AI Can Be Your Next Financial Guru
Financial literacy is not something that comes naturally to a lot of people. In India, a 2019 survey by Scripbox—an online mutual funds platform—found that 72 per cent of Indians were unaware of how much money they were to put aside or invest; 56 per cent of the respondents said they lacked the knowledge to handle personal finances effectively. With fears of bankruptcy, rising costs of colleges, goals of financial freedom increasing in most parts of the world, it is imperative that individuals manage their money well. Today, with the skyrocketing employment of technology and digitisation, there appears to be a new approach to tackle this challenge of financial illiteracy: Artificial Intelligence. Today, machine learning can help with multiple facets of personal finance, ranging from algorithms that guide individuals to make better personal financial decisions to make cash transfers more effortless than ever. It has also been pivotal in facilitating FinTech, a collaborative industry between technology and finance, algorithms to make financial planning and literacy more accessible. Furthermore, machine learning frameworks are capable of effectively tracking fraudulent transactions, making payments systems safer for users. For example, Razorpay’s solutions enable corporates and other individual users to use most payment access modes such as JioMoney, MobiKwik, Airtel Money, Ola Money, credit and debit cards, UPI, and net banking. Harshil Mathur, Razorpay’s CEO, said in an interview with the Economic Times last year that employing machine learning and AI allowed the company to provide a better customer experience, higher payment success rates and allowed them to make Razorpay safer by issuing transaction limits and having the AI monitor online behaviours—for example, a fraudster would perhaps not spend as much time checking shopping details as a typical shopper would. One can see another instance of this with the Chinese financial services firm Ping An which applied AI for loan applications. The service would ask applicants to answer a series of questions and use facial recognition technology to discern whether the applicant is lying. Even credit card companies often use ML algorithms to find anomalies in transactions, which can help track down fraudulent transactions. AI, in this case, could link transactions made in two different countries, say Japan and India, to a third transaction made at, say, Tokyo’s airport and decide that the transaction is an honest one. Otherwise, it would trace it as an anomaly and alert the user. Such uses of AI help make online financial platforms safer, thus encouraging more people to benefit from them. Source: Charlie Individuals can also employ AI to make decisions in budgeting and stock trading. One can find apps that help them tackle their debts. Charlie is one such AI-driven budgeting app that began as a chatbot. The American app analyses daily transactions and gives people advice as a bot posing as a cute penguin at the moment. It also flags transactions that go beyond decided limits and allows for certain flexibility in certain expenses, e.g. a daily cup of coffee. Whereas, Wizely is an Indian alternative to a financial savings app. It also uses AI and ML algorithms, which it uses to give catered financial recommendations to users and allows them to take a ‘financial wellness test’ to get a personalised financial plan of action. (Source: Cleo) Cleo is another AI-powered Messenger based financial assistant that allows users to manage their finances. allows users to link their bank accounts and send money to Facebook Messenger contacts. Cleo allows users to keep track of savings by allowing them to either manually select a particular amount as savings, or autosave—where Cleo’s AI sees how much someone can save in a week and adds that amount to their Cleo wallet (unless they refuse to do so). Finally, one can also ask Cleo for budgeting advice or whether they can afford to purchase something, and can even expect the AI to roast them if they do not stick to their routine. The future of financial advising We can already  see AI tools foraying into stock markets. Many AI-powered tools, such as Kavout and Blackbox Stocks, are available today to help make trading in stocks easier for people. These platforms use ML and AI-powered algorithms to look for news and other online data—which they can crunch quickly—to generate insights on stock predictions, strategies and provide analyses on portfolios and markets. Today, many hedge fund managers and brokers use these tools for their work. So, with its myriad of capabilities, could AI replace financial advisers? Despite the many advancements AI has made in personal finance, the likelihood of AI completely taking over seems low. For one, privacy is an often-cited concern many people have with handing over personal data to AI-powered applications–especially when it comes to technologies such as facial recognition. Secondly, ML algorithms still fall short of outclassing traditional methods when it comes to time-series modeling. We cannot hard code uncertainties of the real world.  At least not yet. A human advisor could also act on the spot and be innovative with their plans, unlike a machine that would stick to standard rules. At the same time, integrating AI tools can reduce human errors and biases. The AI tools mentioned above can definitely encourage financial literacy and enhance financial advisors’ work. In such a world, an individual would be able to use their budgeting apps, transfer money to their parents via messengers, and then go on to financial advisors who can effectively use ML algorithms to better their insights and strategies. Therefore, while completely automated financial advising seems unlikely, many people and firms have welcomed automation in various aspects of financial planning, signalling that AI in financial services is here to stay.
Financial literacy is not something that comes naturally to a lot of people. In India, a 2019 survey by Scripbox—an online mutual funds platform—found that 72 per cent of Indians were unaware of how much money they were to put aside or invest; 56 per cent of the respondents said they lacked the knowledge to […]
Machine learning frameworks are capable of effectively tracking fraudulent transactions, making payments systems safer for users.
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["Go", "machine learning", "artificial intelligence", "AI", "ML", "Git", "RAG", "Ray", "GAN", "R"]
["AI", "artificial intelligence", "machine learning", "ML", "Ray", "RAG", "R", "Go", "Git", "GAN"]
I am an economics undergrad who loves drinking coffee and writing about technology and finance. I like to play the ukulele and watch old movies when I'm free.