Fintech UX

The imminent world of artificially intelligent everything

The imminent world of artificially intelligent everything. Pictured at IBM Research - Tokyo is IBM Researcher Risa Nishiyama with SoftBank's Pepper robot using Watson in a demonstration environment. (Credit: Feature Photo Services)
Written by Chris Skinner

The future of artificial intelligence looks interesting. Chris Skinner explores the potential of artificially intelligent everything.

There’s lots and lots of chat about AI (artificial intelligence) in banking using deep data analytics to augment our financial lifestyles. This is nothing new. After all, Spielberg made a film about it way back in 2001. What’s new is the developments in AI we’re seeing from firms such as Google and IBM. IBM’s Watson became famous for winning on Jeopardy! four years ago …

Watson (named after IBM’s founder, Thomas J Watson) is a cognitive technology that processes information more like a human than a computer. The system is based on an IBM supercomputer that combines AI with sophisticated analytical software to provide a ‘question answering’ machine.

The Watson supercomputer processes at a rate of 80 teraflops (a trillion floating-point operations per second). To replicate (or surpass) a high-functioning human’s ability to answer questions, Watson accesses 90 servers with a combined data store of over 200 million pages of information, which it processes against six million logic rules. The device and its data are self-contained in a space that could accommodate 10 refrigerators.

Watson's key components
  • Apache UIMA (Unstructured Information Management Architecture) frameworks, infrastructure and other elements required for the analysis of unstructured data.
  • Apache’s Hadoop – a free Java-based programming framework that supports the processing of large data sets in a distributed computing environment.
  • SUSE Enterprise Linux Server 11, the fastest available Power7 processor operating system.
  • 2,880 processor cores.
  • 15 terabytes of ram.
  • 500 gigabytes of preprocessed information.
  • IBM’s DeepQA software, designed for information retrieval that incorporates natural language processing and machine learning.

Here’s how it works:

Google, meanwhile, is using DeepMind, a London-based company the search giant acquired in early 2014, to create its own AI program. DeepMind is now doing some amazing things, including creating a program that can beat humans at video games …

… and another that’s worked out what a cat is just by analyzing YouTube videos …

The incredibly brainy Demis Hassabis, CEO of DeepMind Technologies, explains more about the implications of AI in this 16-minute lecture …

… and, if you’re really interested, here’s a 90-minute lecture from Stanford that tells you all you need to know about deep learning from big data analytics.

There are several other AI product developments out there, including Microsoft’s Project Adam (Active Directory Application Mode), Facebook’s Open Sourced Deep Learning tools, Apple’s evolution of Siri and the iOS recognition systems, and Amazon’s Machine Learning Service. These are just the big guys. There are also hundreds of small guys doing neat stuff out there, too.

So, what’s the point of all this AI development? Well, in banking, it’s quite important, because the technology giants are basically training enormous networks of machines to identify faces in photos, recognize the spoken word, and instantly translate conversations from one language to another. This means that not only can people talk with their bank when their bank is a machine, but the bank that’s a machine can also immediately recognize the correct versus the fraudulent transaction.

I’ve already talked about this a while ago, when PayPal used deep learning to track fraudulent transactions, but there are many other applications of AI for banking. For example, several of the new payday lenders and credit firms are using real-time credit scoring analytics to calculate the credit worthiness of applicants. Equally, deep data analytics for marketing (effectiveness of campaigns), trading (to build predictive models of prices, volatilities, and so on), portfolio management (as a source of alpha), and risk management (to obtain better risk estimates) are all areas developing fast.

I was impressed with UBS earlier this year, who proudly told me that it runs deep data analytics combined with machine learning non-stop on its clients’ portfolios of investment, to better advise each customer with specific and personalized services every day.

Equally, I was intrigued to hear DBS talking about using IBM’s Watson earlier this year. Similar to UBS, DBS is using deep data analytics to improve customer service and advice. Instead of spending more than two hours each day poring through market reports, DBS’ relationship managers use the time to meet with clients instead, armed with information from those reports distilled by supercomputer IBM Watson.

Certainly, we’re going to see more and more use of AI for everything from simpler user interfaces, improved customer experiences, automated fraud detection and massively personalized, proactive and predictive services. But where does all of this take us long-term?

Perhaps ANZ’s partnership with IBM’s Watson can tell us …

– This article is reproduced with kind permission. Some minor changes have been made to reflect BankNXT style considerations. You can read the original article here. Main image: IBM researcher Risa Nishiyama with SoftBank’s Pepper robot using Watson. (Credit: Feature Photo Services)

About the author

Chris Skinner

Chris Skinner is an independent commentator on the financial markets through the Finanser, and chair of the European networking forum the Financial Services Club, which he founded in 2004. He is an author of numerous books covering everything from European regulations in banking through to the credit crisis, to the future of banking.

Leave a Comment