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Artificial Intelligence News from 15 September 2017 - AI's Threat to Traditional Banking Models (by Charlie Muirhead)

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Artificial intelligence, machine learning, and the Internet of Things are now defining the future of our economy.

The effect of these technologies, made possible by the colossal volumes of data we are able to collect and process, has been described as the “fourth industrial revolution”.

There are hundreds of ways businesses can apply AI to improve their customer service, identify new products, reduce risk, and increase efficiency. The World Economic Forum has described it as a “seismic shift”, and predicts that the big four tech companies of Google, Apple, Facebook, and Amazon could overturn existing industries, starting with banking.

This is not as unreasonable as it might appear.

Finance is an industry built on data analytics. Under Chatham House rules, experts at our recent CogX conference noted AI can directly replace actuaries and in-house data analysts.

Finance is an industry built on data analytics. Under Chatham House rules, experts at our recent CogX conference noted AI can directly replace actuaries and in-house data analysts.

How many banks are becoming reliant on outsourcing advanced data science to elite tech companies? How many would have the firepower to compete if these companies decided to deal directly with customers?

Not only are these tech giants realising that they have the data science capabilities to perform advanced financial analysis, but they know what they can do with their wealth of additional customer insight.

The businesses built on understanding every aspect of their users’ lives can create a new universe of perfectly personalised products, while finally stitching in a missing part of their model: financial decisions.

Banks need to realise that their traditional protection of high customer switching costs will no longer apply when their competitors are not other banks. To survive, banks will shift seriously now into data science.

The time for experimenting with existing functions through fintech partnerships has passed. Today’s mission is to overhaul legacy infrastructure and practices to bring the entire sector up to speed.

Fintechs’ investment in machine learning and data analytics is increasing, but only a small part of this is being driven by established banks, and it still pales in comparison to the investment among tech giants.

These same fintechs are now able to go after profitable niche industry sectors, such as lending to specific groups, democratising trading, or currency exchange, without building the legacy infrastructure of big banks.

This could leave banks squeezed by the narrow focus and low-cost base of challenger fintechs on one side, and the deep data reserves and advanced machine learning capabilities of big tech on the other.

However, banks still remain uniquely well-placed to handle large-scale transactions and maintain a reputation for data protection and discretion.

Regulation remains significant, the public is mindful of the potential for monopolies, and tech chief executives will think twice about the impact on their culture and reputation before they move into banking.

Instead, we are likely to see an evolution in the banking sectors, with banks increasing their investment in fintech startups, and investing in artificial intelligence capabilities to supplement their partnerships with big tech companies.

The banking industry is not insulated from technological disruption. But this threat is more of a call to action than a funeral bell.

Banks need to look beyond the business model and competitor set they have grown accustomed to over the last century. In the new world, data is available from sources far beyond traditional financial metrics and can be analysed with advanced algorithms to unlock a new world of insights.

This means embedding machine learning into the core of the business, rather than seeing it as an efficiency that can be added to existing practices.

AI will revolutionise all banking functions, from chatbots in customer support and predictive personal budgeting, to robo-advisory and market-tracking services. Incumbent banks remain best-placed to benefit from these changes, provided they can overcome the substantial hurdles of tradition and culture. If the City can learn from Tech City by moving fast and breaking things, then Silicon Valley’s giants will be kept at the Bay.

Read the full article at http://www.cityam.com/272000/ais-threat-traditional-banking-models

For more AI News Podcasts please visit https://www.artificial-intelligence.blog/podcast

  continue reading

4 episodes

Artwork
iconShare
 

Archived series ("Inactive feed" status)

When? This feed was archived on June 08, 2020 04:10 (4+ y ago). Last successful fetch was on April 03, 2020 04:08 (4+ y ago)

Why? Inactive feed status. Our servers were unable to retrieve a valid podcast feed for a sustained period.

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage episode 187098419 series 1433017
Content provided by Steve Digital. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Steve Digital or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Artificial intelligence, machine learning, and the Internet of Things are now defining the future of our economy.

The effect of these technologies, made possible by the colossal volumes of data we are able to collect and process, has been described as the “fourth industrial revolution”.

There are hundreds of ways businesses can apply AI to improve their customer service, identify new products, reduce risk, and increase efficiency. The World Economic Forum has described it as a “seismic shift”, and predicts that the big four tech companies of Google, Apple, Facebook, and Amazon could overturn existing industries, starting with banking.

This is not as unreasonable as it might appear.

Finance is an industry built on data analytics. Under Chatham House rules, experts at our recent CogX conference noted AI can directly replace actuaries and in-house data analysts.

Finance is an industry built on data analytics. Under Chatham House rules, experts at our recent CogX conference noted AI can directly replace actuaries and in-house data analysts.

How many banks are becoming reliant on outsourcing advanced data science to elite tech companies? How many would have the firepower to compete if these companies decided to deal directly with customers?

Not only are these tech giants realising that they have the data science capabilities to perform advanced financial analysis, but they know what they can do with their wealth of additional customer insight.

The businesses built on understanding every aspect of their users’ lives can create a new universe of perfectly personalised products, while finally stitching in a missing part of their model: financial decisions.

Banks need to realise that their traditional protection of high customer switching costs will no longer apply when their competitors are not other banks. To survive, banks will shift seriously now into data science.

The time for experimenting with existing functions through fintech partnerships has passed. Today’s mission is to overhaul legacy infrastructure and practices to bring the entire sector up to speed.

Fintechs’ investment in machine learning and data analytics is increasing, but only a small part of this is being driven by established banks, and it still pales in comparison to the investment among tech giants.

These same fintechs are now able to go after profitable niche industry sectors, such as lending to specific groups, democratising trading, or currency exchange, without building the legacy infrastructure of big banks.

This could leave banks squeezed by the narrow focus and low-cost base of challenger fintechs on one side, and the deep data reserves and advanced machine learning capabilities of big tech on the other.

However, banks still remain uniquely well-placed to handle large-scale transactions and maintain a reputation for data protection and discretion.

Regulation remains significant, the public is mindful of the potential for monopolies, and tech chief executives will think twice about the impact on their culture and reputation before they move into banking.

Instead, we are likely to see an evolution in the banking sectors, with banks increasing their investment in fintech startups, and investing in artificial intelligence capabilities to supplement their partnerships with big tech companies.

The banking industry is not insulated from technological disruption. But this threat is more of a call to action than a funeral bell.

Banks need to look beyond the business model and competitor set they have grown accustomed to over the last century. In the new world, data is available from sources far beyond traditional financial metrics and can be analysed with advanced algorithms to unlock a new world of insights.

This means embedding machine learning into the core of the business, rather than seeing it as an efficiency that can be added to existing practices.

AI will revolutionise all banking functions, from chatbots in customer support and predictive personal budgeting, to robo-advisory and market-tracking services. Incumbent banks remain best-placed to benefit from these changes, provided they can overcome the substantial hurdles of tradition and culture. If the City can learn from Tech City by moving fast and breaking things, then Silicon Valley’s giants will be kept at the Bay.

Read the full article at http://www.cityam.com/272000/ais-threat-traditional-banking-models

For more AI News Podcasts please visit https://www.artificial-intelligence.blog/podcast

  continue reading

4 episodes

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