Artificial Intelligence & Machine Learning in Finance: the Whys, the Hows and the Use Cases

Artificial intelligence or AI technologies are integrating and stepping into the world of financial services because of their exquisite performance of specific tasks compared to human beings, especially when raw unstructured data is involved. The financial services industry is cautiously employing AI and its segments, from statistical methods to computational intelligence.  Machine learning (ML), a subset of AI is the most widely attributed to the financial services needs as it digests data and automates the learning applied to specific financial tasks. Financial analytics, market valuation with investment strategies prediction, fraud detection, cyber risks for corporate finance, robo-advisors for personalized wealth management, targeted on-demand insurance quotations, credit scoring, consumer behavior forecasts, task automation, and corporate performance management are many things that machine learning (ML) and artificial intelligence (AI) can potentially contribute to through improved productivity, reduced costs, and enhanced customer experiences that all deliver exquisitely tailored services and help to make informed marketing decisions. Let us show you how.

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Machine learning in finance: Numbers are all about cold facts

While some argue that artificial intelligence and machine learning might be in their infancy, the numbers tell a different story. The Economist Intelligence Unit’s survey of more than 400 businesses in key markets worldwide indicates that 27% of the responding companies have adopted artificial intelligence, and 46% have at least one AI pilot project underway. Deloitte notes that, among the respondents to its AI survey, as many as 70% of those that offer financial services are using ML for cash flow events prediction and fraud detection.

Indeed, Fintechs are an excellent example of the successful implementation of AI and ML to achieve process automation, reduce operational costs, and improve decision-making. In finance, machine learning sets out to transform the way financial institutions deliver services and how their clients receive them, helping both parties manage financial operations and processes.

AI and ML for banking have also influenced the experiences of individual customers around the world. The number of physical visits to bank offices dropped dramatically in 2020, with 89% of customers preferring to use banking apps according to Business Insider Intelligence’s Mobile Banking Competitive Edge Study. This year’s self-isolation mode can partially explain the trend, but it also has resulted because of the technology adopted by banks, allowing for a smooth and intuitive transition to digital management of personal accounts.

The proliferation of AI and ML is not likely to stop. The Mordor Intelligence report notes that in 2019, artificial intelligence in the finance market was evaluated at $6.67 billion; by 2025, it is estimated to grow to $22.6 billion. Business Insider further reports that potential savings for banks from artificial intelligence applications will reach $447 billion in just two years. Viewing these predictions in light of the stress that COVID-19 has placed on the finance industry, AI adoption is the move that can single-handedly decide the survival of businesses in the years to come.

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How does machine learning in finance work?

To understand how machine learning can be used in finance, let’s first unpuzzle the basic concept of ML. Unlike programming, machine learning is not built on a set of rigid rules that dictate how a machine should behave. Instead, the strength of machine learning algorithms is the ability to learn from the data that is input into the algorithm. Naturally, the process is not that simple since it requires not just any data, but the data: relevant, high-quality and properly labeled. In a nutshell, the machine learning algorithm analyzes data and learns to make increasingly complex predictions.

The advantages of machine learning are ideal for finance, as the industry is built on big data. With a proper machine learning algorithm and a dataset to match, a financial enterprise can tap into a deep pool of opportunities presented by AI and ML for the financial industry:

• Automation. Paper workflows stopped being effective long ago; now, smart ML-based models that allow instant sharing and editing, as well as storage and management of information, can dramatically reduce the time and cost of dealing with documents.
• Productivity. AI excels at tiresome and repetitive work taking reportedly up to 60% of employees’ time. When machine learning algorithms take over the mundane work, employees can concentrate on higher-value tasks and core business goals instead.
• Operational costs. Reducing the cost of human errors by outsourcing certain tasks to machines is another machine learning benefit.
• Security. With appropriate adherence to protection protocols, artificial intelligence allows for enhanced security and improved compliance.
• Customer experiences. Losing a single customer might not seem like a big deal. However, if it happens regularly, due to poor communication, long wait times or inefficient problem resolution, it might jeopardize a large portion of your customer base. AI will reduce the time spent searching for information and resolving customer issues from several days to several minutes. Frictionless, 24/7 customer support shows that a business cares for its customers. AI-based virtual assistants are one example of showing you care.
• Personalization. With the help of machine learning algorithms, AI can evaluate and analyze large volumes of data and, therefore, cater to the specific interests and needs of the customers. When your customer buys a house, they will need insurance. When a customer opens a business, you can offer them a new bank account. Timely knowledge of needs allows you to offer individualized products and solutions. Additionally, assessing the financial health of accounts and providing personalized insights for investment goes the extra mile for your customers and your business.

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Challenges faced by finance companies while implementing AI solutions

Sadly, the benefits of machine learning for finance come with a set of challenges for every business, big or small. Here’s a short list of things to look out for:

• Cost. Implementation of artificial intelligence in finance does not come cheap. From the cost of new software to the expenses of the R&D team or data science experts, businesses that start AI projects should be prepared to pay for the associated benefits.
• Financial risks. Even if the business has the necessary money to invest, there is always the risk of a low ROI.
• Lack of resources. While financial cost might be a significant impediment to AI adoption, the lack of necessary human and tech resources is an issue all on its own. It is not enough to have the money; it’s also important to access top talent and use effective tools.
• Skillset challenges. To maximize the benefit of the new AI solution, it is necessary to train the employees and help them learn new skills. Data analysis with the help of AI is not an intuitive task, so it can take time to transition to the new processes of working with the ML algorithms requirements.
• Data protection. Information is sensitive and this is especially true in the finance industry. In order to handle data consisting of clients’ account numbers and personal information (names, addresses, SSNs and so on), it is crucial to invest in protection protocols and comply with industry standards such as ISO and GDPR. And since AI needs a lot of information to learn and train, building secure while efficient datasets is a task that only experienced ML specialists can fulfill. 

Why Use Machine Learning in Finance? 

If there are so many challenges for machine learning applications in finance, why do it at all? Why spend the valuable resources and time dealing with something so inherently problematic? The answer is simple: because the benefits are far greater than the potential risks. Let’s dive into some practical use cases to see why machine learning in finance is a great match.

Machine learning use cases in finance

As we’ve already mentioned, AI efficiently deals with great amounts of raw data and the finance industry can provide the needed training materials for machine learning. Here’s how institutions can leverage artificial intelligence and improve processes in different financial fields. 

Automated trading

This is not a new use case for AI, but it’s more relevant than ever before due to the improved accuracy and increased trading speed, which is especially valuable for large financial institutions and hedge funds. AI enables extremely accurate trading decisions based on big data. 

Numerous global researches predict that new developments in deep learning and neural networks will further strengthen the motivation to fund machine learning projects. High-Frequency Trading (HFT) is an example of a task people can’t perform without computers. Machines possess the ability to place bids in a fraction of a second, which is important because of lightning-fast market changes.

Fraud detection

One of the most widespread use cases for AI and machine learning applications in finance is fraud detection. AI models based on big data allow detecting and neutralizing fraudulent activities by analyzing the clients’ behaviors and online transaction histories. We’ve actually built a fraudsters identification system ourselves for one of our clients, Trōv.  As a result, the fraudulent activity and loss ratios were reduced profoundly, as well as the time needed for processing claims, which enhanced the overall customer experience.

Marketing research

Relevant research data is essential for improving customer engagement and sales revenues. AI can make accurate predictions based on customers’ personal history of browsing and purchasing behaviors. Based on the data collected, the “perfect customer” profile can be kept up-to-date to help guide the long-term financial business’ objectives.

Investment management

As we face unprecedented technological growth partially caused by the health, political and social crises, people begin to think more about investing in their future. Known as “robo-advisors,” these digital algorithm-driven platforms predict the best alternatives for investment portfolios based on the goals established by the customer. A comparatively new use case for AI, robo-advisors allow both customers and financial enterprises to save money and improve security through the smarter allocation of resources.

Personalization and customer service

Many modern financial businesses rely heavily on their relationships with clients. Artificial intelligence (AI) solutions can enhance customer experiences in the finance industry via chatbots, search engines, mobile banking and financial health analytics.

Chatbots are arguably the most engaging way to improve customer service, especially since conversational assistants can now pass the Turing test. At Avenga, we have experience developing integrated, competent, human-like and secure AI-powered assistants to advance workplace productivity and enhance customer engagement.

Some of the reportedly largest players in the field of mobile-only banking, such as Fidor Bank, Number26, BankMobile and Hello bank! also make use of these current trends. They allow their clients to operate completely through the app, granting them full control over their transactions and payments. But most importantly, they pay attention to the target demographics (usually younger audiences). Mobile banks make two-way communication more transparent, which is reflected in the addition of requested features and services to the apps by customers. 

Financial health is a more subtle example of personalization. Defined as the scope of financial resources and habits, evaluating the financial health of a person can help them achieve goals (e.g., savings for retirement), work toward detecting past errors and preventing future transgressions (e.g., checking the provided numbers and avoiding credit debt).

Currently, the services offered by the banks and credit unions are based on several sparse metrics that measure financial health. However, organizations like CFSI (Center for Financial Services Innovation) work on providing a better understanding of financial health to both the institutions and the public.

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Future prospects of machine learning in finance

AI can complement the finance industry with better customer experiences, optimized processes and higher work efficiency. At the same time, finances are a great learning environment for artificial intelligence since they provide extensive datasets for the machine learning algorithms to process. 

In the future, the trends in this area will likely continue to develop at the same pace, if not faster. New ways of implementing financial machine learning models will arise, and new innovative use cases will appear. There are quite a few fascinating forecasts for the future of AI in finance:

• At the moment, investments and stock prices are the primary sources of analysis for financial institutions and hedge funds. This will gradually change with sentiment and intent analysis, which will bring a clearer understanding of the bigger picture and how political news, social media and other sources impact the financial market.
• According to Deloitte’s research, the distributed ledger technology (aka blockchain) will reshape the current infrastructure and processes within the finance industry. The major points of attention will be security, transparency and efficiency of the transactions, which will positively influence the trust between all parties.
• McKinsey declares that the banking industry will expand its reliance on machine learning algorithms. Utilizing the possibilities of AI, the bank of the future will be optimized primarily for operational efficiency. As machine learning algorithms become more advanced, we will probably see complex diagnostic engines working for the customer’s benefit.

Keeping up with the current trends, increasing user acceptance will continue, as will the high demand for a more personalized and humanized approach from financial institutions and businesses. This will most probably result in the transformation of the regulatory frameworks, which will expand the application of artificial intelligence and machine learning in finance.

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Conclusion. Can it all be more optimistic?

With smart technology at the forefront, every financial company is bound to become a tech expert in order to stay relevant with every customer segment, from the “silver tech generation” through millennials and up to GenZs.

Most leading financial firms and institutions are using artificial intelligence to some extent. Whether it’s algorithmic trading, fraud detection, marketing research or customer service, machine learning algorithms are becoming a part of the operational cycle of a financial business. 

And it’s not surprising, given the number of benefits AI brings. It enhances security and improves compliance, helps with workflow automation, increasing productivity and allows for the cutting down of both time and costs. Moreover, AI helps to reach out to customers by offering them the services they actually want and personalizing their experiences.

→ Have a look at  Meeting the Future. Trends & Technology 2021.

Of course, there are quite a few challenges that AI brings along. The cost of AI and the lack of essential resources (human, tech, infrastructure, or all at once) may play a significant part in financial institutions postponing the implementation of artificial intelligence and adopting the “wait-and-see” attitude. Still, the future seems bright for the adopters of AI as there are many attractive prospects to explore. From intent analysis to blockchain technology, from transparency to efficiency, AI in finance will be carried forward by the formulation of the new regulatory policies and the innovations that will make the industry more tech-oriented and client-focused.

As much as the turbulence of the global economies, climate change, social precariousness and other factors have taught us, we have learned that only the ones who are prepared to recognize and quickly adjust to sudden market changes and imbalances will be able to appropriately address possible risks, survive the changes and lead the way towards digital transformation. Understanding the dynamic consumer landscape and outlining detail-rich customer insights can help embed the fundamental value proposition in every feature of financial offerings. With Avenga’s expertise in fintech and a portfolio of successful projects, we can help you open the gateway to the AI-powered future.