The Financial Sector needs YOU to Step up

February 12, 2019 | Posted by BPR Team << back to blog

Authored by Roshan James from PGPM Class of 2019, our first blog for this year focusses on the importance of AIMLA (Artificial Intelligence, Machine Learning & Analytics) in the Financial Sector, and how companies and professionals should adapt themselves to these tech advantages that will further enable to progress in the industry.

Digital technology has rapidly evolved over the years and beyond the shift from Radios and TVs to the internet, today we are gradually moving on to the realm of Artificial Intelligence (AI). AI is highly popular among firms today owing to the amount of data they are dealing with, and accordingly market projections predict that more than 60% of the financial services companies are already using or will use AI in a year or two. Most of the major banks, brokerage firms, services firms and institutions are already using AI to add value to and protect their financial assets.

There is no other business sector that is more focused on adopting and implementing AI for improving speed, accuracy, and efficiency as much as the financial industry. At its heart, the AI revolution comprises of machine learning algorithms, a software that self-improves as it is fed high volumes of historical data, a trend that is tailor made for the finance industry. AI in finance is creating opportunities for trillions of dollars and here are the major areas that it bears effect on:

Fraud Detection: Every business aims to reduce the risk conditions that surround it. This is all the more relevant and true for financial institutions. The loan a bank gives you is basically someone else’s money, which is why you also get paid an interest on deposits and dividends on investments. This is why banks and financial institutions take fraud very, very seriously. AI is on top of things 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. Another excellent feature of fraud detection using AI is that the system has no qualms about learning. 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 and what cannot.

Financial Advisory Services: According to the PWC Report, we can look forward to more robo-advisors. As the pressure increases on financial institutions to reduce their rates of commission on individual investments, machines may do what humans don’t- work for a single down payment. Another evolving field is bionic advisory, which combines machine calculations and human insight to provide options that are much more efficient than what their individual components provide. Collaboration is key. It is not enough to look at a machine as an accessory, or on the other end, as an insufferable know-it-all. An excellent balance and the ability to look at AI as a component in decision-making that is as important as the human viewpoint is the future of financial decision-making.

Trading: 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. They 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 stock. One with a lower risk appetite can receive alerts for when the market is expected to fall and can thus decide about whether to stay invested in the market or to move out.

The future

AI in finance is all about continuous learning and re-learning of patterns, data, and developments in the financial world. AI gives the flexibility to build upon the current system or line of financial products and services. There is a steep rise in the requirement of professionals in the financial sector for understanding, implementing and optimizing AI and machine learning and this is where you can be the next big thing.

With more than 30% rise in jobs which require AI, RPA and ML, you have a bright future in the financial sector if you are educated in these fields. The future is now and hence if you are looking to enhance your career or grow quickly AIMLA (Artificial Intelligence, Machine learning, Analytics) is the way to go. From understanding and modeling to predicting and visualizing AIMLA has it all and it is a part of our curriculum here at Great Lakes.

*Image(s) courtesy – Google Images

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