Artificial Intelligence and Machine Learning algorithms in finance: the whys, the hows and the use cases

Artificial
Intelligence and
Machine Learning
algorithms in
finance: the whys,
the hows and
the use cases

Machine Learning in Finance

AI and ML have a bright future in many industries, and finance is no exception.

Artificial Intelligence (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. However, the financial services industry cautiously employs AI and its segments, from statistical methods to computational intelligence. Machine learning (ML), a subset of AI, is the most widely attributed to financial services needs as it digests data and automates learning for 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 ML and 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.

ML in finance industry: numbers are all about cold facts

While some argue that AI and other ML solutions might be in their infancy, the numbers tell a different story. For example, the study conducted by McKinsey indicates that 27% of the responding companies have adopted AI, and 46% have at least one AI pilot project underway. In addition, 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 event 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, ML technology 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. According to Insider Intelligence, about 89% of people use mobile banking instead of going to the bank directly. This year’s self-isolation mode can partially explain the trend. Still, 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 likely to continue. According to this report, AI in the finance market is expected to reach $64 billion by 2030. Viewing this prediction in the 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.

How does ML in finance work?

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

The advantages of ML are ideal for finance, as the industry is built on big data. With a proper ML algorithm and a dataset to match the ML jobs, 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, innovative ML-based models that allow instant sharing, editing, storage, and information management can dramatically reduce the time and cost of dealing with documents.
  • Productivity. AI excels at tiresome and repetitive work, reportedly taking up to 60% of employees’ time. When ML algorithms take over mundane work, employees can concentrate on higher-value tasks and core business goals instead.
  • Operational costs. Reducing the cost of human errors by outsourcing specific tasks to machines is another ML benefit.
  • Security. With appropriate adherence to protection protocols, AI allows for enhanced security and improved compliance.
  • Customer experiences. Losing a single customer is not 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 ML 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. You can offer customers a new bank account when they open a business. Timely knowledge of needs allows you to provide individualized products and solutions. Additionally, assessing funds’ financial health and giving personalized insights for investment goes the extra mile for your customers and your business.

The list of advantages, as mentioned above, does not exhaust with the ones we presented. However, aspects like automation, productivity, operational costs, security, customer experiences, and personalization are thes one driving the success of ML in finance. Yet, it is crucial to understand that the technology above is not an all-fits-one solution. There are particular disadvantages to consider as well.

Challenges faced by finance companies while implementing AI solutions

Sadly, the benefits of ML 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 AI in finance is a costly undertaking. 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. More is needed to have the money; accessing the top talent and using practical tools is essential.
  • 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. 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 much information to learn and train, building secure, efficient datasets is a task only experienced ML specialists can fulfill.

In the context adobe, with both advantages and disadvantages in the open, you can know have a more or less objective angle on the ML in finance. Tapping into the pros and battling the cons determines how effective the application of the chosen technology will be.

Why use ML in finance?

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

ML use cases in finance

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

Automated trading

This is a typical use case for AI. Still, it’s more relevant than ever 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 researchers predict that deep learning and neural network developments will further strengthen the motivation to fund ML projects. High-Frequency Trading (HFT) is an example of a task people can’t perform without computers. Machines can place bids in a fraction of a second, which is essential because of lightning-fast stock market changes.

Fraud detection

One of the most widespread use cases for AI and ML applications in finance is fraud detection. AI models based on big data allow the detection and neutralizing of fraudulent money laundering activities by analyzing the clients’ behaviors and online transaction histories. For example, we’ve built a fraudsters identification system 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 financial companies’ customer engagement and sales revenues. For example, AI can make accurate predictions based on customers’ personal history of browsing and purchasing behaviors. As a result, the “perfect customer” profile can be kept up-to-date based on the data collected to help guide long-term financial business objectives.

Investment management

As we face unprecedented technological growth partially caused by health, political and social crises, people begin to make financial services companies 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 customers and financial enterprises to save money and improve security through the more intelligent allocation of resources.

Personalization and customer service

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

Some of the reportedly most prominent players in mobile-only banking, such as Fidor Bank, Number26, BankMobile, and Hello Bank!, also use these current trends. They allow their clients to operate entirely through the app, granting them complete control over their transactions and payments. But most importantly, they pay attention to the target demographics (usually younger audiences). In addition, mobile banks make two-way communication more transparent, reflected in the accumulation 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 a person’s financial health can help them achieve goals (e.g., savings for retirement), work toward detecting past errors, and prevent future transgressions (e.g., checking the provided numbers and avoiding credit debt).

Currently, the services offered by 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.

Learn more about how Avenga managed to use AI and ML technologies to provide the client with a comprehensive tool to use data and turn it into an effective risk anticipation and risk mitigation approach. Ayasdi

Future prospects of AI and ML in financial services

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 AI since they provide extensive datasets for the ML algorithms to process.

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

  • Investments and stock prices are the primary analysis sources for financial institutions and hedge funds. This will gradually change with sentiment and intent analysis, clarifying the bigger picture and how political news, social media, and other sources impact the financial market.
  • According to Deloitte research, distributed ledger technology (aka blockchain) will reshape the current infrastructure and processes within the finance industry. The significant points of attention will be the 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 ML algorithms. Utilizing the possibilities of AI, the bank of the future will be optimized primarily for operational efficiency. As ML algorithms become more advanced, complex diagnostic engines will probably work for the customer’s benefit.

Keeping up with the current trends, increasing user acceptance of fraudulent financial transactions will continue, as will the high demand for a more personalized and humanized approach from financial institutions and businesses. This will transform the regulatory frameworks, expanding the application of AI and ML in finance.

The bottom line

Most leading financial firms and institutions are using AI to some extent. Whether it’s algorithmic trading, fraud detection, marketing research, or customer service, ML 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, increases productivity, and allows for cutting down time and costs. Moreover, AI helps to reach out to customers by offering them the services they want and personalizing their experiences.

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 AI and adopting the “wait-and-see” attitude. Still, the future of the financial sector 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 formulating new regulatory policies and 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.

Contact us, and our teams will translate Avenga’s expertise in fintech and a portfolio of successful projects to help you open the gateway to the AI-powered future. Understanding the dynamic consumer landscape and outlining detail-rich customer insights can help embed the fundamental value proposition in every feature of financial offerings.

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