rescue. Machine learning can be used to forecast the churn (attrition) rate of existing customers. It allows financial institutes to re-evaluate their customer services or revise interest rates or provide promotional offers.
4. Robo Advisors — Robo Advisors or automated personal financial assistance are the fastest-growing field in the financial industry. In the last decade, there were many newcomers in finance with this facility. Also, many big financial institutes are investing in AI/ML to shift from personal financial advisors to Robo advisors. Robo advisors take personalized financial decisions for customers based on their long/short-term goals and risk tolerance. For example, Robo advisors will predict high-risk high reward kinds of investments for younger clients whereas clients approaching retirement age will be given safer investments. Robo advisors are especially useful for customers who are new to investing and want comfortable options.
5. Loan underwriting — Many financial institutes are using AI/ML techniques to take quick decisions for loan underwriting. Machine learning models analyze large datasets using age, occupation, family, income, assets, previous loan history, and credit history for existing & defaulter customers and make decisions if a customer is qualified for the loan or not. Using machine learning for loan underwriting is not only saving time but also human resources.
6. Credit Assessment — AI/ML is helping financial institutes in taking credit decisions for their customers. Based on customer income and payment history, the machine learning model identifies the customers that are good candidates for credit and also flags customers that can turn into defaulters. The machine learning model also predicts a safer credit limit for customers. Nowadays many banks increase or decrease the credit limit of customers in real-time using machine learning based on their expenditure nature and repayment history.
7. Customer Service (Chatbots) — The first step in retaining existing customers is to provide excellent customer service and AI/ML can be used to digitize customer service with the introduction of chatbots. Chatbots are programs that are trained with NLP using deep learning techniques. Many financial institutes are already using chatbots in their customer service and few are extending chatbots to provide voice support along with text using Conversational AI. Conversational AI provides more engaging and human-like interaction for customers. Over time chatbots can be retrained and improved to provide a more personalized experience. Conversational AI is the future of customer support operations in the banking industry and in the next few years, we’ll see more personalized conversational banking.
8. Personalization — AI & ML are playing an important role in providing personal financial management. Based on customer age, demographics, income, assets, and spending patterns; customers are clustered with their areas of interest and relevant news & content will be shown on their banking app’s home page. Using machine learning, financial institutes make decisions to approach customers on their past behaviors, providing them with relevant information about their future goals & investments and improving their financial decision-making.
9. Risk Management — Risk Management is a major aspect to track the stability of a financial institute. Credit risk is a complex field of study and machine learning has provided great help to measure the uncertainty. Using complex data and patterns machine learning is providing real-time predictions to find the likelihood of loan defaults and risky investments and helping banks in taking better decisions.
10. Financial Product Recommendations — Most banks already have a rule-based recommendation system for the sales of their financial products. AI/ML is transforming this area as well where based on customer finances, past activities, and demographics; machine learning models provide more personalized financial product recommendations in real-time. These recommendations also improve with time when more clients buy products based on these recommendations.

Challenges

As we have seen above there are many areas where financial institutes are investing in AI/ML and we are already seeing the results in our day-to-day banking whether it's talking with automated chatbots or getting personalized products & offers or Robo advisors. But still, there are challenges that every financial institute faces while adopting machine learning-based solutions.

Source: Image by the author

1. Data availability — Most often companies move into ML transformations but leave without going live, and the reason is they are not data-ready. The realization comes in form of Data Engineering. There is a saying in the ML world- “Garbage In, Garbage Out”. Most companies focus only on getting value from data, but the data is only valuable if it is in the correct shape. So the aim should be to aggregate the data first using Data Engineering and then focus on depicting any value from data using machine learning.
2. Complexity — Starting machine learning from scratch is a complex task. Only ML Engineers/Data Scientists are not sufficient for any project. A Team should involve Data Scientists, ML Engineers, Data Engineers, Software Developers, DevOps Engineers, Technical Architects, and Domain Experts. Hiring and retaining the team of these people is complex in terms of both time and cost.
3. Cost — Machine learning involves investment for R&D. Infrastructure is also a concern for companies who have not moved to Cloud. Training, maintenance, and deployment of ML systems require both scalability and faster execution.
4. Late Comers — Many times companies don’t hit the market and invest so much time in getting the initial results or spend time in improving the results, and in the end, they lose the ground. An early entry into the market and improvement with time is always beneficial.

Another challenge is setting high expectations from the beginning. No companies can build Algo Trading in six months or Robo Advisors in a year. A few other challenges are issues in scaling up the solutions, less automation, not working on deployment strategy, and the learning curve for new technologies.

Conclusion

Despite all the challenges, machine learning is promising in transforming the way banks work. And most companies are investing in AI/ML for good. The next decade will completely change the banking & investment industry. We will see more chatbots and Robo Advisors instead of humans, more personalization, and improved security.

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