Long gone are the days when you had to wait in line or make an appointment to talk to a financially savvy consultant. Now you can have one in your pocket 24/7 for a fraction of the cost. Although most people are afraid of automation, it is not only inevitable but desirable. Humans are not able to make split-second decisions with the same accuracy as machines, and most specialists would not be happy with the pennies they would get from such transactions. The future of finance lies in AI.

It’s only human to make errors, but it’s unacceptable from a money-handling perspective. AI systems have excellent accuracy, predictability and can ensure compliance without becoming tired or bored. A byproduct of them is even more data which can be used for training and calibration.

Furthermore, such systems generate significant cost cuts after the initial set-up of the system, which can be quite expensive. Yet, the 24/7 operating schedule, low maintenance cost and, in the case of AI, the possibility of self-improvement, can easily justify the investment.

Using robo-advisors brings scalability to the process. Once the initial set-up is in place, it only costs a fraction to add thousands of more users. It gives the adopting company a competitive edge. The same algorithm is replicable even in other situations, for example for detecting fraud risk for cards and prevent identity theft, which makes it very cost-effective.

For example, in the case of risk management, the AI looks for patterns in data, keeping in mind that people tend to have habits, like accessing from the same device, around the same time interval and usually from the same geographical area. If any of these variables is seen as an outlier, it will alert the user and ask for other verifications. The great gain for the bank is that it protects it from important loses and image damage. Even if the amounts are insured, the scandal following a security breach is more important.

The primary challenge of AI systems is getting quality data to train them, because it’s a matter of garbage in, garbage out. Although all financial institutions have heaps of data going back a few years, these are not always ready to be used on even on a digital format, as compliance regulations required a lot of hard copies. These include information on clients, accounts, transactions, payments made and payments due, credit scores, default risk and more.

Even the data which is already in computers or servers are not entirely usable since it’s usually stuck in different isolated silos. To become a raw matter for AI training, it needs to be uploaded in the cloud, on a distributed file system, ready to be handled by Hadoop.

Not only getting the data online is a problem, next comes the quality. Of course, structured data is easy to handle, but it does not contain all the information a robo-advisor should offer like on the spot market variations or even panic-selling which could be detected easier.

The trend of offering robo-advisors is growing, and some institutions have already taken some important steps in this direction. Most are digital assistants or operate as chatbots replacing customer service assistants. Others deal with fraud prevention and work in the background. Some of the most interesting so far are:

Although usually a slow technology adopter, banking and finance are catching up with technological trends. We can expect to see more robo advisors in different areas, from front-desk to back-office, from personal finances to high-frequency trading. This tendency is dictated by the new generations which are digital natives and are most comfortable with operating cloud-based apps instead of dealing with clerks.

The post AI, Automation and Robots: the Digital Future of Banking appeared first on CoinSpeaker.

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