One of the most exciting industries that AI has influenced is banking and finance. AI developers have identified multiple use cases for automation and machine learning in financial services and money transfer operations.
The financial services industry is all about making good financial decisions. Be it the bank or the customer, everyone wants to make the best bang for their buck. With the advancement in business intelligence and data science, we now know that the answers to all our questions lie in clean, consolidated, and insight-offering data. For financial institutions to gain an advantage today, they need to leverage the power of data, something that’s often called the lifeblood of Artificial Intelligence and Machine Learning models.
In the past few years, Artificial Intelligence has shown how it can revolutionize virtually every industry. Here’s how AI will continue to influence the finance space:
Artificial Intelligence has paved the way for a higher emphasis on customer satisfaction. With user data, banks can understand the type of customer they’re catering to, and suggest personalized services accordingly.
Banks collect user data from customer transactions, subscriptions, investment plans, etc. Today, banks can acquire even more information using chatbots. Banking chatbots allow customers to voice their concerns and receive immediate attention. Banks can collect such queries from multiple customers for gaining insights. For example, if many customers complain about not being able to perform a certain type of online transaction, banks can decide to make the process simpler by optimizing their webpage/user interface.
Such chatbots can also perform quick transactions, provide balance sheets, etc.
Personalized banking can also help customers achieve their financial goals. Based on a customer’s financial information, banks can provide personalized advice and an objective route to monetary success. Personalized services such as bill payment reminders, expense planning, etc. can go a long way in increasing customer satisfaction and brand loyalty.
Credit scoring makes lending decisions simpler for banks and money lenders. Such scores are determined using data representing your payment history, current debt, credit length, new credit, and credit types. Each of these data points hold different weights while determining a customer’s credit score.
Today with Artificial Intelligence, the credit scoring system can benefit from higher objectivity. AI models provide a more accurate representation of a customer that wants to borrow money, all at a far lesser cost. AI-backed credit scoring processes can function on a higher level and a more complex set of rules. Using such a credit scoring system, banks and money lenders will have a better understanding of how much money can be made of money lent. With AI-backed credit scoring, banks can distinguish between applicants that perform regular late payments and customers that pay bills on time. Such a credit scoring system can also provide a more accurate score to young applicants who do not have a long history of borrowing money.
Markets fluctuate and economies rise and fall. These behaviors are influenced by factors such as the sub-prime mortgage crisis (that led to the 2008 financial crisis), and the coronavirus outbreak that is causing the on-going 2020 stock market crash.
Machine Learning algorithms can learn from historical data and identify patterns. Such systems can locate economic threats at an early stage and create a call for mitigation. This will ensure that the financial mistakes of the past never happen again. AI/ML models can perform high-level market analysis that would take ages for humans to perform. As a result, the machines can alarm us about potential issues the economy could face, thus giving us the time and the data resources to tackle it.
Another important activity in banking is locating financial fraudsters. As mentioned earlier, AI systems that can assign highly representative and accurate credit scores can also detect users known for fraudulent activities. AI-backed fraud detection systems analyze a customer’s transactions and purchasing habits. If AI systems detect unusual activities (such as the sudden withdrawal of an unusually large sum of money/expensive purchases unlike regular activity), they can trigger the required security mechanisms, thus saving the customer from being de-frauded.
We have witnessed an increase in the number of data-driven investments in the past couple of years. With data, everyone has an objective reason to make a decision, and this includes trading on the stock market.
Also known as algorithmic, quantitative, or high-frequency trading, it has allowed for more fact-based reasoning behind investing. AI models view data with ultimate objectivity, and human flaws such as confirmation bias don’t influence AI models. So, when an AI model suggests a certain lucrative investment, you can rest assured that the suggestion is based on exhaustive stock market research.
Trading floors also benefit from AI-inspired solutions since such systems save time, and we all know that time is money.
While dealing with money, we can’t compromise on accuracy. Artificial Intelligence provides that very solution to the financial services industry. Future predictions for AI in finance show the industry is rapidly changing, and processes are being revolutionized left, right, and center.
From AI-backed credit scoring to personalized banking, Artificial Intelligence has forced every major banking institution to become a tech company. Interestingly, Goldman Sachs, one of the world’s largest investment banking enterprises, employs more software programmers and tech engineers than Facebook! It’s a growing sign of the future of services in finance, who’s “a” today stands for none other AI.
Subscribe now and get updates on the latest happening in the world of AI & Big Data, what's happening at Bridged & much more!