The Avenga Team
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.
→ Explore our take on Customer experience in the financial industry
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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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:
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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:
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.
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.
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.
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.
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.
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.
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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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:
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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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.
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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.
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