How Three EU Companies Implement AI in Personal Finance
Financial inaccuracy is one of the significant reasons clients turn to artificial intelligence apps
With the appearance of artificial intelligence (AI), the world of personal finance and investing is rapidly changing, adapting to the new reality of managing money. ChatGPT, one of the recent breakthroughs in the AI field, can provide information and guidance on various financial topics such as budgeting, saving, investing, and even retirement planning.
Thanks to data analytics, machine learning, and natural language processing (NLP), ChatGPT offers a personalized experience, an innovative and cost-effective approach, and diverse investment solutions. Yet, some bottlenecks are restraining the tool from perfection, including data accuracy and interpretation, EU regulations such as GDPR, and privacy laws.
This article will explore AI's reliability in personal finance, compare it to traditional investing models, and showcase some companies already implementing artificial intelligence in their business ecosystems.
AI in personal finance and investing: How far have we come?
Let's define artificial intelligence and understand how it could relate to finance, primarily investing. TechTarget states, "Artificial intelligence is the technology that mimics human intelligence in machines or systems, enabling them to perform tasks that require reasoning, learning, and problem-solving." Simply put, AI enhances different work areas, eliminates potential problems, and increases productivity and accuracy.
In personal finance, experts determine artificial intelligence as a valuable tool used to "automate and optimize various financial activities, such as budgeting, saving, investing, trading, and risk management," explains the Money Added portal. Nevertheless, the ability of AI goes beyond that as it analyzes large amounts of data, including market trends, company performance, customer behavior, and ESG metrics, allowing investors to make decisions faster and more effectively.
On top of that, AI can provide a piece of personalized financial advice based on the user's preferences and goals, making investing more straightforward and accessible to the majority of people. Let's discover some examples in Central and Eastern Europe.
Different AI tools used in personal finance
In 2023, most AI tools used in personal finance are "software applications that use artificial intelligence to perform personal finance and investing tasks," notes MobiDev. Let's analyze some vivid examples of such apps and the functions they can perform with the latest technology that is changing the world of finance.
Trade Ideas is an AI trading app where users can learn how to approach trading and investing in the stock market. It uses "a simulated trading platform and an AI software called Holly to generate trade ideas and automate trading strategies." LikeTrade Ideas, apps could monitor the market and adjust the user's portfolio by rebalancing assets, hedging risks, and taking advantage of opportunities.
Scienaptic AI is an excellent solution for lenders to make better credit decisions, thanks to alternative data and machine learning that analyze several factors, including income, expenses, behavior, and social media. The evaluation assesses borrowers' creditworthiness and suggests the most optimal solutions. In addition, by interacting with users via enhanced engagement, AI tools can answer questions, explain concepts, and provide guidance with the help of data visualization and gamification, making personal experience more advanced and personal finance more understandable, noted Forbes.
Mint is a must-have for everyone who wants to own their assets, not owe them. According to PC Mag, this personal finance app helps users manage their money, budget, and bills with the help of artificial intelligence that categorizes transactions, tracks spending habits, and provides personal insights.
Artificial intelligence or cutting-edge technology
The European Union also followed the trend of artificial intelligence in the market, and several companies have implemented AI in their business activities. However, there is an ongoing debate about whether businesses actually use artificial intelligence or just say so, referring to cutting-edge technology. We spoke to some technology-driven companies, and here is what we found out!
Finax is a Slovak securities trader offering online investing in index ETFs, which made it the first robo-advisor in Central Europe managing client assets. "The idea behind Finax was to offer effective investing to the retail masses. We wanted to change the narrative that investing is for the rich and that good investments are unavailable to small investors," said Radoslav Kasík, co-founder and Chief Investment Officer at Finax. The company aims to increase financial literacy and teach people how to keep their finances in good shape, leading to well-being and a better society.
Finax is a fintech company that uses robo-advisors in its business ecosystem but hasn't implemented artificial intelligence. "We have been following AI development, and we regularly evaluate its usage in our activities, but we haven't found a proper and effective use yet," underlined Kasík. The corporation uses other technology, with the help of which Finxax was able to scale up the business, automate some business processes, and make their services cheaper and more accessible to the masses.
Nevertheless, Finax sees a bright future ahead, as the investment market is still underdeveloped in Central Europe, and the corporation considers the growth potential of the entire industry. Kasík believes that "private investing will play a more crucial role in our lives as our incomes will rise, state pensions will decrease, and the social security network is becoming unsustainable."
Another company with forward-thinking practices is Zonky, a pioneer in the Czech market for peer-to-peer loans and a part of the PPF Group and Air Bank. Roman Macháček, a Deputy Spokesperson for Air Bank and Zonky, explained the company's principle as a way "to connect people who need to borrow with people who, in turn, want to value their money by investing in these loans." In other words, Zonky works on the P2P principle, serving as an intermediary between lenders and borrowers, allowing people to solve everything online, from the application to the signing of the contract.
Zonky uses different technology to improve its services, including artificial intelligence for faster document reading and machine processing of bank statements. "We were even the first in the Czech fintech industry to obtain a PSD2 license in full form from the Czech National Bank in September 2019, bringing automation and speeding up the assessment of client credibility," shared Macháček.
The popularity of loans grows consistently among the Czech population, and Zonky's future is pretty straightforward: "Zonky can offer clients more favorable loan conditions and at the same time do so simply and safely, which is exactly what people want and look for in loans."
Twisto, a startup that evolved into the thriving company we covered in one of our previous articles, is a vivid example of a successful fintech company using artificial intelligence in its exosystem. As of 2023, Twisto is "a fully-fledged daily payments app where customers can use Twisto for their daily spending online and in brick-and-mortar stores," said Michal Šmída, founder and CEO of Twisto.
Interestingly, Twisto has used artificial intelligence since the first day of operations. In particular, Šmída emphasized that Twisto uses "a tool built based on machine learning to score the customers, and today AI also helps us work with data, and we are starting to use it in marketing for content creation." As a result, Twisto seems to remain in demand in the future, and interest in companies' services will continue to grow and offer new solutions for online and offline shopping.
Limitations and reliability of AI
The future of personal finance seems bright with the dominance of artificial intelligence in the industry. However, generative AI tools have limitations and challenges, and they may only sometimes create accurate or reliable content and could mislead the end user. So what are the common hurdles?
First, poor data quality could lead to misleading outcomes and errors in the AI application. In most cases, this problem arises most often as AI tools rely on a significant amount of data in the learning phase. Usually, such data may need to be more accurate, reliable, and unbiased. In the case of ChatGPT, you must remember that it has a limited knowledge base that is updated until 2021, meaning that the data could be inaccurate. That is why it "may not be able to provide advice on financial products due to legal restrictions."
Transparency and explainability are other common issues faced by most AI tools. For example, the article Toward Data Science article explained that "AI tools often operate as black boxes, meaning their logic and reasoning are not easily understandable or interpretable by people." That is why it leads to such problems as trust, accountability, compliance, and other ethical matters that are highly regulated in the finance industry.
Furthermore, some people may assume that AI tools are meant to replace the labor force in the finance industry, creating the problem of human oversight. However, this is far from reality, as AI tools are designed to assist people in decision-making, suggesting potential solutions to the problem. It is essential to understand that AI cannot fully replace humans, but it could be a valuable tool for them.
Finally, management is crucial in implementing AI tools in finance. Some resistant stakeholders in the company could be acting against adopting new technology. While it remains one of the most standard issues in any industry, it has persisted for a while.
AI vs. traditional investing models
Even though artificial intelligence uses machine learning algorithms to analyze data and make investment decisions, it faces data reliability challenges, causing some people to be skeptical of the technology. So what is the significant difference between AI and other traditional models, and why is artificial intelligence most likely to outperform them?
According to Forbes, the traditional model includes human financial advisors providing customized financial advice and guidance to clients based on their expertise and finance experience, per their needs, preferences, and goals. Then, like AI, they can create a financial plan and ways to implement it, with periodic market reviews and adjusting the investment portfolio.
While the work of financial advisors has the same scope as that of AI financial apps, clients often face difficulties with high costs, limited availability, human errors, conflicts of interest, and regulatory compliance. On top of that, financial advisors could make mistakes and misjudgments by using their biases and emotions, which may not be aligned with client needs and expectations.
Financial inaccuracy is one of the significant reasons clients turn to artificial intelligence apps that can process and analyze large amounts of data without relying on the human analyst to gather the information, which could be time-consuming and prone to errors. And by combining the strengths of AI with the insights of financial analysts, it is more than possible to create a comprehensive investment strategy that would be the most accurate, efficient, and effective.
The future of AI in personal finance is promising, as it has the potential to automate and optimize many financial processes, from fraud detection to portfolio management, ultimately leading to better decision-making and increased efficiency in the industry.