For decades, banks and insurers have employed the same relatively static, highly profitable business models. But today they find themselves confronted on all sides by innovators seeking to disrupt their businesses. Crowdfunding, peer-to-peer lenders, mobile payments, bitcoin, robo-advisers – there seems to be no end to the diversity, or to the sky-high valuations, of these “fintech” innovators.
Yet, some might note that they have heard this tune before. The direct banks and “digi-cash” of the 90s captured the imagination of journalists and investors in a similar fashion, but ultimately had little impact. In fact, the financial services industry has been remarkably impervious to past assaults by innovators, partially due to the importance that scale, trust and regulatory know-how have traditionally played in this space.
However, as they say in investing, “past performance is not an indicator of future success” and the same may be true for banks’ and insurers’ record of besting innovators.
There are key areas that are incorporating technology into financial activities to help develop the customer journey including:
Perhaps the biggest way that FinTech is disrupting the finance and banking sector is through customer service. In the past, a good customer service team was vital for any company involved in finance. Anything that involved the handling of money or financial matters required trained staff to be able to help sort out problems and provide assistance to people.
Banking was traditionally something that was done in the non-virtual world. People would go into town to their bank to withdraw money, transfer funds from one place to another, and sort out their finances. You’d speak to a helpful staff member and interact with people in a brick and mortar building. However, these kinds of premises are rapidly becoming redundant. Online banking is getting more and more sophisticated on a daily basis – we can transfer money or pay for goods with just the push of a button.
The investigation and identification of fraud used to be an equal effort from both man and machine. The system would help to track potential fraudulent transactions, but it would be up to the staff who were trained to find fraud to look through all the information and determine if there was fraudulent activity on the account or not.
However, AI is progressing beyond the capacity of the people designing it, and they’re now starting to be able to detect fraud and identify it. The machine can track through the history of the victim, and then calculate and predict the likelihood of fraud based on previous patterns. This can all be done at a much faster speed than a human could, which means that a lot of fraud teams don’t need to be as big as they are, and can instead be cut down to a small handful of individuals.