Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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How Experian Is Using Big Data And Machine Learning To Cut Mortgage Application Times To A Few Days

By Nand Kishor |Email | May 25, 2017 | 4635 Views

Credit reference agency Experian hold around 3.6 petabytes of data from people all over the world. This makes them an authority for banks and other financial institutions who want to know whether we represent a good investment, when we come to them asking for money.

Like all financial services, they are being rapidly changed by waves of technological innovation sweeping through industry - none more so than artificial intelligence and machine learning.

Machine learning is essentially teaching computers to teach themselves - much the same way as humans can - by giving them access to huge amounts of data, rather than having to teach them to do everything ourselves.

I spoke to Experian CIO Barry Libenson about how the business - a pioneer in Big Data-driven analytics - is adapting to meet the challenges and reap the rewards offered by the new generation of cognitive, self-teaching technology, and the ever-growing data streams which power them.

Libenson outlined three driving forces behind Experian's move to be at the vanguard of the "fourth industrial revolution". The first is new and emerging technology.

"There's a movement towards open source technology which is less costly to operate and scales very effectively, so essentially you have a lot more horsepower at your disposal and can operate on much larger datasets.

"Just a few years ago when we did analytics on a dataset it was based on a smaller, representative set of information. Today we don't really reduce the size of the dataset, we do analytics across a terabyte, or petabyte, and that's something we couldn't do before."

Larger datasets obviously give a more accurate picture of whatever they represent, leaving less margin for error. This leads to analytics, simulations and insights which more closely reflect real-world outcomes - such as whether someone will repay a loan.

The second driving force is that it isn't just the size of data which has increased, but its speed as well. Thanks to sensors, mobile phones and Internet of Things (IoT) technology, data is coming in at greater velocity than ever meaning insights can be based on what is happening now, rather than what happened previously.

"A decision based on information that's a month old is not nearly as impactful as a decision based on data that's a day, or hours, old," Libenson says.

Thirdly, there has been a significant change in the way their customers want to consume the information they provide. The growth of software-as-a-service (SaaS), cloud based platforms and API solutions based on open standards has brought about a seismic shift in the way the data trade does business.

Libenson says "It used to be a very simple model, you would deliver a piece of technology to the customer, and they would install it and run it from their location.

"Now the large financial institutions don't want you to dictate to them how they consume information, they want to tell you how they want to consume it, and you have to deliver it to them in that way. So they can say 'hey, Experian, we have a simple question we want to ask and we expect a simple response, and we want it in real-time, and we want to be able to get it 24 hours a day from anywhere in the world.'"

The technological advancements which have changed the way it's customers expect Experian to service them also provide immense opportunities for Experian themselves. Where machine learning excels as a technology for driving change is its potential for automating complex but often mundane and time-consuming calculations at incredible speed, using information from vast and quickly-changing datasets. These type of calculations make up the bulk of what Experian does in its role as a credit reference agency, with data coming from transactional records, marketing databases and public information such as court records.

One potential use which is causing a lot of excitement and is already on the way to becoming a reality is speeding up the traditionally lengthy and labor-intensive process of applying for mortgages.

"Machine learning is absolutely one of the hottest topics right now and something we are embedding into our products, in terms of better decisions and analytics," says Libenson. Read More

Source: Forbes