• Barsha Singh

AI Literacy for the Finance Professionals

A computer's artificial intelligence is referred to as AI. It is defined as "the science and engineering of creating intelligent machines, especially sophisticated computer programs." Artificial intelligence (AI) is the process of programming a computer, a computer-controlled robot, or software to think intelligently like humans. In order to construct intelligent software and systems in computers, AI researchers investigate how individuals think, learn, and solve issues. AI stands for Intelligent and Emotional Quotient, and it relates to the establishment of IQ and EQ in computers.

Artificial intelligence (AI) may seem intimidating. Until recently, many people believed that AI was only accessible to those with a PhD and a thorough knowledge of mathematics (and sometimes still does!). This is not the case, though. Yes, if you want to create new AI systems, you'll need this degree of understanding. This isn't required, though, if your objective is to employ AI in your sector (where you have relevant expertise). In this case, all that is required is that you have a sufficient understanding of AI to be able to use it successfully in your domain, that you are aware of the tools and services accessible to you, and that you are aware of the AI rules that apply to your domain. To employ AI in a safe and secure manner.

Financial literacy is not something that many individuals are born with. In a 2019 poll conducted by Scripbox, an online mutual funds platform in India, 72 per cent of Indians were ignorant of how much money they should set away or invest, and 56 per cent indicated they lacked the expertise to efficiently manage their finances. Fears of bankruptcy, growing tuition fees, and aspirations for financial independence grow in most regions of the globe, it is critical that people handle their money effectively. With the increased use of technology and digitalisation, there seems to be a new way to address the problem of financial illiteracy: Artificial Intelligence.

Machine learning can now assist with a variety of aspects of personal finance, from algorithms that help people make better financial choices to making cash transfers easier than before. It has played a crucial role in supporting FinTech, a collaborative business that combines technology and finance, algorithms that make financial planning and literacy more accessible.

Furthermore, machine learning frameworks can monitor fraudulent transactions efficiently, making payment systems safer for consumers. Razorpay's solutions, for example, allow businesses and individuals to utilize a variety of payment methods, including JioMoney, MobiKwik, Airtel Money, Ola Money, credit and debit cards, UPI, and online banking. In an interview with the Economic Times last year, Razorpay CEO Harshil Mathur said that using machine learning and AI allowed the company to provide a better customer experience, increase payment success rates, and make Razorpay safer by issuing transaction limits and having the AI monitor online behaviours—for example, a fraudster might not spend as much time checking shopping details as a typical shopper would. Another example is the Chinese financial services giant Ping An, which used AI to process loan applications. Applicants would be asked to answer a series of questions, with face recognition technology used to determine if they were lying.

Credit card firms often utilize machine learning algorithms to detect irregularities in transactions, which may aid in the detection of fraudulent transactions. In this scenario, AI might connect transactions from two distinct nations, such as Japan and India, to a third transaction from a third location, such as Tokyo's airport, and determine if the transaction is honest. Otherwise, it would be flagged as an anomaly and the user would be notified. Such AI applications serve to make online financial systems safer, which encourages more individuals to utilize them.

Individuals may also use AI to make budgeting and stock trading choices. Apps that assist with debt management are available. Charlie is an AI-powered budgeting tool that started out as a chatbot. The American software analyzes everyday transactions and provides recommendations through a bot that is now dressed as an adorable penguin. It also highlights transactions that exceed certain thresholds and provides for some flexibility in some spending, such as a daily cup of coffee.

There are a variety of SaaS (software as a service) choices where pre-trained AIs may be altered to match your demands if your objective is to have the AIs built and utilized by finance domain specialists with little to no data science background. These are often used for more general services (such as customer-facing chatbots, marketing intelligence, and so on) that do not need your company to provide sensitive data.

There are still numerous tools available that vary from no-code to low-code to code if you need to construct a bespoke AI that learns from your data. Here are a few instances, but there are many more. Furthermore, the AutoML trend has enabled many professionals to use a wide variety of AI algorithms without needing a comprehensive grasp of how they work (or the code expertise required to program them). It is, nonetheless, beneficial to know which algorithms are appropriate for certain use cases, especially if your company or the use case is subject to industry laws.

Fintech Companies' Contribution to Financial Literacy –

In our country, technological innovation has become a paradigm shift in financial and banking services. In this context, fin-tech companies have played a critical role in disrupting the expansion of fin-tech in customized banking and insurance services in recent years. Various parties, such as the government and financial organizations, profit from this success.

Financial counselling in the future

AI is already making inroads into the stock market. Many AI-powered programs, such as Kavout and Blackbox Stocks, are now accessible to assist consumers with stock trading. These platforms hunt for news and other web data—which they can process quickly—to create insights on stock forecasts, strategies, and portfolio and market analysis using ML and AI-powered algorithms. These tools are being used by many hedge fund managers and dealers.

modelling could AI, with its many talents, eventually replace financial advisors? Despite the significant advances AI has made in personal finance, it seems that AI will not totally replace humans. For starters, many consumers are concerned about turning over personal data to AI-powered apps, particularly when it comes to technology like face recognition.


Second, when it comes to time-series modelling, ML algorithms still fall short of outperforming conventional approaches. We can't hard code the real-world uncertainty. Not yet, at least. Unlike a computer, human modelling may act on the fly and be creative with their ideas, but a machine would keep setting regulations. Simultaneously, using AI techniques may help to eliminate human mistakes and biases. The AI technologies listed above can certainly promote financial literacy and help financial advisers do their jobs better. In such a future, a person may utilize budgeting applications, send money to their parents through messenger, and then go to financial advisers who can use machine learning algorithms to improve their insights and plans. While fully automated financial advice is implausible, many individuals and businesses have embraced automation in different parts of financial planning, indicating that AI in financial services is here to stay.

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