Prediction for 2018: Artificial Intelligence takes off in banking
2018 may be the year that banks, feeling pressure from fintech competitors and from customers expecting to get personalized services 24x7, accelerate building Artificial Intelligence (AI) into their processes and operations.
Agorithms with human-like learning and flexible decision making capabilities are transforming business. They are recognizing complex patterns, synthesizing information and doing many functions such as forecasts--activities that not long ago were assumed to require human cognition. Meanwhile social networks, mobile phones and cloud services have created an explosion of data to feed these AI engines. Compared to other industries, banks start with an advantage in AI readiness. First, they possess large data sets and have decades of experience using analytical tools, building models and employing teams of software developers and, more recently, data scientists.
Second, the public is ready for everyday AI-based services like banking: studies show that the public expects machine intelligence to permeate daily life within the next decade. While some banks have started to use AI to collect, find patterns in and classify information, most are still in the early stages. According to a recent survey, only about a third of bank executives said they are using AI technologies such as predictive analytics, recommendation engines and voice recognition.
The AI boom comes at a time when banks are moving forward to personalize their services. For example, banks are looking at customizing their communications to new accounts based on the customer’s initial activity. AI can help to scale banks’ advisory business, which has tradionally been costly to deliver. Consumers seem open to “robo” financial advisors – online services that deliver algorithm-based advice on rebalancing their portfolios to maintain their original investment guidelines. These automated services operate at a cost of less than 100 basis points, compared to 200-300 basis points for traditional brokers. The hope is that AI-based services will give customers the impression that the bank understands their needs, as used to happen when they had more in-person interactions with bankers.
A prominent example of European bank investment involved in AI in just this way is Norwegian digital bank Skandiabanken. As an example, they have acquired a 40% stake in local AI and machine learning company Quantfolio. The investment is part of the bank’s push into robo advisory and risk management services for customers of its private savings division.
To take full advantage of AI, banks will need cross-industry partnerships, such as with retailers and loyalty card providers, to get data that is the basis to generate better customer insights. The key for banks is to deliver AI-based services that get the balance right between machine intelligence and human intervention. Early evidence suggests that there is a strong business case to be made that AI can deliver cost-effective value across European bank operations.
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