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Executive Conversation: Scott Slifer and Phani Nagarjuna on how analytics can improve the mortgage and lending process

Sutherland Mortgage Services unlocks the treasure in customer data



Executive Conversations is a HousingWire web series that profiles powerful people in the financial industry, highlighting the operations and the people that make this sector tick. In the latest installment, we sit down with two executives from Sutherland Mortgage Services: Scott Slifer, CMB, senior vice president and global head of mortgage industry vertical, and Phani Nagarjuna, global head of Sutherland analytics and the CEO of Nuevora. 

Q. What are the Business Drivers for Predictive Analytics in Financial Services?

Scott SliferScott Slifer:

At Sutherland, we strive to look at analytics differently than what has traditionally been done in the past. The typical data elements are still important; mortgage amount, number of liens, etc., but we now have access to new information that when used appropriately, can really set the customer experience apart.

Phani Nagarjuna:

To that end, some of the key business drivers for predictive analytics in financial services now include customer centricity, risk management, portfolio and capital management, marketing effectiveness and insurance. 

Q. What is the competitive advantage for financial institutions that use customer data and analytics?

Scott Slifer:

There is a wealth of proprietary information about your customer’s needs and desires locked up in your customer data. The institution that can unlock this treasure trove and put it into the hands of their marketing, origination, and customer service teams will have a competitive advantage. This represents the difference between an institution with a proactive, forward-leaning stance in the marketplace and an institution that is reactive and always on the defensive.

Phani NagarjunaPhani Nagarjuna:

A data-driven institution will have predictive behavioral technologies (models and infrastructure) in place that enable them to think and plan strategically while acting on specific tactical or operational opportunities.

Strategically, the financial institution that has done its homework will have a behavioral and value driven customer segmentation schema in-place and socialized across the organization.  They will understand the strategic levers of each customer micro-segment with respect to revenue, margin, competitive risk and lifetime value, and they will have a plan in place to anticipate, monitor and drive positive migration across those customer segments as their customers move through their lifecycle.

From a tactical standpoint, specific communication objectives will be developed as part of the customer management strategy.  In a data-driven institution, these objectives will be driven by prescriptive behavioral models enabling optimal targeting of messages around each phase of the customer lifecycle: acquisition and onboarding, servicing, next-best product, client/portfolio retention and reactivation.

If you are not driving each of these activities through objective data-driven predictive insights, then you have an opportunity to add significant value to your business simply by mining your own customer data – and organizations such as Sutherland and Nuevora can help.

Q. What type of data does Sutherland provide in the mortgage space?

Scott Slifer:

We bring a unique set of data assets to our solutions including macroeconomic, demographic, and other third-party/public data.  However, it is important to note that many institutions have not fully unlocked the wealth of information within their own organization.  We have tremendous experience working with institutions to more effectively source, clean, and extract value from their internal data.  In our experience this cannot be understated; it pays to take a fresh look at internal data assets.

Phani Nagarjuna:

The level of detail we can extract is pretty amazing. Based on external and publicly available data such as unemployment details, commodity prices, retail sales, bureau of labor statistics data, etc., Sutherland has developed and maintains a proprietary set of US Zip Code level market data, including indicators for economic health, knowledge gap, assets, realty, income, job stability, joblessness and wealth.

Q. When it comes to marketing and customer acquisition, where do you think mortgage bankers need the most help?

Scott Slifer:

The key areas where lenders could focus include modeling existing profitable customer, referrals from current customers, needs-based marketing and multi-channel insight.

While Sutherland is justifiably proud of the quality of our analytic solutions, we also focus on the ‘last mile’ of big data and making those solutions consumable by the institution.  Too often, a solid analytic solution is developed – only to fail in its roll-out and deployment.

Phani Nagarjuna:

Personalized solution offerings is a big opportunity play for mortgage bankers in effectively marketing and acquiring the right customers. While one-to-one marketing has been talked about quite a bit in the industry, what has not been practiced yet is predicting consumer lifecycle changes and offering up solutions proactively to help mitigate attrition and drive up customer life time value. There are so much publicly available data that when intelligently mined using machine learning based prescriptive analytics it can lend insights into consumers’ ability to pay and their tolerance to risk leading to identification of potential targets inside and outside the bank.

When your customer acquisition and marketing strategies are underpinned by these powerful insights then you start building a sustainable competitive advantage.

Q. How does Sutherland help mortgage bankers design effective customer management strategies?

Scott Slifer:

Different institutions will have different needs, and we provide specific and customized analytic solutions for each client.  In particular, Sutherland understands the customer lifecycle and has experience working with clients across that entire journey.

Lenders looking for a strategic partner will find Sutherland to be an expert at developing foundational data driven solutions around topics like Customer Segmentation, Lifetime Value, or Drivers of Satisfaction and Loyalty.

Mortgage bankers that have these topics well in-hand will find Sutherland ready to move forward with point-solutions within the lifecycle like acquisition, next-best-product and client/portfolio retention.

The combined efforts of Sutherland and Nuevora can provide lenders with better customer engagement strategies, such as descriptive analytics, reporting, predictive and prescriptive analytics.

Q. How can your predictive analytic solutions assist lenders and investors with fraud and risk management?

Phani Nagarjuna:

There are several points of leverage here. Many organizations will have one or two experts that are subject matter experts for fraud risk. The challenge to relying upon this resource is that it is not institutionalized and does not scale. These people can leave or retire, or the workload may not match the resource.  An expert can be institutionalized and extended by incorporating their knowledge into sophisticated data-driven tools.  We have experience doing this.

Fraud schemes can be fluid, often changing or evolving over short periods of time.  Sutherland’s analytic solutions may be deployed on a platform that enables rapid response to changing conditions – shortening your exposure to having a sub-optimal solution in the field.

Fraud solutions are work-flow based.  We work with our clients to ensure that the analytic models we develop are deployed in a triage-like fashion within the workflow so that we enable rapid processing for low-risk applications and appropriately flag the smaller number of higher-risk apps for increased review.

We also provide cloud-based analytics, providing real-time insights/dashboards into the Client APIs, and centralized analytics resources, which improve processes, standards and innovation – long term.

Ultimately, our focus to financial services is more of ‘How to Institutionalize Analytics Monetization’ rather than which analytics or data points to use.

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