Artificial intelligence (AI) has pervaded almost every industry, and banking and financial services stand as no exception. Institutions in the financial sector are employing AI to facilitate data-driven decision-making, automate repetitive tasks, improve data processing speed and accuracy, minimize operational expenses, refine customer service, and more.
According to a recent survey, 70% of financial services firms are already using machine learning to predict cash flows, improve credit scores, and detect fraud. And these figures will grow exponentially in the days to come.
In this blog, we’ll discuss a few key areas in the financial services industry where AI is making a tremendous impact, and offering additional value over conventional methods.
Credit Scoring
Several financial institutions including banks and NBFCs are in the practice of lending money to customers. To do so, they need to evaluate the creditworthiness of the borrower.
Such assessments have traditionally been done by analysts who use a scoring method that takes into account the past performance of the borrower. The conventional approach can make the process painstakingly lengthy and introduce human bias too. AI allows for a quicker, more accurate evaluation of the creditworthiness of a potential borrower.
AI systems use complex classification algorithms that use a large number of variables (eg. income, savings, demographic data, credit history, transaction history, etc.) to calculate a score that determines if an individual will receive the loan or not.
This allows lenders to easily differentiate between creditworthy and high-risk individuals, and make informed decisions. It also allows individuals without a long credit history to establish trust and borrow from a potential lender.
Fraud Detection
In recent years, financial institutions have seen a surge in fraudulent activities, primarily on account of the growing number of online transactions, increased popularity of e-commerce, and third-party integrations. In India alone, frauds to the tune of 60,414 INR were reported in the financial year 2021–22, reveals a report by the Reserve Bank of India (RBI).
Earlier, financial institutions used to tackle fraud using hardcoded rules created by experts. The key drawback with this method was that these rules could be easily discovered and exploited by fraudsters. So, the rules were gradually replaced with AI-based models that can learn from historical patterns and evolve over time.
AI models can sift through hundreds and thousands of transactions and study features such as the client’s past behaviour, spending pattern and location to spot transactions that seem anomalous.
While traditional machine learning techniques such as logistic regressions and decision trees can detect fraudulent activities reasonably well, industry experts constantly strive to push the envelope and develop more sophisticated algorithms that can identify complex relationships that may not be evident otherwise. As neural networks can learn on their own, they hold the potential to transform this area.
Algorithmic Trading
Since AI models can sift through large volumes of data to discern patterns, they have been found to be immensely useful in algorithmic trading where AI-based trading systems can make split-second buy or sell decisions and automatically execute trades based on the pattern identified.
Needless to say, these automated systems vastly outperform humans who can never be as fast or accurate. In fact, according to a study, AI-driven hedge funds produced cumulative returns of 34% during a 3-year period from 2016–2019 in contrast with the 12% returns from the global hedge fund industry for the same period.
The performance of these automated trading systems can be further improved through integration with other data sources. For instance, NLP-driven sentiment analysis using the data extracted from newspapers, social media conversations, and industry forums can be used to refine buy-sell decisions.
Personalized Banking
In the banking sector, providing personalized customer service is the key to building long-term relationships and winning customer loyalty. And AI is helping them achieve the same in several ways. NLP-based chatbots, for instance, are intelligent enough to understand the intent of a customer and guide him in the right direction. They can even gauge customers’ emotions and adjust their responses accordingly. So, if they find a customer is angry or frustrated, they will try connecting him to a human agent, so that the issue at hand gets resolved immediately and doesn’t metamorphose into something bigger.
Several financial institutions leverage AI-driven forecasting tools to analyze customers’ income, bank balances, spending patterns and upcoming obligations to advise them on how they should spend their money, when they should or shouldn’t make a purchase, and so on. These tools can not only help these institutions cross-sell their services but also gain a loyal customer base.
Process Automation
Process automation through AI can help banking and financial institutions improve productivity as tasks that may otherwise take days can be done in hours or minutes. Activities such as document digitization, form processing, or extraction of relevant information from forms that were typically performed by humans are being automated using advanced optical character recognition (OCR).
By automating mundane and repetitive work, financial institutions enable their workforce to concentrate on crucial aspects of their job; this helps boost employee satisfaction. Additionally, automation ensures a high degree of accuracy and minimizes errors at every step of the process.
Wrapping Up
In the blog, we covered a few major areas in finance that can benefit immensely from AI integration. That said, AI deployment in finance isn’t easy by any means and comes with a host of challenges. e.g. corrupted or biased data which when fed into a machine learning algorithm can have dire consequences, as it may cause a bank to decline an individual’s loan application or a stock trader to incur heavy financial losses. That’s why it’s important to have clean, well-curated data for financial models. Notwithstanding these challenges, we can be certain that we are on the brink of an AI-powered disruption that will affect each of us in one way or the other.
We at CellStrat have also dealt with insurance clients and have been able to generate savings of more than USD 600,000/ year and small invoicing related clients giving them savings of close to USD 15,000/ year. Contact us if you would also like to get some of your financial problems solved with artificial intelligence solutions.
