Report Will Help Lenders Get More Actionable Data Earlier In The Process

Finicity, a provider of real-time financial data access and insights, announced today the release of its new AssetReady Report that will rapidly identify a borrower’s assets using consumer-permissioned data during a lender’s pre-qualification process.  As a result, lenders will more easily qualify borrowers and generate a higher quality sales funnel for loan officers while enabling a seamless transition into other necessary asset, income, and employment verifications needed in the loan origination process.

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Lenders have the option to receive balances and other data without having to ask for or include consumer SSN or date of birth. Fast, high-value data with less friction on lower probability applicants can provide lenders with better insights on how to strategically move borrowers forward in the application process without asking for detailed verification reports.

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Once leads have been qualified, borrowers can seamlessly permission their data for Finicity’s other digital verification solutions like assets, income, and employment required for the origination process. This single-source solution model for verification optimizes lender workflows to maximize ROI.

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“Now lenders can receive more data earlier in the application process than ever before,” said Steve Smith, Finicity CEO. “This report will provide the opportunity for even more customization and better experiences for borrowers from their first interaction to close.”

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“Getting a verified snapshot of borrower assets during the pre-qualification stage is key to speeding up our business processes,” Bill Cosgrove, President & CEO of Union Home Mortgage, said. “Once a borrower has engaged with our digital pre-qualification solution the stage is set for a seamless transition into Finicity’s full suite of asset, income and employment verification tools.  A single-source solution provider is a great fit for our business model.”

The pre-qualification report includes current account balances and average balances over the previous two and six months, as well as the number of negative balances in the past six months and the most recent negative balance. The report also provides account owner and other account details.

Finicity is working with leading mortgage ecosystem platform providers to simplify lender access to the digital tools they need to improve the origination experience.

Delivering Data Decisions

To increase velocity for deploying predictive models, FICO and Equifax are introducing the Data Decisions Cloud. The new Data Decisions Cloud is an end-to-end data and analytics suite that addresses key needs across risk, marketing, and fraud to enable financial institutions to meet the needs of consumers faster and more precisely than ever before.

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The Data Decisions Cloud integrates the Equifax Ignite platform differentiated data and analytic management with FICO Cloud applications and the FICO Decision Management Suite (DMS), a digital decisioning platform. This broad strategic alignment will enable organizations to easily explore differentiated data, uncover deep new insights, build highly-predictive models and rapidly deploy decisions into production systems across the customer lifecycle. Financial institutions will benefit from an increased pace of innovation for data and decisioning, supported by incredible industry expertise and explainable artificial intelligence (AI).

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“We are energized about this broad partnership between Equifax and FICO. Two industry leaders are joining forces to help financial institutions better meet the needs of consumers and improve business agility,” said Mark W. Begor, CEO of Equifax. “Our partnership will seamlessly integrate Equifax’s differentiated data assets and Ignite platform with FICO’s market-leading cloud based decisioning software and applications.”

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“Our common mission is to empower financial institutions to leverage data-driven decisioning in all their customer interactions,” said William J. Lansing, CEO of FICO. “With this strategic partnership, FICO and Equifax will help organizations operationalize the best data with unparalleled predictive analytics and applied AI, and do so in a streamlined and cost-effective way.”

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The strategic partnership is focused on a connected, end-to-end development and decisioning management platform that allows customers to quickly explore, develop, test, and deploy powerful insights into production systems across the organization. In addition, FICO and Equifax are planning to release three pre-built solutions:

>>A connected system for real-time access to raw and trended data that enables the rapid creation and deployment of new predictive elements and promotes data science collaboration.

>>A Compliance-as-a-Service solution that enables customers of all sizes to support their anti-money-laundering and know your customer obligations across the customer lifecycle.

>>An integrated pre-screen marketing automation solution that develops FCRA-compliant campaigns to acquire and retain customers.

Liza A. Yannon, director of Quantitative Analysis at Key Bank, said, “I’m excited to see that FICO and Equifax listened … and I look forward to seeing how they help us obtain more ready access to data.”

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Automating Loan Workflows With Perfected Data

Capsilon, a provider of digital mortgage solutions, has partnered with Blue Sage, a browser-based, end-to-end mortgage platform, to automate key steps in the loan origination process. 

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As a result of the partnership, Capsilon’s patented document recognition and data extraction technologies have been integrated into the Blue Sage Digital Lending Platform to help mortgage lenders of all sizes drive down origination costs and improve customer satisfaction. The integration with Capsilon is made possible through Blue Sage’s unique application programming interfaces, or APIs, which make interoperability between third-party technology providers completely seamless.

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Capsilon IQ captures and perfects mortgage data from any source, eliminating manual data entry and comparison, and enabling automation with complete, accurate information. The Capsilon IQ platform helps lenders to speed up loan intake and reduce the manual work typically associated with handling inbound documents and data, so they can redeploy staff on more valuable tasks. 

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“We are very excited to partner with Blue Sage, as it represents the cutting edge of today’s mortgage origination technology,” said Sanjeev Malaney, CEO of Capsilon. “Blue Sage and Capsilon also share a common goal—to help drive down origination costs  while helping our mutual customers take on more volume, scale appropriately and create key competitive advantages that drive their business growth.” 

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The Blue Sage Digital Lending Platform is a browser-based, highly scalable solution capable of supporting any mortgage channel, including retail, wholesale and correspondent lines of business. Built, managed and delivered through a cloud environment, Blue Sage can be accessed on any device and handles pricing, underwriting and loan decision-making from the point-of-sale stage all the way to the closing and funding of a loan.  “Capsilon IQ perfectly and seamlessly complements our robust workflow tools and enhances our ability to deliver a truly unique, digital mortgage experience,” said Joe Langner, CEO of Blue Sage. “Not only will Capsilon’s technologies help save our lending clients time and money, they will improve quality and efficiency at every stage of the mortgage lifecycle. We couldn’t be happier to be working together.”

Capsilon Launches Capsilon IQ, Moves From Docs To Data

Capsilon has launched Capsilon IQ platform and Capsilon Data Audit app, and the rebrand of DocVelocity. More than 160 mortgage companies rely on DocVelocity for mission-critical business operations, and 15% of U.S. mortgages touch the system each year. Mortgage companies are under increasing pressure to deliver faster, easier mortgages at a time when margins are at their lowest point in years. Companies who want to compete need to evolve their businesses and transform how they work. To meet this need, the company is expanding the Capsilon platform to harness new technologies that drive this evolution.

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Introducing Capsilon IQ

Capsilon IQ is the platform at the heart of the next-generation mortgage operating model. Formerly known as DocVelocity, Capsilon IQ evolved from enterprise-wide document management to an end-to-end mortgage automation engine. It combines Intelligent Process Automation with patented data recognition and extraction technology to create efficiencies at every stage of the mortgage lifecycle. Capsilon IQ enables users to create intelligent work experiences that make people more productive and existing systems more powerful.

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Capsilon IQ: Document Edition includes the core document management functionality that solves key workforce productivity challenges of managing and manipulating documents. It speeds up loan intake and reduces the manual work associated with handling inbound documents.

Capsilon IQ: Docs & Data Edition includes all of the features of Document Edition, plus a new set of tools to help mortgage companies do more with their loan data. It includes Capsilon Mortgage Data Management, a data repository that companies can use to power their mortgage business. The platform absorbs millions of data points from documents and digital sources, standardizes them into a consolidated record for each loan file. With Mortgage Data Management, companies can ensure data integrity across all business applications.

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Companies can also extend Capsilon IQ with productivity boosting apps that speed up teams and their tasks, solving the biggest pain points that weigh mortgage companies down.

NEW Data Audit App

A major pain point for many of customers is the dreaded ‘stare and compare’ process that frustrates staff and takes up valuable time. Capsilon’s new Data Audit app frees up staff time spent searching for docs and data. It provides a single place to see and compare data across sources, including the LOS and supporting documents. Capsilon Data Audit flags data mismatches for review so staff can instantly spot where supporting documents and data don’t match.

Capsilon Data Audit boosts data integrity and drives productivity gains across functions.

Equifax Unveils New Analytic Dataset

Equifax Inc. has introduced Analytic Dataset, a new analytic tool that provides borrower-level data in an anonymous and non-aggregated format. The dataset provides key information for researchers and modelers such as credit risk scores, geography, debt balances and delinquency status at the loan level for all types of consumer loan obligations and asset classes.

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With this new solution, investors and other market participants have the ability to better model delinquency, default, loss severity and prepayment, as well as the ability to more accurately value securities and understand broader consumer credit trends. Asset Backed Securities (ABS) and Mortgage Backed Securities (MBS) investors, issuers, traders, and ratings agencies researchers can use the tool to analyze and model consumer payment performance across a variety of asset classes such as auto, credit card, mortgage and unsecured personal loans. Also, better modeling may give investors better predictive power to price risk and thus finance consumer debt at the best possible rates.

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“This data is one of the most important advances in consumer modeling and analytics,” said Professor Tomasz Piskorski, Columbia Business School.

Analytic Dataset is created from an unbiased ten percent statistical sample of the U.S. credit population across all geographic boundaries, with historic data starting in 2005. It provides insights into the credit health and payment performance of U.S. consumers over time and across various economic cycles.

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“When businesses or government entities are able to apply segmentation and perform analytics by credit quality or asset class, they can better determine important factors such as how consumers prioritize payments and the impact of behaviors of given loan types on other forms of credit,” said Geoffrey Hickman, Managing Director of Government Credit and Capital Markets at Equifax. “This in turn gives them the ability to drive a deeper level of understanding and improve modeling efforts.”

Analytic Dataset is available now for direct delivery from Equifax. Additionally, the solution can be accessed through 1010data. “1010data is excited to expand our decade-long partnership with Equifax by hosting their new Analytic Dataset. This is the largest and most powerful consumer modeling set across all consumer asset types,” said Perry DeFelice, Director of 1010data. “The combined Equifax/1010data hosted solution eliminates the burdens traditionally associated with managing and analyzing data of this size and complexity by providing direct browser-based access to every data element, at its most granular level, for unlimited analytical flexibility.”

The Analytic Dataset solution, which is available beginning today, enables entities with little or no historical data in multiple consumer asset classes to quickly acquire insights that can materially improve their business prospects.

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Value Your Data

We have heard industry veterans like Roger Gudobba and others say, “It’s all about the data.” The phrase has become so overused that it almost means nothing anymore. However, lenders and vendors alike should listen to this sound advice. Roger was talking about how data can improve the mortgage lending process, and that’s true, but I’m here to say to you that data can improve your marketing process, as well.

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In a White Paper entitled “Put Data First: Why Data Quality in CRM and Marketing Automation are Top Priority” written by RingLead, the author states that whatever your situation may be, you will quickly realize that it all comes back to data, because data is the real value in your CRM and marketing automation platform.

We don’t mean to trivialize the importance of workflows, automated processes and drip nurturing campaigns that these systems offer. These features are one of the primary reasons that organizations invest so much time and money into their implementation and ongoing administration and improvement, but many are rendered utterly useless when they come into contact with dirty data.

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Dirty data has a way of silently infiltrating your organization, creating frustration, inefficiency, and loss of confidence (eg. dismal user adoption) in the systems themselves. It can affect each department and group of stakeholders in a very different way, but unless there is a “State of Our Data” address, the problem is not brought to the forefront of the organization’s collective psyche.

One of the key requirements of a customer and prospect database is to easily segment the records, allowing your organization to interact with one set of contacts differently from others. This can be easy if you have a strict set of values for each field and the input is controlled at the insertion point.

A common requirement is segmentation by job title, but there are simply too many variations on an individual’s job title to try to account for each with a picklist value, so the standard method of insertion is via a regular text field. This creates a pretty big problem for segmentation.

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According to a 2013 Experian QAS survey, 94 percent of businesses believe there is some level of inaccuracy within their CRM systems. When you think about the time, money and focus that is put into CRM, an allowance for inaccurate and useless data is mind boggling.

Think about the time that your organization is wasting sifting through inaccurate or worse, completely useless, data. Inaccurate data leads to:

>>Wasted sales efforts on useless bits of information stored in CRM, leading to discontent and potential abandonment of the CRM system (ie. decreased user adoption)

>>Longer wait times for support while reps are forced to piece together information while on the phone with a customer, leading to decreased customer satisfaction

At Sirius Summit 2013, Jim Ninivaggi, Service Director of Sales Enablement Strategies at Sirius Decisions cited a study that found roughly 30% of an enterprise salesperson’s time is spent doing research on the Internet. If you think about that in the context of an 8 am – 6 pm workday, that means that 35 days per year are spent doing research.

In Data Driven: Profiting from Your Most Important Business Asset, Thomas C. Redman sums up the advantages of data completeness as “A moat around our business [that] gives us a unique competitive advantage.” Nowhere is this more evident than the aspect of data completeness.

Duplicate records in CRM and marketing automation platforms are a familiar aspect of bad data. The errors and frustration that duplicates cause can be felt across most departments at almost every level.

Reports are skewed, the wrong messages are being sent, and quarrels are created over one record that somehow made it into the system twice and was distributed to two different sales reps.

How are duplicate records created? Today’s CRM and marketing automation platforms come equipped with very basic duplicate identification, which is, in almost every case, based on a scan for an exact-match email address.

Many modern, technology-enabled organizations are using more than one software platform to manage their customer and prospect data. It is crucial to keep your data in sync across your email, ERP systems, CRM, marketing automation platform, and more. If your data quality plan is limited to one platform, you’re only solving part of the problem.

It’s important to remember that dirty data can be a big problem, but can be easily solved. Analyze the problem and try to hone in on the areas that are causing the most pain. Then get in touch with a team that has experience in resolving these types of issues.

For example, NexLevel Advisors is focused on companies that are looking to take their business to the next level. NexLevel Advisors assists you in elevating your results. Creating new opportunities, executable strategies, and delivering results creates an environment that promotes continual growth and business value for your company.

We add value through strategic advice specific to your company. Our team has years of experience and have been in your situation and position. These individuals possess in-depth knowledge of your complex product and service offerings, the nuances of your market segment, and the challenges of your product roadmap and lifecycle. We deliver customized differentiation in the marketplace for your organization while producing measurable results.

What this means for your business is that you get customized programs from accomplished executives who offer proven results-oriented solutions specifically created to take your organization to the next level, quicker and more strategically than you could on your own.

NexLevel’s experience has covered multiple industries including: Financial Services, Healthcare, Legal Services and Insurance in delivering marketplace results, with extensive expertise in complex technology oriented products and services. Our customized solutions help you to sell more, more frequently, to more people by clearly establishing your specific value propositions. This is where real world experience, strategy and execution deliver measurable results for your organization.

For over 20 years the advisors of NexLevel have been leading and creating market leaders in business, delivering success after success in taking companies to the next level in revenues and profitability. This vast expertise comes from real world experience in running companies, building organizations and holding the following positions of leadership: CEO, CMO, VP Business Strategy, and Director of Sales & Marketing. Our experience makes the difference in your business.

If your CRM reporting seems “off”, if your marketing campaigns are less than impressive, if your sales team is underperforming, then this is your system flashing the Check Engine light. More often than not, dirty data is the root cause.

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eClosings Are Picking Up Steam

Talk of the Digital Mortgage is everywhere. This is the big push for lenders looking to increase efficiency and cut cost. And vendors are stepping in to help lenders achieve this goal. For example, Visionet Systems, has released an enhanced web services integration layer for CD2UCD, a solution that converts Closing Disclosure (CD) images to Uniform Closing Dataset XML.  Visionet will be exhibiting its  solutions at the National Technology in Mortgage Banking Conference & Expo 2017, an event held by the Mortgage Bankers Association at the Hyatt Regency, Chicago from March 26 to 29.

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CD2UCD addresses situations where the data required to produce the UCD is not available, such as CDs modified at the closing table and loans acquired through correspondent or bulk channels.  The enhanced interface allows lenders, closing agents, document preparation providers and Loan Origination System (LOS) vendors to easily pass a CD document and the required GSE data fields to the CD2UCD service and receive back a fully GSE-compliant UCD.  CD2UCD also fulfills single and bulk requests via secure web portal and file transfer mechanisms.

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“We’ll be discussing our latest enhancements to the CD2UCD platform at MBA Tech 17, and highlighting the important industry problem it addresses,” said Norman Gottschalk, CTO at Visionet. “CD2UCD is the result of two years of investment, collaboration and learning, and we continue to invest in its development.  We look forward to sharing our expertise and experience with our present and future clients and colleagues, and we encourage partnerships and integrations with all industry participants.”

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Visionet’s solutions help lenders, servicers and investors stay compliant with the ever-changing regulatory environment, while staying as efficient and cost-effective as possible.  CD2UCD represents a prime example of how the application of technology can significantly boost back office productivity. Instead of spending countless hours compiling and entering the information required to produce each UCD, our solution is simple and scalable, and drastically reduces the time and resources mortgage firms spend on investor delivery.  Visionet is proud to be the first to bring such a game-changing product to market.

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The Truth About Taxes

Some of you may be familiar with the old adage, you only have to do two things in life, die and pay taxes. While some people might add working out and watching your gluten intake, paying taxes still remains very true. For property taxes to be paid correctly, it is crucial for servicers to set up loans correctly on their system during the boarding process. Not doing so could lead to penalties or even the loss of a property.

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The tax line is a record in a loan servicing system that includes all the data needed to identify when property taxes need to be paid, the amount of payment and what jurisdiction receives the payment.


The following charts represent the monthly number of items and dollar amounts for occurrences of paying the wrong amount of property taxes. The information is based on LERETA national data from prior servicer/lender acquisitions from July 2015 through April 2016.

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One important area to be cognizant of is the tax line, which includes all the data needed to identify when property taxes need to be paid, the amount and the jurisdiction to receive the payment. The line includes:


>> The tax payee code is the unique number assigned to identify the tax collection jurisdiction on the servicing system. This code cross-references to another file that includes the taxing jurisdiction name and mailing information.

>> The tax identification number is the actual number assigned to the property by the taxing jurisdiction and is required when obtaining any information from the taxing jurisdiction about the property.

>> The disbursement amount or last amount paid is the last amount paid to the taxing jurisdiction for this tax line (can be an annual or installment amount) and is updated with the current amount to be paid when new taxes become due.

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>> The due date is the date the servicer assigns to this line to alert them to an upcoming payment requirement (this date is based on business rules and is generally 15 to 30 days before taxes are due to the taxing jurisdiction).

>> The type is a code that identifies the type of taxes due, typically county property taxes, city taxes, school taxes and some non-standard payees, such as sewer taxes, garbage taxes, ground rents, etc. Servicers are required to report escrow payments annually to borrowers, and the tax type is used in preparing tax deductions on the borrower’s income tax return.

>> The term is used to identify the frequency the taxes are being paid, for example a Term of three indicates the annual taxes are being paid every three months, or quarterly (three months x four installments = 12 months).

>> Other codes as determined by the business rules of the servicer.

Servicers cannot rest on their laurels once the tax line is set up on a loan. It is extremely important that tax lines are monitored and maintained to ensure the accuracy of the data due and to any potential changes. Those changes could include new payees or the consolidation of taxing jurisdictions that no longer collects taxes, and how they are now collected by the county or separating taxing jurisdictions (a city decides to collect its own taxes instead of them being included by the county). There could also be changes in the due dates, contact information or tax identification numbers.

Servicers can either take on the responsibility of managing this process or work with a tax service vendor that can offer and facilitate the tax line setup and tax line audit services on behalf of the servicer. Either way, the taxes have to be paid correctly, else penalties will be incurred, which no one wants to happen.

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Bloom’s Taxonomy


Let’s start with describing Bloom’s Taxonomy. In 1956, Benjamin Bloom with collaborators Max Englehart, Edward Furst, Walter Hill, and David Krathwohl established a framework for categorizing educational goals. Generally referred to today as Bloom’s Taxonomy, this framework has been applied by generations of educators at all primary, high school, and collegiate levels.

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The Original Taxonomy (1956)

Here are the authors’ brief explanations of these main categories.

  1. Knowledge: Involves the recall of specifics and universals, the recall of methods and processes, or the recall of a pattern, structure, or setting.
  2. Comprehension: Refers to a type of understanding or apprehension such that the individual knows what is being communicated and can make use of the material or idea being communicated without necessarily relating it to other material or seeing its fullest implications.”
  3. Application: Refers to the use of abstractions in particular and concrete situations.
  4. Analysis: Represents the breakdown of a communication into its constituent elements or parts such that the relative hierarchy of ideas is made clear and/or the relations between ideas expressed are made explicit.
  5. Synthesis: Involves the putting together of elements and parts so as to form a whole.
  6. Evaluation: Engenders judgments about the value of material and methods for given purposes.

Although it received little attention when first published, Bloom’s Taxonomy has since been translated into 22 languages and is one of the most widely applied and most often cited references in education.

The Revised Taxonomy (2001)

One of the basic questions facing educators, whose core mission is to improve thinking, has been where to start. As always, definitions are in order. Before we can make a thing better, we need to know more about what the thing is.

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In 2001 a group of cognitive psychologists, curriculum theorists, instructional researchers, and testing and assessment specialists published a revision to Bloom’s Taxonomy that focuses on a more dynamic classification. The changes occur in three broad categories: terminology, structure, and emphasis.

A. Terminology: Changes in terminology between the two versions are readily apparent. In short, Bloom’s six major categories were changed from noun to verb forms. The use of verbs more accurately describes the cognitive processes by which thinkers encounter and work with knowledge. It is also notable that the top two categories are switched in this revision so that creating (formerly synthesis) occupies the top position

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Bloom’s Taxonomy

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Let’s look at the revised Bloom’s Taxonomy and add some reference to MISMO.

  1. Remembering: Retrieving, recognizing, and recalling relevant knowledge from long-term memory. MISMO: The initiative started with a small group of individuals organizing a list of data elements based on their past experience and interactions, partially influenced by their area of interest or expertise.
  2. Understanding: Constructing meaning from oral, written, and graphic messages through interpreting, exemplifying, classifying, summarizing, inferring, comparing, and explaining. MISMO: The first goal was to define a system or method to attach a label and definition of the data element in the hopes of identifying and eliminating duplicates. Obviously, there was a lot of discussion and different opinions.
  3. Applying: Carrying out or using a procedure through executing or implementing. MISMO: This was the Logical Data Dictionary (LDD. Looking back, it was probably the most significant achievement in exchange of information (data) between two entities where they both had the same definition. Although many feel we are in maintenance mode, the increasing focus on demand for more data-driven processes will continue to be a major initiative going forward.
    • The first 3 steps were building the foundation.
    • The next 3 steps were bringing it all together.
  4. Analyzing: Breaking material into constituent parts, determining how the parts relate to one another and to an overall structure or purpose through differentiating, organizing, and attributing. MISMO: Based on the XML standard at the time and our knowledge and experience, some of the earlier transaction sets were defined as Document Type Definitions (DTD). Specifically, they were created around defined transaction types, like credit, mortgage insurance, etc., independent of each other.
  5. Evaluating: Making judgments based on criteria and standards through checking and critiquing. MISMO: Next, the development of the schema and business reference model was also very significant. However, to the non-technical person, the visual of this model can be overwhelming. The need to get the business side involved is paramount to the continuing success of the organization and the industry.
  6. Creating: Putting elements together to form a coherent or functional whole; reorganizing elements into a new pattern or structure through generating, planning, or producing. MISMO: The development of the Logical Data Dictionary and the Business Reference Model by all the volunteer contributors from all areas of the industry was unprecedented. Kudos to all!

B. Structural: Structural changes to the taxonomy are well-considered and provide an easy-to-grasp understanding of the structure’s logical underpinnings. Bloom’s original cognitive taxonomy was a one-dimensional form. The Revised Bloom’s Taxonomy, with the addition of products, takes the form of a two-dimensional table. One of the dimensions identifies The Knowledge Dimension (or the kind of knowledge to be learned) while the second identifies The Cognitive Process Dimension (or the process used to learn).

C. Emphasis: Emphasis is the final category of changes. Bloom himself came to understand that his taxonomy was being used by many groups and organizations that never considered an audience for his original publication. In contrast, the revised version of the taxonomy is deliberately intended for a broader audience. Certainly, the same could be said for MISMO.

People around the world are familiar with the original Bloom’s Taxonomy and are not necessarily quick to embrace its change. After all, change is difficult for most people. The mortgage industry is no exception.

The goals for MISMO are threefold. 1) Increase adoption. 2) Increase membership, especially in the lender community. 3) Be cognizant of new opportunities to further the advancement of the standard.

My goal always is to present something that you might not have known about in the hopes that it will spur you to think differently. So, why do I bring this up? Are you focused on what MISMO is doing right now? Maybe you are and maybe you’re not, but if you’re not, you should be. Data standardization and the industrywide acceptance of that data standard is absolutely necessary for the mortgage industry to advance. Change may not be comfortable, but you can’t have true advancement without embracing change.

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Yes, Innovation Really Matters


I say it a lot because I believe it: Those companies that innovator will be tomorrow’s industry leaders. For example, RealtyTrac unveiled a new multi-sourced national property database named the ATTOM Data Warehouse that will be curated by ATTOM Data Solutions, a newly created parent company operating a rapidly expanding property data licensing business along with existing consumer websites.

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The new ATTOM Data Warehouse features enhanced and standardized data for more than 150 million U.S. property parcels — representing an expanded footprint that covers 99 percent of the U.S. population. The enhanced data warehouse is more than 9 terabytes and contains 13.9 billion rows of data and more than 6,000 discrete data attributes. Data available includes current and historical property tax assessor information, deed, mortgage, foreclosure, environmental risk, natural hazard, health hazard, neighborhood characteristics, and other property characteristics — all mapped to a unique ATTOM ID for each property.

“The new ATTOM ID is an innovation long-overdue in the real estate data industry, linking all property-centric data from myriad sources to one unique parcel identifier, and it’s just one example of how the new ATTOM Data Warehouse creates value for our customers and elevates our industry” said Rob Barber, CEO at ATTOM Data Solutions. “Under the new ATTOM Data Solutions brand, our mission as a company will continue to be increasing real estate transparency for businesses and consumers. That mission will be carried out in a variety of venues, including bulk file licenses, APIs and customized reports, along with our increasingly popular consumer websites.”

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The ATTOM Data Warehouse fuels predictive analytics and machine learning developed by Audantic, a Seattle-based company that provides top-tier real estate investors — those typically purchasing about 100 homes a year — with marketing lists of homeowners likely to sell.

“We’ve tried to source national property data from many places, and the ATTOM Data Warehouse is head and shoulders above the rest,” said Franklin Sarkett, CTO of Audantic, which has grown from just two clients in 2014 to now having clients in more than 200 counties. “Without the data we wouldn’t have a business.”

ATTOM’s high-quality data that is easy to ingest has been key for Audantic’s meteoric growth and 95 percent client retention rate, according to Sarkett, who previously worked as a data scientist at Facebook.

“When we’re creating machine learning models, the better the data is, the more predictive the result is. If we’re feeding in bad data or inconsistent data or incomplete data … when we go to do the actual marketing it doesn’t work,” he said, noting the ATTOM data fed through Audantic’s predictive models have produced improved results for clients. “We can eliminate 80 percent of the population and we can double or triple their results.”

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