Machine learning: an emerging revolution in the financial sector?
Far from just a mainstay of science fiction, artificial intelligence has now entrenched itself in many aspects of our lives. At a relatively rudimentary level, AI affects how the Amazon homepage is personalized to suit different viewers and the way that Google translates websites seamlessly.
However, a fresher strand of AI, machine learning, goes further. Whereas humans have long been required to input rules into machines to govern their behavior, machine learning enables machines to draw their own implicit conclusions by analyzing data.
Machine learning is taking effect in a range of sectors, but the emerging possibilities for the financial sector are especially tantalizing. Here are just a few examples…
Allowing banks and customers to interact in a multilingual fashion
While you might usually deem communicating with your bank a straightforward endeavor, this is not always the case in emerging markets. In these territories, there can be something of a patchwork of languages in common use. South Africa, for example, has 11 national languages.
This is problematic because, though English remains the most commonly used language for interacting with banks, English is the second or third language for many people. AI could ease this situation with voice responses comprehensively using local dialects, says The Financial Revolutionist.
Expanding variety and flexibility in products
Another way in which AI can help to break down language barriers is by enabling chatbots to use these native dialects. Along similar lines, AI could allow for an advisor finbot that lets customers pick from options other than the usual standardized products.
This would solve a common problem with banking: that of banks with overly uniform offerings that fail to meet many customers’ very particular needs. However, with an advisor finbot, a bank could tailor its terms and services to take both the customer and their context into close account.
Injecting more elasticity into services, too
On the initiative of micro-entrepreneurs, the digitization of banking operations has even extended to emerging markets. If you work for an AI-powered bank, you could capitalize through setting up bots which are each capable of providing practical advice to a specific group of customers.
This could mean you set aside one bot for farmers and another for retailers… and so the pattern can continue. As these bots learn from the customers that they serve, the bots could even coach entrepreneurs seeking to expand their skills base and increase their productivity.
Preventing and recovering from the financial crime
Financial crime is a risk to which many businesses in the financial sector are worryingly prone. However, Risk.net has enthused that “the machine-intelligence revolution promises to be fruitful on multiple fronts in the war against dirty money”.
Machine learning could, for example, be used in screening potential employees and customers, preventing financial crime occurring and, if such crime happens regardless, analyzing the crisis that has emerged in the wake of the event.
Making accurate financial forecasts
Many financial firms rely on accurately predicting outcomes for customers and businesses. However, these financial companies could be hamstrung in their efforts without the use of predictive modeling, for which many of them continue to use the Microsoft spreadsheet software Excel.
This software is notable for being equipped with a range of self-service analytical tools which assist businesses in building sophisticated models for financial predicting. However, by transferring their Excel workloads to the Microsoft Azure cloud platform, organizations can further ease their efforts.
This move would also let them utilize Azure Machine Learning, the RedPixie site explains. This cloud consultancy’s services are now included in the HPE finance in the cloud services. The Hewlett Packard Enterprise website further details benefits of these offerings.
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