TM SM ® Trademark(s) of Black Knight IP Holding Company, LLC, or an affiliate.DA0816 © 2016 Black Knight Financial Technology Solutions, LLC. All rights reserved.

The pace of technological change, and how it translates into consumer expectations, is presenting

profound challenges and new opportunities in virtually every business sector. Transformational,

disruptive innovation is changing – or poised to change – entire industries, and mortgage banking is

certainly no exception.

The particular security, compliance and regulatory oversight demands of the industry may make change

in mortgage banking more complex than in other sectors, but the industry is in no way immune to the

transformational forces at work today.

The enormous acceleration of innovation and consumer adoption should come as no surprise. However,

the pace at which this change continues to occur is extraordinary.

Think of the length of time it has taken particular technologies or products to achieve critical mass. If

we consider 50 million users as the point of mass adoption, then it took the airplane nearly 70 years to

achieve critical mass and the telephone almost 40 years. The personal computer took approximately 30

years, and smartphones less than 10. Facebook had 100 million users in just over four years.

The speed of adoption is accelerating, and this time around consumers are driving the need for response

from business, not the other way around. The rise of mobile technology – smartphones, tablets and even

smartwatches – has created an “always-on” level of consumer demand that has never been seen before.

It’s an entirely different world: new technologies, products, and paradigms are borne and adopted, and achieve global critical mass at a pace – and

at levels of penetration – previously unheard of. In business, these changes – which more often than not are coming from outside of a given sector,

rather than from within – are requiring action on the part of established industry players with the agility and speed to match that pace of change.

Today’s Infrastructure in a NextGen WorldThe mortgage industry has not only been historically risk-averse and resistant to change, but also encumbered by an avalanche of regulatory

requirements. Lenders need to be able to work and compete in a next-generation world. It’s a complex environment, where technology is

driving massive change in customer-expectation dynamics. Customers, especially younger consumers that have grown up with heightened

expectations around responsiveness, are driving these expectations, and enterprises are struggling to cope while also adhering to ever-

increasing regulatory and compliance demands.

Since the 1980s – when the average time to process a credit card application, personal loan or home mortgage took 14 days, seven days and 30

days respectively – to today, where decisions are expected instantaneously, expectations for the time to react have compressed dramatically.

Patience is in low supply in this world of instantaneous gratification. Lenders need to be able to react quickly to customer demands while being

able to quickly pivot to deal with potential threats or opportunities on any number of fronts.

Without the proper next-generation infrastructure that facilitates that kind of agility based on data and analytics, lenders are simply not going to

be positioned to address the situation. The fact is, most enterprises have yet to upgrade their systems and processes sufficiently to handle this

new environmental challenge.

Kevin CoopPresident

Black Knight Financial Services Data & Analytics

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TM SM ® Trademark(s) of Black Knight IP Holding Company, LLC, or an affiliate.DA0816 © 2016 Black Knight Financial Technology Solutions, LLC. All rights reserved.

New World, New DataAt Black Knight, we have made significant investments to bring the promise

of big data technologies and the next-generation infrastructure to the

mortgage industry. The way we see it, technological disruption can either

come from within the industry and present an opportunity for industry

leaders, or come from the outside and represent a threat. Regardless, it

will come, one way or another.

What we have done is create an enterprise data hub that turns traditional

database theory on its head. Rather than designing datasets and models

beforehand, we’re leveraging Apache Hadoop framework to create a single

data repository – a data ecosystem if you will – that provides the utmost in

flexibility and customization when it comes to utilizing data on the client end.

We are able to pull in data from any number of sources – from our

mortgage industry performance dataset and our property information

database – the largest in the industry – as well as the client’s own data

sources and any number of third-party databases, and gather it all together

in that unified data ecosystem.

Access to data ecosystems such as ours allows for large-scale data modeling on the back-end by highly skilled technical professionals, whether

those are in-house analytical teams on staff at a given enterprise, or qualified teams brought in to supplement that skill set. It also allows for

incredible efficiencies and newfound opportunities.

Detailed Insight, Identifying OpportunityWhen all of an enterprise’s data is linked within a single ecosystem – for example, loan-level information is linked to property data, as well as

to multiple listing service (MLS), market, or valuation data, etc. – it becomes much easier to maintain all of that data, and leverage it to reduce

risk and avoid missed opportunities. In the case of our clients, they can easily obtain extremely detailed insight into what is going on with a

given borrower or property.

For example, a bank that links its servicing platform to MLS data can know immediately when a home for sale has been listed by a current

customer. The lender can then proactively contact the customer for retention purposes, and offer to finance his or her next home purchase,

with significant advance notice. In fact, lenders can even utilize in-memory analytics to predict the likelihood a borrower will list their home

before the home is actually listed. And, given the makeup of the data ecosystem and the tools to access it, those sort of analytics can be built

on day one. That is light years ahead of the traditional method of first gathering those data sources, then putting them in an Access or SQL

database – or even a spreadsheet – before having a programmer write the analytic, and then maintaining and updating the database on a

consistent and ongoing basis.

Another great example of data supporting customer retention resulted from a challenge posted on Kaggle, the world’s largest community of data

scientists, by Santander Bank. As Santander looked into customer churn, the bank wondered if historical customer data could identify unsatisfied

customers early in the relationship. The data scientists at Black Knight responded to this challenge by building a predictive model based upon our

big data approach to the mortgage industry, employing elements of our data hub, in-memory analytics engines, and advanced machine-learning

algorithms such as extreme gradient boost.

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The idea was that by analyzing anonymized information about thousands of customers, their loans and customer service experiences, as well as

whether or not the customer ultimately stayed with the bank, we could build a model that predicted the likelihood of customers’ future behavior.

The model was an incredible success, and it’s something we’ve been able to incorporate into our offerings for all of our clients.

That’s valuable for a number of reasons, but key among them is the fact that it costs considerably more money to acquire a new customer than it

does to maintain an existing one. Being able to provide clients with predictive analytics, running on top of big data technologies that deliver real-time

insights, gives them the ability to make more data-driven decisions. Even more importantly, this gives our clients actionable intelligence on which to

take proactive, pre-emptive actions to retain customers, which in turn directly improves their bottom line.

These are just two examples of how a next-generation, big data approach to the challenges facing the mortgage industry can help solve

problems, as well as create new opportunities. Every day, we’re unearthing new possibilities, and exploring bold new paths to increasing

efficiencies and bottom-line performance for our clients. The truth is that we’re only just scratching the surface. There are many more ways

big data and the analytics built on top of it can help our clients, and advance the entire industry. From where I’m standing, it’s inspiring to be

part of the beginning of the big data revolution in our industry.

www.BlackKnightDNA.com 866.964.8343

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