By Arvind Tiwari
Over the past few years, Artificial Intelligence has been the buzzword with companies working overtime to ensure they don’t lack behind in keeping pace, lest they stare at obsoletion due to the pace of progress. The advent of Chat GPT was the inflexion point in mass awareness of the potential of this technology. From cars to space tech, lending to investing, education to climate change AI is being used everywhere in some form or the other and the pace of adoption is so strong that leaders like Elon Musk have publicly demanded a cautious and restrictive approach to AI worrying about its threat to humanity itself.
Technologies within AI like Natural Language Processing (NLP) is revolutionizing the way customers interact with financial services. AI-powered chatbots, like ChatGPT, are becoming more and more common, making it easier for individuals to access information about their portfolios and explore new investment opportunities. This not only enhances the customer experience but also provides a convenient and efficient means of communication.
With regard to financial services the loan industry, which is making lending safer by analysing trends from historical data, is the one that has been most disrupted and benefited by AI. Machine learning algorithms assess a borrower’s creditworthiness more accurately and efficiently than traditional methods. This is not only reducing the risk for financial institutions but it also opens doors for individuals who may have been excluded from traditional lending due to a lack of credit history and no other geography better than India and south Asia to bridge the gap and ensure that credit reaches the needy.
However other aspects in the financial services sector like investments have not kept pace with the AI boom due to multiple causes viz. 1) Security concerns expressed by individuals, 2) Long gestation profitability for companies that use this technology ( Investment Advisors Vs product distributors), 3) Lack of adequate data ( which is slowly getting addressed especially in India with regulations like the Account Aggregator
One of the most compelling aspects of AI in finance is its ability to personalize financial services. AI models trained to map socioeconomic variables to individuals can determine personalized risk profiles and ideal asset allocations, taking into account their societal and lifestyle factors. The result is unmatched: individuals receive recommendations and advice tailored to their unique circumstances, enabling them to make informed choices and better manage risk or seek higher returns.
Let’s take some examples, 5 years from now we would see investment advisors and their proprietary algos be able to ensure that individuals don’t invest outside of their risk profiles, ensure that due to the influx of information they are able to dissect the right advise and action them, advise is generated basis a large set of data, domestic and global so that no angle is missed out. (Viz. If an individual is bearish on global oil prices, technology can ensure that his/her portfolio doesn’t have any investment directly or indirectly effected by oil prices as raw materials) This is but a very small example of what AI can achieve in investment tech.
It is also used in various aspects of financial operations, from image processing for document analysis to real-time monitoring and alerts. These applications streamline processes, reduce errors, and improve the overall efficiency of financial institutions.
Despite its tremendous potential, the adoption of AI in finance is not without challenges. Data security and privacy are paramount concerns, particularly given the sensitivity and value of financial data. Ethical concerns arise when AI is entrusted with decision-making, including the allocation of responsibilities and transparency in its decision-making processes.
Resolving these challenges one by one, the future of AI in finance looks exceedingly promising. The progress made in fields like image and language processing, where it’s increasingly challenging to differentiate artificial from real, serves as a testament to AI’s potential. While the future remains uncertain and unpredictable, AI empowers us to model, predict, and make informed decisions. As we reach for the stars and tackle epidemics, AI will enable the development of more reliable financial services and equip us to confront financial crisis with greater resilience.
(The author is Arvind Tiwari, Head of Tech and System Architect, GoalTeller, and the views expressed in this article are his own)
