Technological advancements are part and parcel of modern life, including the business and economic sectors. Artificial Intelligence is changing the way financial services conduct their operations. Ordinarily quite traditional, the financial industry is now leading the way in terms of AI adoption. Industry professionals must deal with petabytes of data regularly. It is no wonder that the industry has latched on to the tech trend so steadfastly.
AI can perform mundane processes on a larger scale and at a faster pace than humans can. Herein lies its great value. What is also quite nifty about AI is the insights it can pick up. Patterns and trends that can inform decision-making processes tend to be seen more clearly by AI.
The financial services sector is ideally placed to adopt AI techniques because it has deep enough pockets to fund R&D innovations in this area. AI is so robust that 70% of financial services firms use machine learning to do everything from guesstimating cashflow trends to detecting fraudulent transactions.
Machine learning stands out as a subset of AI because it offers specific and significant solutions to complex problems. By teaching machines to search for – and recognize – trends in large sets of data, ML can make pretty accurate predictions about what will happen next in any given organizational system. ML is specifically well-suited to the data-heavy finance industry because it thrives on large amounts of data.
ML has been put to various good uses by many financial services firms, including Wells Fargo, which automated its operations by using a chatbot on Facebook Messenger. By using the bot, the company communicates with its customers more efficiently regarding account management and passwords.
Investment practices have also reaped the benefits of increased use of machine learning. Algorithms that identify relationships between large amounts of investment data have helped managers and investors to predict stock market behaviour. When it comes to robo-advising and algorithmic trading, machine learning has made it clear that technology can significantly derange traditional investment banking practices.
Security enhancement has long been an issue in the financial services sector. The increase of third-party users and the numerous daily transactions occurring within the sector make it extremely crucial to provide safe solutions. Machine learning goes a long way to preventing security threats while enforcing compliance.
Anti-fraud measures are part and parcel of financial practices. Banks pay almost three times as much in recovery and restitution for every fraudulent dollar put into circulation. It is crucial for best practices and customer satisfaction that financial organizations minimize fraud as much as possible.
Since the dawn of banking, banks have been tinkering with various ways to counter fraudulent activity. Machine learning algorithms handle fraud very well. By keeping track of recurring patterns, ML can successfully block transaction-related activities that are uncharacteristic for a particular customer. ML performs such tasks at speed and with a precision that is inimitable by humans.
Payment platform PayPal, for instance, uses machine learning to expand its fraud detection abilities. With AI, PayPal mitigates risks associated with customers who do not behave above-board with their financial transactions. The best part about the machine learning algorithm used here is its capacity to detect fraud-related activities in real-time. Without this feature, PayPal would be receiving a report after the fact, that is, once the suspicious transaction has already taken place.
The finance industry’s sluggish attitude towards adopting technology is not without reason. To adopt modern technological methods, it is a key requirement – for any organization – to be agile. It is not as easy for businesses within the financial sector to be as flexible as companies in other parts of the economy. Updating organizational infrastructures to accommodate change takes up a significant amount of time. The long lead times needed to set up a financial institution are the same lengthy leads changemakers must contend with when introducing novel ideas to a financial company.
Furthermore, where financial organizations do express a willingness to adapt, their expectations are often frustratingly unrealistic. While it is true that machine learning algorithms used well, and designed with specific targets in mind, can achieve an accuracy that helps with decision-making processes, ML is not magic.
Machine learning reduces the time involved in completing run-of-the-mill tasks, offers increased exactness, and helps companies to achieve their goal of providing better customer service. Anything beyond these three pillars is an expectation that ML engineers cannot meet, no matter how sophisticated the algorithm they build is. Executives need to learn, and ML data scientists need to be willing to patiently demonstrate, that some human input is still required for ML to meet an organization’s needs fully and efficiently.
Financial industries stand to gain much from applying ML algorithms to their procedures. This much is already clear from the advanced rate at which most companies in the sector have adopted Artificial Intelligence. The trend is likely to remain robust within finance for the next decade, easing the burden of increasing data analysis in the years to come.
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