“A year spent in artificial intelligence is enough to make one believe in God.” — Alan Perlis
AI has impacted every banking “office” — front, middle, and back. That means even if you know nothing about the way your financial institution uses complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, chances are that you’ve probably interacted with its customer service chatbot, which runs on AI.
Here are some other areas you might have not known about.
Since the very basis of AI is learning from past data, it is natural that AI should succeed in the Financial Services domain, where bookkeeping and records are second nature to the business. Let’s take the example of credit cards. Today, we use a credit score as a means of deciding who is eligible for a credit card. However, grouping people into ‘haves’ and ‘have-nots’ is not always efficient for the business. Instead, data about each individual’s loan repayment habits, the number of loans currently active, the number of existing credit cards, etc. can be used to customize the interest rate on a card such that it makes more sense to the financial institution that is offering the card.
This is where AI comes in. Since it is data-driven and data-dependent, scanning through these records also gives AI the ability to make a recommendation of loan and credit offerings which make historical sense. AI and ML are taking the place of a human analyst very fast as inaccuracies that are involved in human selection may cost millions. AI is built upon machine learning which learns over time, less possibility of mistake and analyzing vast volumes of data. AI has established automation to the areas which require intelligent analytical and clear-thinking.
Fraud Detection And Management
Every business aims to reduce the risk conditions that surround it. This is even true for a financial institution. The loan a bank gives you is someone else’s money, which is why you also get paid interest on deposits and dividends on investments. This is also why banks and financial institutions take fraud very, very seriously. AI is on top when it comes to security and fraud identification. It can use past spending behaviors on different transaction instruments to point out odd behavior, such as using a card from another country just a few hours after it has been used elsewhere, or an attempt to withdraw a sum of money that is unusual for the account in question. If it raises a red flag for a regular transaction and a human being corrects that, the system can learn from the experience and make even more sophisticated decisions about what can be considered fraud.
AI is especially effective at preventing credit card fraud, which has been growing exponentially in recent years due to the increase of e-commerce and online transactions. Fraud detection systems analyze clients’ behavior, location, and buying habits and trigger a security mechanism when something seems out of order and contradict the established spending pattern.
Investment companies have been relying on computers and data scientists to determine future patterns in the market. As a domain, trading and investments depend on the ability to predict the future accurately. Machines are great at this because they can crunch a huge amount of data in a short while. Machines can also be taught to observe patterns in past data and predict how these patterns might repeat in the future.
While anomalies such as the 2008 financial crisis do exist in data, a machine can be taught to study the data to find ‘triggers’ for these anomalies, and plan for them in future forecasting as well. What’s more, depending on individual risk appetite, AI can suggest portfolio solutions to meet each person’s demand. So a person with a high-risk appetite can count on AI for decisions on when to buy, hold and sell a stock. One with lower risk appetite can receive alerts for when the market is expected to fall, and can thus decide whether to stay invested in the market or to move out.
Intelligent Trading Systems monitor both structured (databases, spreadsheets, etc.) and unstructured (social media, news, etc.) data in a fraction of the time it would take for people to process it. And nowhere is the saying “time is money” truer than in trading: faster processing means faster decisions, which in turn mean faster transactions.