Smart Lending Strategies

Eric Kujala, Enterprise Sales Manager at Capsilon Corporation, which provides comprehensive cloud-based document and data management solutions for the mortgage industry, recently joined the PROGRESS in Lending Association Executive Team. He is a real visionary individual with strong feelings about how the industry can and should improve going forward. Capsilon did a recent study that found that 70 percent of mortgage lenders report that they expect total loan product costs to continue to rise in 2017. So, how do lenders embrace smart automation to stop this trend? Here’s what Eric told us:

Q: How did you get started in the mortgage industry?

ERIC KUJALA: I joined Flagstar Bank as a loan officer in 2002 and became a VP of the Direct Lending branch soon afterwards. We took our branch paperless in 2005 using shared drives, and that was my first experience implementing technology to improve the loan production process. Flagstar then adopted the DocVelocity document and data management platform to speed loan production, and in 2008 I was asked to drive implementations of DocVelocity at our wholesale lender customers. I loved introducing DocVelocity to Flagstar’s wholesale lenders because I’d already experienced first-hand how the technology optimized our workflow at my branch, and knew how thrilled our wholesale lenders would be. With DocVelocity, our average days to close fell from 33 days to 17 days.

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In 2013 I joined Capsilon Corporation, the makers of DocVelocity, as one of its first enterprise sales executives because I really believed in the technology and knew it could solve many of the problems the mortgage industry faces in regards to optimizing loan production while ensuring quality. Since then, I’ve been helping large mortgage companies speed loan production, improve the overall quality of loans, and reduce loan production costs with Capsilon solutions. And, 10 years later, I’m happy to report that Flagstar Bank is still a Capsilon customer! I’m especially proud that our earliest customers still rely on our products today.

Q: You mentioned loan quality a couple times. The industry has been discussing loan quality for years. Is this still an issue?

ERIC KUJALA: You’re right. Most lenders have been focusing on loan quality for years, but few have examined their entire operations to understand how they can improve data integrity. Despite the availability of technology solutions that can greatly increase a lender’s ability to ensure the integrity of the data used to make underwriting or purchase decisions, many lenders have been reluctant to take advantage of this technology. Instead, they rely on humans to “stare and compare” across documents to verifying loan information for accuracy and completeness. This reliance on labor is time-consuming, costly, and error-prone. Lenders who rely on this approach are plagued with inaccurate, inconsistent, or incomplete data that increases compliance risk. Many lenders then throw more labor at the problem.

The approach is changing because lenders now realize that the only way to ensure loan quality and achieve compliance in a cost-effective way is by leveraging technology. I’m now seeing lenders, including most of my customers, moving quality control to the front of the loan process by leveraging technology that extracts loan data from the appropriate documents as they come in, and using this extracted data for automated review and analysis of the loan file as soon as possible.

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Data extraction technology makes it easy to compare data in the system with data on the original documents, and spot anything that requires additional validation early in the process. I believe this trend of using technology to ensure data integrity is positive for the entire mortgage industry because technology can help lenders improve the consistency and quality of loan information throughout the lifecycle of a loan, while reducing the cost of validating loan data.

Q: What is the “hot topic” that’s top-of-mind with lenders today?

ERIC KUJALA: Once lenders had time to adjust to the new way of doing business under TRID, and began to see the negative effect that lengthening close times had on borrower satisfaction, industry conversation shifted to the customer experience. I think this conversation really began to take off with Quicken Loans’ introduction of Rocket Mortgage in late 2015, and it was amplified along with Quicken Loans’ seemingly ubiquitous Rocket Mortgage marketing campaign beginning with their 2016 Super Bowl ad. Rocket Mortgage became the catalyst that forced lenders to evaluate their digital strategies, and the hot topic of conversation shifted from regulation to the innovative technologies required to enable a digital mortgage experience.

Today, automating the borrower application experience and/or the closing process are central to the digital mortgage definition. But what I hear from many of my customers is that this definition is much too narrow. My customers think the definition needs to be expanded to include the automation of steps throughout the entire mortgage manufacturing process, from loan setup to underwriting to post-close audit. Automating these production steps will accelerate origination and contribute to the exceptional customer experience borrowers have come to expect from technology-enabled financial transactions. What good is a great front-end experience if the experience with the rest of the process falls short?

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We conducted a survey at last year’s Mortgage Bankers Association’s Annual Convention and Expo that probed this very topic. The results of the survey, which polled more than 100 executives from leading mortgage companies, indicate that lenders are already expanding the definition of the digital mortgage to include key “back-end” steps in the mortgage manufacturing process.

In that survey, the lenders were asked if automating the consumer experience during the application process or automating key steps in the loan production process is most important to their companies. 45 percent of the respondents stated that automating key steps in their company’s loan production process is most important, 37 percent stated that automating both the consumer experience and their loan production processes are equally important, and only 15 percent stated that automating the consumer experience during the application process is most important.

Those results tell me that the industry realizes that in order to accelerate loan origination while delivering an exceptional customer experience, key steps throughout the entire loan manufacturing process must be automated, not just the application step.

Q: Where do you see mortgage technology headed?

ERIC KUJALA: I really believe the future is in automation, and I’m really excited about some of the newer technologies that are enabling the automation of the mortgage process. I’m hearing a lot about robotic process automation, machine learning, and other advanced technologies that some lenders are already adopting in small ways.

Today, our industry is too reliant on manual labor. It’s one of the reasons that loan production costs are reaching all-time highs, and personnel expenses represent roughly 2/3 of total per-loan production costs. The entire loan process is a series of checks and re-checks that require manual labor. And with the increased regulatory scrutiny of the past several years, many lenders have hired additional personnel to ensure loan integrity, further increasing loan production costs.

This approach, with its reliance on labor, is not sustainable. Lenders need to adopt automation technology to speed loan production and decrease loan production costs. Lenders that leverage this technology will gain a huge competitive advantage.

Q: Where do you think this automation technology will have the most impact?

ERIC KUJALA: Automation technology is the key to dramatically reducing loan production costs, and every step in the process can benefit from intelligent automation. Let’s take a look at a critical step – underwriting. Today, underwriters rely on checklists to evaluate loans. The process is slow and error-prone, and critical calculations often are done manually, where errors can be costly. Using automation technology, checklists are completed in a consistent manner, and the technology flags only those checklist items that don’t “pass” and require manual review.

Using automated data extraction (ADE) technology, underwriters are able to complete checklists in seconds, cutting the time it takes to evaluate loan files by up to 80 percent. ADE technology automatically extracts critical data from loan documents, compares values across documents in a fraction of a second, runs the data through pre-defined rules engine, performs calculations, and provides alerts on any values that fall outside of established parameters or tolerances.

This exception-based model eliminates the costly and time-consuming “stare and compare” approach to verifying data across several documents, and reduces the multiple touches used today to ensure data integrity. This allows the underwriter to focus on loans that require more careful scrutiny, such as loans with non-occupant co-borrowers, loans on investment properties, loans with borrower self-reported income, and other loans with unique characteristics.

Automation also ensures that calculations are done quickly and correctly. Without automation technology, underwriters must manually enter data into a spreadsheet, a loan origination system (LOS), or some other system to perform the numerous financial calculations used in the credit process. And mistakes made while keying data into evaluation tools could result in faulty underwriting decisions that might negatively affect a lender’s ability to sell loans to investors or, even worse, lead to loan buy-backs. With automation, underwriters save time and eliminate errors with technology that performs required calculations in a standardized, repeatable way—something auditors require.

As I mentioned, every step in the mortgage production process can benefit from automation technology. Today, most functions are guided by checklists, and each function checks and rechecks what the previous function has already checked! Most of the items on these checklists can be reviewed and validated with automation technology, dramatically increasing the velocity of loan production.

Q: What can we expect to see from Capsilon?

ERIC KUJALA: I said earlier that the industry really needs to transition from a labor-centric process to a technology-driven one, similar to a digital factory. Capsilon is fully committed to delivering the technology that will power this modern digital mortgage factory. Technology that transforms the speed, user experience, and economics of the mortgage process.

At the heart of the mortgage process are documents and data. And document and data management is in Capsilon’s DNA. Our DocVelocity platform has been the leading cloud-based enterprise mortgage document and data management platform for more than a decade.

We’re building on this heritage and leveraging our patented document recognition and data extraction engines, and the power of the cloud, to turn volumes of mortgage documents into intelligent, searchable digital assets necessary to convert the slow, inefficient mortgage process into a high-velocity digital mortgage factory. We use intelligent process automation to eliminate up to 80 percent of the labor involved at each step of the mortgage production process.

Capsilon is building the digital mortgage platform the industry needs, and I’m super-excited to deliver the technology that will power my customers’ digital mortgage factories, increasing the velocity of loan production while slashing loan production costs.

Industry Predictions

Eric Kujala thinks:

1.) The mortgage production process will transition from the 80% manual (20% automated) process it is today to an 80% automated process within 5 years.

2.) There will be new entrants to the mortgage lending space who will be technology-focused and will forever change the way mortgage transactions are handled.

3.) The broker comeback will continue.

Insider Profile

Eric Kujala started at Flagstar Bank as a home loan advisor in 2002 with responsibility for originating new residential mortgage business for Flagstar Bank’s Direct Lending department. In 2003, he was named team leader in the department, and in 2004 he was appointed assistant vice president, responsible for the entire Direct Lending sales team. He was promoted to vice president in 2006. In 2008, he joined DocVelocity, the flagship product of Paperless Office Solutions, Inc., a wholly owned subsidiary of Flagstar Bancorp, where in 2013 Flagstar Bank sold the subsidiary to its long term partner Capsilon Corporation where he is currently their Enterprise Sales Manager. Eric has been an integral part of the Capsilon team which in 2016 led to Capsilon’s partnership with Francisco Partners, a growth equity firm in San Francisco, CA.

Increase Efficiencies

The servicer’s imperative post-foreclosure is to liquidate the property and seek recovery under whatever guaranty may exist for the loan. Given the complexity and inherent risk of these processes, servicers have traditionally engaged expert, third-party providers to assist with hazard insurance claims and investor claims. Historically, different companies have specialized in these discrete claim types, so servicers have gravitated toward the leading providers of those services to meet their specific needs.

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However, the “long tail” end of the mortgage crises has reached the areas of servicers responsible for conveyance and investor claims processing, causing volumes to spike and surfacing more comprehensive needs. So, what happens when a servicer needs assistance processing both hazard and investor claims? Must they partner with multiple providers? A few years ago, the answer might have been, “yes.” However, by bundling hazard and investor claims services, servicers can save time and money while producing better outcomes. This “collateral loss mitigation” approach recognizes the interconnectedness of processes and commonality of data elements in post-foreclosure mortgage loan servicing. By dual tracking work, compiling claims in advance, and leveraging insights gained from data processing in hazard claims, curtaiments (meeting timeframes) are minimized while recoveries for servicers are maximized.

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When a servicer engages a third-party provider for collateral loss mitigation, the investor claim begins while the hazard claim is still in process. During the resolution of the hazard claim, the third-party provider takes a proactive approach and begins work on the investor claim due to the similarities of the documents and the requests that are made when filing a hazard claim. In addition to ensuring consistency of process throughout, this approach enables the field services vendors to more quickly complete any repairs necessary to effectuate conveyance condition (“ICC”) for the property.

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We have seen a significant reduction in processing time when hazard claims adjustment and investor claims management processes are combined. In some cases, as many as ten days are shaved off the overall claims process. This streamlined approach not only reduces processing time for both types of claims, but it also potentially saves servicers hundreds of dollars per property. If a projection is done over the servicer’s portfolio, the savings could be significant with this proactive approach.

Servicers dealing with increased volumes of conveyances and investor claims should be looking to reduce risk and decrease timeframes. An effective third-party provider can assist by offering both hazard claims and investor claims processing as bundled services under the umbrella of collateral loss mitigation. This innovative approach, which combines economies of scale and commonality of data, assists in minimizing the risk of curtailments while ensuring maximum recoveries for servicers in both the hazard and investor claim domains.

About The Author

Leverage Partners

As a long-time consultant in the financial services industry, I’ve been involved in every aspect of software implementations. Over those years, one thing is certain: Everyone is on the hunt for innovative opportunities to reduce cost, duration, and program risk during implementation. While there are many ways to approach solving these implementation concerns, I’ve seen one tried-and-true method work for both large and midsize lenders alike: leveraging third party partners that are hyper-focused on identifying and avoiding the challenges commonly encountered when deploying a new loan origination system (LOS) will help fill key resource, skillset, and expertise gaps that neither the mortgage lender nor the LOS vendor have readily available or seek to staff long-term.

Tactical and Practical Tools for Success

Partners provide three distinct tactical benefits during implementation for both lenders and providers alike. First, a partner can help cascade the implementation program’s goals, functioning as an intermediate layer between broad directives from executive leadership and tactical changes requested from the operational team on the ground. By establishing explicit and prioritized guiding principles, they continually confirm and/or redirect the implementation to meet its goals for duration, price, scope, and other factors. This independent viewpoint allows for rigorous assessments of the costs, benefits, and risks of implementing each specific scope item or change. They constructively challenge the notion of “because we’ve always done it this way” by constructing and analyzing other scenarios that also meet or exceed project goals.

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In addition, partners can impartially define, consolidate, monitor, and report on priorities across all parties (such as leadership, business, operations, IT/architecture, and vendors), performing program arbitration as needed when conflicts arise. Given their experience in similar situations, partners can also educate parties about the key areas that are frequent offenders for delays, conflict, budget blowouts, and overlooked risks, while helping watch for signs of trouble. Constant momentum is also maintained, working with the team to focus on daily and weekly next steps, while showcasing how these translate to the next phase and longer-term goals, avoiding analysis paralysis and connecting the parts of the overall project vision.

Finally, partners can also offer useful perspective on the broader picture. By attending industry conferences and engaging in larger corporate-wide strategy conversations, partners can help counsel on external topics such as developments in customer behaviors and competitors, as well as internal topics spanning strategies, technologies, and efficiencies across lines of business.

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While it’s clear that a partner’s “outsider” status strengthens their ability to add tactical experience and expertise that isn’t part of the current organization, they also come equipped with a kit of practical tools that have been refined and expanded over many years, projects, industries, clients, and teammates. A third-party partner should bring to each assignment a kit of practical tools backed by specific expertise in areas including:

Program strategy – Ideally, partners will have line of business-specific templates for target operating models across people, process, and technology and maintain frameworks for developing quick wins, intermediate goals, and longer-term program vision.

Vendor management – Lenders can leverage partner-developed vendor RFI scorecards, questionnaires, and high-level requirement templates for initial comparison and selection, and then subsequently use trackers for monitoring and ensuring on-time delivery.

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Program management – Partners should maintain a toolkit of status report, risk/issue trackers, project plans, role/responsibility definitions, and project organizational chart templates across the variety of implementation methodologies.

Analysis – Partners should have developed best-practice process flows for both business and technology as well as high-level and detailed requirements and use case templates.

Testing – Lenders can avoid developing a single-use testing strategy and supplement their own test scripts and defect management tools via partner templates.

Training – Partners pair with vendors to design comprehensive training strategies as well as build and execute training.

Configuration and support – Provide data mapping, code development, testing, and deployment within or wrapping around the core platform

The benefits don’t stop there, either. Due to the transient lifespan of any single implementation, partners have developed resource specialties to fill the temporary project gaps created when a lender with a strong focus on operational strategy decides to implement an offering from a vendor with a strong delivery strategy.

For example, partners have built up teams that specialize in specific verticals such as mortgage, home equity, consumer lending, and commercial lending. These are particularly useful for lenders that want to challenge their current operations or are moving into a new product offering. Partners also have teams working across products to focus on horizontals such as channel optimization and marketing, risk and regulatory, business process outsourcing, talent and organization, etc. This can provide lenders with a health check on how they’re performing or supplement areas in which the lender knows they need additional support improving.

Partners may also have specialized capabilities and thought leadership in new offerings such as digital, mobile, reporting and analytics, artificial intelligence, machine learning and robotics, and data privacy and security. This can augment the partner’s ability to define progressive, yet practical next steps.

When to Leverage a Partner?

Comparing the implementation strategy and goals against the current organization capabilities and availability will help confirm the cost/benefit of adding a partner. Key elements to consider include the need for advisory support (important if the leadership wants independent advice on the industry, strategy, execution, and/or operations, or if the lender is moving into a new line of business or expanding into unfamiliar territory) and/or strategy support (which may depend upon how mature their model is for defining, framing, and executing strategies).

The lender may also need execution support, depending upon who and how much of the current operational team can be diverted to support the implementation and what mechanisms exist to backfill those needed for the project. Finally, the lender may need capability support, depending upon any implementation skillset gaps that internal resources have and whether the lender is looking to build and leverage these project execution skillsets again in the future. If so, key focus areas for internal development plans need to be established.

Two other elements are important in the decision as to whether to take on a partner. The first is project duration. What would the expected ramp-up time for a partner resource be, given the role and responsibility? What would the expected ramp-down time be for leveraging an internal resource in the same role?

The second is project cost. Here, the analysis focuses on comparing the expected cost, skillset, and allocation for an external partner against an internal resource.

How to Leverage a Partner

Partner organizations have built out flexible models to deploy resources aligned to lender scope, budget, and duration. Depending on the project, different types of resource models are available; three of the most commonly used are:

1.) Staff augmentation. Partners can provide one or two resources in key areas to function alongside internal project resources. This allows the lender to pull in templates, industry knowledge, and extra support on smaller projects.

2.) Tactical team. Partners can also be placed within specific areas, such as a requirements-gathering, testing, or project management, to provide a specific skillset or function. This allows the lender to reduce the project load on the operational team.

3.) Advisory services. Lenders can also request specific advisory services from partners to speak on key industry topics, review project strategies or decisions, or serve on executive committees. The independent voice helps provide an additional layer of quality assurance for the project.

In the end, it is up to each organization to assess their own implementation readiness and maturity, ultimately determining when, where, and how best to leverage potential partners. There is ample opportunity to create a unique combination of internal resources, software vendor resources, and third-party partner resources to best fit priorities and budgets while driving towards a successful LOS deployment that is fueled for long-term prosperity.

About The Author

Finally Getting It Right

The CFPB’s announcement that it had finalized the long-awaited amendments to TRID, initially proposed in July 2016 and commonly referred to as “TRID 2.0,” was a welcome surprise. The industry had been calling for updates, both in the way of substantive changes as well as clarifications of numerous ambiguities in the rule, since TRID’s inception. With the finalization of TRID 2.0, the CFPB has at last answered those calls.

“While the yearlong delay since its initial proposal has been frustrating to many in the industry, I think it’s clear from reading through the final rule that the changes ultimately adopted, and the Bureau’s accompanying commentary, reflect a thorough and thoughtful consideration of all feedback received from consumers and industry in response to the updates initially proposed. The Bureau clearly took their time to try to “get it right,” and I think they should be commended for that,” said Michael Cremata, Senior Counsel and Director of Compliance, ClosingCorp.

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Headquartered in San Diego, Calif., ClosingCorp owns and operates the premier source of intelligence for closing costs and service providers in the U.S. residential real estate industry. Through innovative solutions, progressive technologies and strong alliances, the company delivers timely, accurate and transparent results that help optimize closing processes and services for mortgage lenders, title and settlement companies and real estate professionals. Clients rely on ClosingCorp to help improve efficiencies and mitigate risk.

“Some of the important changes made by the rule include: introducing a tolerance for the “total of payments” disclosure; clarifying requirements around the disclosure of construction and construction-permanent loans; expanding the exemption for certain housing assistance loans; and clarifying and revising various calculations in the “Calculating Cash to Close” table. All of these changes are helpful, and should be welcomed by the industry. However, there are a few areas where I believe the Bureau missed the mark.

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“One such area is the final rule’s failure to add meaningful guidance regarding the extent to which settlement service fees may be itemized on a Loan Estimate (LE) or Written List of Providers (WLP). While the initial proposal included a helpful clarification that fees for certain “packages” of settlement services may be aggregated, the Bureau decided to drop this clarification from the final rule in favor of a comment clarifying that lenders need not include on the LE or WLP “related fees . . . not themselves required by the creditor . . . such as a notary fee, title search fee, or other ancillary and administrative services.” Whether or not these fees are disclosed on the LE or WLP, though, the rule makes clear that they still must be included in tolerance calculations at closing if they fall in the “10% bucket.” Therefore, no lender would intentionally exclude “related” fees from the LE or WLP and thus suffer a smaller “baseline” for purposes of calculating tolerances (and that’s to say nothing of the context in which the fees are held to zero tolerance, in which case there’s no clarity at all as to how they would be treated for tolerance purposes).”

However, John Levonick, Director of Regulatory Compliance at Clayton Holdings believes that as the industry digs through the new 2017 TILA-RESPA Integrated Disclosure Rule (TRID), or TRID 2.0, compliance and quality control service providers are left scratching their heads about the complexity, and possible confusion, that the rule’s open adoption period is going to create.

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“Based on our preliminary review of the 560 pages of “clarifications” that make up TRID 2.0, many if not most of the changes ease more onerous obligations from a timing, data, tolerance, content or calculation validation perspective,” said Levonick.

“The TRID 2.0 rule has an effective date that is 60 days from the date on which it is published in the Federal Register. However, compliance with the rule is optional for creditors until the mandatory compliance date of October 1, 2018. This creates an open phase-in period from the publication date through October 1, 2018, whereby creditors are permitted to choose to handle certain origination practices and disclosures either (1) in the way that was in place prior to the TRID 2.0 effective date, or (2) in the way identified as appropriate in TRID 2.0. In other words, during this phase-in period creditors can selectively comply with whichever individual requirements within the original rule and the TRID 2.0 rule that they prefer. Good news for lenders; bad news for automated rules engines and QC personnel.

“From a technology standpoint, this will cause certain external automated compliance tools to falsely identify errors that prior to TRID 2.0 were “material” and are now no longer. Providers will then need to manually “clear” these non-material errors. Most of the TRID 2.0 changes will require only minor readjustments to current loan origination system configurations (although construction loans will require more). But even minor changes take development time. And, at the moment, with many lenders focused on the coming Uniform Closing Dataset (UDC), the TRID 2.0 changes—which will not be subject to enforcement liability until the October 1, 2018 mandatory compliance date—will not go to the head of the queue.”

The bigger question is how will the Secondary Market react to TRID 2.0? Will investors be concerned about liability, and whether consumers have a private right of action for errors arising during this 2017 TRID phase-in window? “This will remain an unknown, to be addressed on a case-by-case basis as issues are identified. While the CFPB has stated that its “clarifications” are not retroactive, what will become of pre-existing TRID errors that, had they occurred after TRID 2.0’s effective date, would not be TRID errors?” answered Levonick.

“In the meantime, we all continue to work through the new rule, hopeful that, in the long run, its clarifications will reduce confusion, lead to fewer errors in origination, and increase secondary market pull through on loan acquisitions,” he added.

Cremata agrees that TRID 2.0 has some flaws. “It’s disappointing (although not surprising) that the Bureau refused to address simultaneous issue rates, additional cure mechanisms, or the so-called “black hole” (although the black hole is the subject of a new proposal, released at the same time as the final rule, on which the Bureau is currently seeking comments).

“Overall, the finalization of TRID 2.0 represents a significant positive development for the industry. Although it fails (or declines) to resolve several of what have been the industry’s biggest pain points with TRID, it nonetheless introduces a number of much-needed clarifications and amendments, and is unquestionably a step in the right direction by the Bureau,” he concluded.

About The Author

OCR For Mortgage In Action

The financial services industry is challenged with managing large volumes of documents with varying layouts containing immense amounts of data – part of which is highly critical with regards to compliance. The traditional manual process for classifying and keying data from these documents is time consuming, error prone, and costly due to the sheer volume and complexity of the mortgage documents. In an industry where standardizing forms is not always possible due to their varying systems and points of origination, an acceptable automation solution must be able to properly and compliantly handle this variability.


Top-Five Originator. This bank is one of the largest in the United States. It is a leading lender offering a range of quality home loans, including government and conventional. These loans are provided through multiple channels.

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The mortgage lending industry presents a number of unique challenges for classifying and extracting data from key documents. This is due in part to the large volumes of disparate document variations found in most loan files.

>>A typical incoming mortgage loan file may contain 250 to 600+ pages of various size documents, comprising more than 250 potential document types. Older loans files may grow to well over 1000 pages.

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>>Manually sorting each set of loan documents is a labor intensive and error prone effort, typically requiring the addition of document separator pages if the file is to be scanned.

>>Due to the sheer labor effort required, the typical level of detailed document sorting possible with a manual approach is very “coarse”. In other words, only the most critical documents and document groups are classified rather than attempting to identify all specific document types. An example of this limitation might be a manual grouping of a series of specific documents into a “Credit Documents Group” rather than breaking these out specifically by document types such as bank statements, credit reports, and brokerage statements.

>>To compete in this extremely competitive market segment, organizations are looking for ways to reduce costs and streamline their processes.

In addition to the challenges described above, this top five originator was looking for a solution to help automate the laborious task of providing data for a number of audit-centric applications. These ad-hoc projects commonly had tight timelines and included wide ranges of loans, and millions of pages to be audited.

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Project Description

At the start of the Project, this top five originator had a sophisticated document capture infrastructure feeding a well-known enterprise content management system in place. What was missing from this infrastructure was an advanced recognition module that could deal with the document variations expected in an organization serving borrowers across the nation.

The ideal solution needed to provide a seamless interface to this current capture infrastructure. This would greatly simplify the implementation by allowing the existing interfaces to both front-end scanning and back-end image storage to be largely unaffected by the addition of the recognition technology.

Prior to the installation of the new recognition components, a large team would manually classify incoming documents into a moderately broad range of categories or Document Groups. Once these documents had reached the enterprise content management system, a team of underwriters would review, manually enter data, and process the loan.

Limitations of this approach included:

>>Heavy reliance on the skills of the people manually classifying documents and extracting data. Error rates varied from operator to operator. Thus, a loss of a skilled operator for any reason had a negative impact.

>>Time is of the essence in any mortgage-processing environment. Using a human-centric approach meant that processing times were proportional to staff availability at any given moment.

>>People tend to be more expensive than computers and software.

>>Regulatory bodies as well as this originator would have preferred a greater granularity in the way documents were classified. However, this need was outweighed by the complexity and difficulty presented when attempting to teach and maintain a group of individuals in how to classify documents among over 250 possible choices.

The new extraction system was selected after an exhaustive evaluation process. A competing solution was initially tried. However, after months of tests, it was determined that a more advanced solution was available which had a number of capabilities that surpassed other solutions previously tested or reviewed:

>>This new solution was by far the fastest technology available to read OCR mortgage documents. Pre-production technical due diligence empirically showed a system that was capable of processing approximately 1 million images per day on a single twelve-core server.

>>This solution was able to use one set of rules to process and recognize all document variations. Because of the extremely large number of documents (and variations of each), which this top five originator encounters, they required the flexibility offered by a non-template-based ADR (Automated Document Classification) and data extraction solution.

>>This solution offered pre-built mortgage logic, which “understands” the vast majority of the document types and variations that were required to be recognized. This solution allowed this originator to rapidly implement an ADR and data extraction solution for their specific needs.

The initial focus was to implement an ADR solution that supported more than 250 different document types and potentially hundreds of variations of each document type. The vast majority of the pages in a loan are now identified automatically with no human intervention. The remaining exceptions are presented to operators who either accept the first choice page type or choose an alternative.

This system is able to narrow down the page types that are lexically possible based on the text on the page. Because of this, in most cases, the operator can choose from a list of no more than five alternate page types. This reduces errors and review time in the verification process.

Upon production implementation of the ADR solution, the focus shifted to automatic data extraction. A list of more than 1500 fields was identified for the first implementation phase of data extraction. Both this project and the ADR work that preceded it were initially implemented in one of the originator’s major channels in order to ensure a wide variety of document sources and variations.

Today both of the projects described above are in full production. The amount of manual labor previously required for these tasks has been reduced significantly. Error rates are lower than the human processes that preceded implementation. The end to end processing time has been vastly reduced due to the fact that much of the human labor has now been replaced by lightning fast computer CPU cycles. Additionally, this top five originator has implemented sophisticated downstream mortgage lending business rules to take advantage of the valuable data generated by the new system.

This top five originator, like any other mortgage lender, is subject to a variety of time-sensitive requests such as internal audits. These audits require that specific data be tabulated from each loan file and reported to the appropriate entity. In some cases, the volume of loans included in these audits can reach into the tens of thousands, with a very limited response timeframe. With the system now in production, it is possible for this organization to be more agile than in the past. New data fields can be configured and tested in a few hours and a million images can now be interrogated for salient data overnight.

Additional capabilities leveraged successfully at this customer include:

>>Verification provides a list of likely document types to further increase speed of verifying exceptions.

>>Ability to customize how documents are handled based on the type of process to be conducted (e.g. origination, servicing, audit, etc.).

>>Ability to quickly recognize additional document types using the automated learning facility.

>>Database lookups and business rule logic checks to ensure the highest degree of data accuracy.

>>No scripting interface, with easily configurable rules to modify customers’ highly sophisticated ADR and data extraction processes.

>>Ability to add processor cores (including new servers) to the environment in a matter of minutes to quickly scale and meet tight deadlines or increased staffing demands.


The project was successfully implemented and released to production on time. As a result of this experience with both the Paradatec staff and the Paradatec solution, this customer is prepared to act as a reference on behalf of Paradatec. Prospective clients are encouraged to take advantage of this opportunity.

Paradatec is rapidly approaching the significant milestone of processing 300,000,000 pages annually for this client alone. As a company, Paradatec processes several billion pages per year.

Paradatec’s solution is an advanced and unique OCR recognition technology. It utilizes neural networks technology and artificial intelligence and is able to read structured, semi-structured, and unstructured documents. It then makes ‘decisions’ about document characteristics in much the same way as a human being does— only many times faster and without human intervention.

Paradatec takes a very different approach from other OCR forms processing technologies in that it is a truly template-free design, allowing the system to easily cope with the varying layouts of each document. In performance terms, Paradatec is capable of processing thousands of documents per hour with a single processor. It provides even further scalability by offering seamless support for the latest in multi-core processor technologies and multi-server configurations.

Per Neil Fraser, Director, of US Operations, “To be chosen by such a high-profile client for a project of this size was a vote of confidence for Paradatec and our leading edge technology. I would encourage other similarly placed clients to reach out to Paradatec to setup a ‘One-Day Blind Test Challenge’. In just a day it is possible to see what this technology can do, right out of the box.”

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Overcoming Challenges

Newsflash: marketers feel overwhelmed. According to a study by Emma, only 12% of marketers say they always meet work expectations.

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The email marketing company also learned the following about marketers:

>>A whopping 64% don’t have enough the time or personnel to do the kind of marketing they would like.

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>>They suffer from conflicting priorities, with nearly 50% reporting they feel more pressure to meet internal (organizational) goals than audience expectations.

>>Nearly 40% say they wish they could do more targeted marketing.

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For highlights from the report and tips on how to overcome marketers’ challenges, check out this infographic:

The Push For Faster, More Accurate Transfers

Effective October 19, servicers must comply with recently finalized mortgage servicing rules, which include an updated set of regulations. Among other things, the regulation has clarified the requirements regarding the transfer of mortgage servicing rights (MSRs), particularly as they apply to borrowers who are in default. The regulatory agency is requiring that these distressed loan files be transferred “seamlessly.” In other words, the receiving servicer is required to pick up right where the selling servicer left off in terms of the loss mitigation process so the distressed borrower has a smooth experience during transfer. Easy enough, right? Not really.

It means a receiving servicer, in addition to determining how many loan files in a transaction are actively in loss mitigation, must also have access to a complete detailed history of what has been offered to the defaulted borrowers to date in order to determine where they are in the loss mitigation process.

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Central to achieving this seamless experience is the accurate transfer of loan-level data from one mortgage servicing system to another. Today, providers of mortgage servicing technology have made great strides in developing reliable integrations or bridges between systems allowing for the transfer of MSR data. Although transfers between the major servicing platforms are generally fast and reliable due to their out-of-the-box capabilities, transfers between newer systems and older, legacy systems — or homegrown platforms — can be more error-prone because these integrations are harder to carry out.

This, of course, is on top of the vast array of discrepancies that can exist within the loan files — from missing data fields, missing data or documents to misnamed data fields — often depending on the vintage of the loans. Sometimes there’s data included that simply can’t be correlated with anything else in the file. And practically every servicer has a different way of addressing such issues. For these reasons, the first transfer with a partner is often the messiest and most challenging. However, if done correctly, each subsequent transfer becomes easier as issues are smoothed out.

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Although the MSR transfer process is still far from standardized, today there are advanced technology tools that ensure fast and accurate transfer of data between systems. Using data, analytics and automation, these tools help servicers quickly go through loan portfolios and identify potential compliance problems ahead of time.

At BSI Financial, we developed Asset360, an analytics engine that sits on top of our vast database of loan performance data. It delivers a complete, data-driven life-of-loan system that, in addition to enhancing the MSR transfer and loan onboard process, enables ongoing, comprehensive monitoring of all loans for performance and compliance.

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We’ve found that by using a mix of data, analytics and automation, we not only have the ability to monitor all loans for potential performance, compliance and documentation issues, we can also achieve faster data transfer speed and accuracy during MSR transfers. As these tools continue to develop, they will reduce the need for the antiquated “stare and compare” manual post-transfer reviews.

The servicing rules have, in essence, required servicers to create complete “life of loan” databases that facilitate a complete audit trail for where that loan has been, who serviced it, and when — basically every detail of how the loan has been serviced to date. The problem, of course, is that not every servicer keeps the same data on every loan. That means sometimes when loans are transferred, the information the successor servicer receives is incomplete. Being able to identify these loans quickly and take action on them during the transfer process is essential.

But data and analytics have applications in the loan transfer process that go well beyond just finding which loans are in default and ensuring the loss mitigation remains intact “in flight.” To better understand how data and analytics can aid in the transfer process, including how they can improve accuracy and efficiency; let’s take a look at the various stages and where they come into play.


At our firm, once we schedule a transfer, our coordinator will setup a kickoff call between the counterparties to discuss deliverables, timelines, etc. We know that boarding loans has instant Karma tied to it — one day a servicer is on one side the deal, the next day it’s on the other side — so, having an open channel of communication and scheduled meetings ensures a good final experience.

During the initial meeting, we typically request preliminary data from the seller to be delivered to us 30 days prior to the transfer. We run this preliminary data through Asset360 and analyze it as if it were final data. In addition to running rigorous data checks, we also make sure that we have all the data, that it is formatted properly, and that it is logical. Once the preliminary data has been received, our mapping team creates the upload sheet, and completes the Balance & Reconciliation against the prior servicer’s data.

The mapping between core systems — and how accurately it is done — is a key factor when trying to achieve fast and reliable transfer of loan data. We make sure every transaction gets a template so that when we conduct a new deal, we are building on the knowledge gained from the last one. Inevitably, there are minor changes in systems and in the terminology codes that are used and we’ve found that using a template that is continuously updated makes it easier to track and make small changes.

A word about communication

At this point, it should be mentioned that communication is a critically important factor in achieving fast and accurate MSR deals these days. Communication between the buyer and the seller — particularly after the deal is closed — is absolutely critical. The two teams involved must be able to communicate readily and easily, in case questions regarding discrepancies in loan files arise. The new data and analytics tools, such as Asset360, make this communication much more targeted and specific, because it allows teams to identify problems and drill down into the related data quickly.

Not only is communication between teams an important aspect of the MSR transfer process, it is equally as critical that servicers stay abreast of the latest regulatory changes. We communicate weekly with our legal team to check the pulse of regulatory changes, so those changes get written into code that ensures compliance.


During the verification stage, quality assurance should be reviewed to determine whether the mapping needs to be updated.

We require final data and Balance & Reconciliation to be sent to us within five days of service transfer. At that point, the mapping team will review the final data and create the final boarding templates. After the templates are reviewed, the loans are ready to be boarded to the production region.

Post boarding

Once loans are active on the system, a notification is sent out to the company to advise the same and a QA process is performed on the pool. Welcome/DVN letters are mailed within 15 days of transfer.

Post mortem quality assurance

Following transfer, I recommend a post-mortem call be conducted with the conversions team to review all issues that arose during the transfer. Comments should be logged and tracked for future reference. I have found it is very helpful to capture what went well, what didn’t, and then institutionalize that knowledge so we’re more efficient the next time we transact with that same servicer.

Continuous refinement

Today, servicers need to stay focused on getting ahead of regulatory guidance. We’ve found that the most effective way to do that is to build software to house the logic needed to identify high risk loans. In addition, boarding data before loading the loan, defining logical and illogical conditions, running data through conditions checks and communicating seamlessly with the prior servicer and the borrower are critical aspects of any successful loan transfer program.

Today servicing technology can quickly quantify risk points and carry out logical condition checks to perfect data that doesn’t make sense. By focusing on compliance and seamless boarding from one platform to another, MSR transfers can be completed faster and with a smaller error rate.

At BSI Financial, we’re pleased that the technology we developed to handle these challenges is getting its day to shine.

About The Author

Augmented Intelligence

One of the first introductions of artificial intelligence to the general population came in 2011 when Watson competed on Jeopardy. Ken Jennings and Brad Rutter were arguably the best players the show had produced over its decades-long lifetime. In total, they had walked away with more than $5 million in prize winnings, a testament not only to the breadth and depth of their knowledge, but their strategic savvy with category selection and wagering. Watson, a computer system developed by IBM, was capable of answering questions posed in natural language. Watson had access to 200 million pages of structured and unstructured content, but was not connected to the Internet. Watson consistently outperformed its human opponents on the game’s signaling device, but had trouble in a few categories, notably those having short clues containing only a few words. The key here was the development of a natural language processor that would become the foundation for numerous future applications like Siri. But while its rapid responses to questions may have struck many as robotic, Watson was not a robot in the traditional sense. Robots are machine built to carry out physical actions and may or may not be designed to approximate the human form. I am sure many of you remember the TV series, ‘The Jetsons’ with Rosie, the humanoid robot maid and housekeeper. Or maybe not, because that series was on in the early 1960s.

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‘In Search of a Robot More Like Us’ was a 2011 New Your Times science article written by John Markoff. He stated that:

The robotics pioneer Rodney Brooks often begins speeches by reaching into his pocket, fiddling with some loose change, finding a quarter, pulling it out and twirling it in his fingers. The task requires hardly any thought.” But as Dr. Brooks points out, training a robot to do it is a vastly harder problem for artificial intelligence researchers than IBM’s celebrated victory on Jeopardy…. Although robots have made great strides in manufacturing, where tasks are repetitive, they are still no match for humans, who can grasp things and move about effortlessly in the physical world. Designing a robot to mimic the basic capabilities of motion and perception would be revolutionary, researchers say, with applications stretching from care for the elderly to returning overseas manufacturing operations to the United States.

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So, let’s leave the discussion about robots for another time. Instead, I’ll focus on defining augmented intelligence and differentiating it from artificial intelligence. It’s more than a question of semantics. Artificial intelligence, perhaps from its popular culture use in general and its science fiction use in particular, can conjure up images of the sentient machines with personal agendas. It suggests a culture where, at least in some part, humans are no longer required to make decisions. Some industry experts believe that the term artificial intelligence can create more negative speculation about the future than hope. defines augmented intelligence as an alternative conceptualization of artificial intelligence that focuses on AI’s assistive role, emphasizing the fact that it is designed to enhance human intelligence rather than replace it. An alternative label for artificial intelligence also reflects the current state of technology and research more accurately.

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According to an article by Athar Afzal, “We’ve transitioned from an agricultural-dominated society to the industrial revolution – and now to a more data-driven economy. What we’ve witnessed during each of these stages is some form of mechanics or machinery developed to augment our performance, thereby improving our outcome…. The world has a lot of opportunity to gain and make our lives better with augmented intelligence – it’ll make our lives far smoother and more enjoyable. I invite everyone to view Ginni Rometty’s speech at the World Economic Forum.”

Researchers and marketers hope the term augmented intelligence, which has a more neutral connotation, will help people understand that AI will simply improve products and services, not supplant the people who use them.

While a sophisticated AI program is certainly capable of making a decision after analyzing patterns in large data sets, that decision is only as good as the data that human beings gave the programming to use. The choice of the word augmented, which means “to improve,” reinforces the role human intelligence plays when using machine learning and deep learning algorithms to discover relationships and solve problems. I’ve summarized some definitions by Jean-Albert Eude below.

Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range. The processes involved in machine learning are similar to that of data mining and predictive modeling. Both require searching through data to look for patterns and adjusting program actions accordingly.

Deep learning is an aspect of artificial intelligence (AI) that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge. At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction. Each algorithm in the hierarchy applies a non-linear transformation on its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label “deep.” The advantage of deep learning is that the program builds the feature set by itself without supervision. This is not only faster, it is usually more accurate. In order to achieve an acceptable level of accuracy, deep learning programs require access to immense amounts of training data and processing power, neither of which were easily available to programmers until the era of big data and cloud computing.

The value of such augmented predictive analytics to a segment of the economy as dependent on data as the mortgage industry is obvious. What is also obvious, unfortunately, is that we may be among the last to seat ourselves at the technology table.

Often, an early title or tag line for a concept or theory evolves over time as others develop their ideas and work toward a solution. In the mortgage industry, the concept of paperless mortgages was proposed in the early 1990s to reduce and/or eliminate what some conceived as unnecessary paper and to improve the overall experience for the consumer. Along the way we started referring to it as an electronic mortgage (e-mortgage) and now it is the digital mortgage, an all-inclusive data and documents packaged in a format for both human and machine consumption. That will certainly achieve the initial objective to eliminate paper and improve the consumer experience. The operational benefits extend from origination all the way through to the secondary market.

But going digital without building the internal architectures to capitalize on data-driven support technology is like going to a 3D movie, but not putting on the 3D glasses to watch it. If we don’t keep moving our own finish line, we risk being trampled by those with a longer view of the race.

About The Author

Talking To Prospects

According to Jill Konrath, in today’s crazy-busy world, the ONLY way to capture and keep your prospects’ attention is to talk with them about what matters to them. Pure and simple. That’s all they care about.

Your prospects have zero tolerance for salespeople who think their job is to share their unique methodologies, great technology or extraordinary service. All that info is available online. If it’s of interest, they’ll find it—on their own time.

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Jill Konrath is a globally recognized sales strategist. She’s an in-demand speaker at sales kick-off meetings and conferences where she shares fresh strategies to help salespeople deal with emerging challenges.

Forrester Research also found executives state that 86% of the salespeople who called on them were NOT prepared to have an intelligent conversation on their initial meetings. Most didn’t get a second chance.

Yet your prospects are thirsty for ideas that can help them do their job better, faster, or more productively. They crave information on how to increase revenue, reduce costs or expand their market presence. They search for insights on how to deal with changes in the business environment. A salesperson that delivers these ideas, insights and information is pure gold.

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Additionally, Forrester Research shows that the first vendor to create a viable vision of the future has a 74% chance of closing the sale. That’s an amazing statistic, but entirely achievable IF you target the right prospects, research their business and talk with them about what matters most. In short, you need to use catalysts and context to lead an intelligent conversation.

According to Wikipedia, a catalyst “speeds up a reaction by lowering the activation energy required for the reaction to proceed.” In sales, a catalyst does the same thing. Suddenly the organization is receptive to new options. The grip of the status quo has been loosened. Money even appears out of thin air. Here’s how to find and leverage these trigger events for maximum impact at your company.

Identify The Catalysts

To get started, analyze your existing clients. Ask yourself:

(1)…What internal factors (inside the company) made your clients finally decide to take action?

(2)…What external factors (outside the company) changed their business environment sufficiently so that the status quo was no longer sufficient?

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Often salespeople discover distinct changes or issues that are behind virtually all decisions. Invariably, these are the most common:

New Leadership: Often, within 3-6 months, new executives are implementing fresh initiatives to drive revenue, reduce costs or increase efficiencies.

Financial Announcements: If business is up, expansion projects take priority. If business is stagnant or down, productivity or cost-saving initiatives jump to the forefront.

Mergers/Acquisitions: Any change in this area causes organizations to re-evaluate all their supplier relationships.

New Strategic Initiatives: When new corporate directives are announced, the entire organization shifts to ensure they’re in alignment.

Legal/Compliance: Changes in government regulations (e.g. Affordable Care Act) cause organizations to take immediate action.

These are only some of the many catalyzing events that can create opportunities for savvy sellers. Others include reorganizations, new product/service announcements, relocations, market expansions, new business deals, or new funding.

If you’re new to this type of thinking, review local or national business publications and ask yourself: “If this happens, how does this impact an organization’s need for what I sell?”

Knowing your catalysts frees you to pursue those companies where you have a higher likelihood of closing an accelerated deal. Plus, you’ll know what to talk to these prospects about since you are deeply immersed in their issues and challenges. It’s the fodder you need to have an intelligent conversation.

Get A Jump Start On Competitors

Here’s the good news. Many of these catalytic events are newsworthy announcements, shared publicly by the company. Or they’re part of required financial reporting.

The easiest way to get your hands on this info is to leverage sales intelligence tools – ones that automatically search for your specific catalysts and deliver them to you on a timely basis. If you sell to a small number of companies or track a finite set of catalyzing events, you can get by with Google Alerts.

But things get pretty complicated when you need a steady stream of new prospects. Or when you’re selling to multiple market segments. Or when a variety of these catalysts can signify a loosening of the status quo.

Sales intelligence apps like InsideView, Lead 411 or DiscoverOrg can totally automate this process for you. They sort through all the junk that’s out there, giving you daily alerts about what’s happening in your territory – on just those catalyzing agents that you want to follow.

Intelligent conversations depend on sale intelligence. Today, it’s imperative to be the first one in the door with fresh insights on how to deal with the emerging priorities and issues.

Planning an intelligent conversation involves combining what you know about the catalyst’s impact with your understanding of the context of prospect’s situation.

From this, you develop a game plan to pique your prospect’s curiosity, showcase your depth of knowledge and build trust that you’re an invaluable resource.

You’ll know you’ve succeeded, if, at the end of your conversation, your prospect says, “Very interesting. We clearly need to look at this in greater depth.”

Let’s go back to where we started. According to Forrester Research, the first vendor to create a viable vision of the future has a 74% chance of closing the sale. That means you need to identify a gap between your prospect’s business goals and their current situation.

When companies are disrupted by catalytic events, a gap naturally emerges. Suddenly the status quo is no longer sufficient. Change is needed.

Salespeople who use sales intelligence apps to notify them when these trigger events occur consistently outperform their peers. They get in early, which gives them the ability to shape the discussion.

When these same salespeople leverage context, they integrate their knowledge of the company, individual people and their digital behavior into the conversation. They know more—which enables them to come up with better ways to help their prospects achieve their specific goals.

Combined, catalysts and context yield intelligent conversations – ones that are focused on helping prospects create a viable vision of what’s possible in their organization. In this article, we’ve just talked about the initial conversation. You’ll need more to close the deal. But you’ll get there faster and with minimal competition. It’s definitely the way to sell today.

About The Author

Build Trust Through Authentic Content

Because the word authentic is so nebulous, it becomes hard to define, but I take it as creating value for an audience without ulterior motives. Yes, you want more customers. Your content shouldn’t be thinly-veiled advertising, but instead, should provide useful information to your audience. You’re not trying to be something your brand isn’t. You stay true to your company values and impart them through your content.

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Being authentic doesn’t mean pulling crazy stunts like Burger King did recently when it hacked Google Home with its ad. Authenticity isn’t attention-seeking or off-the-wall. It’s real. It feels comfortable.

So how can you build trust through authentic content?

Know Your Audience

I put this as a tip in so many of my marketing articles, because there is nothing more important than really, truly understanding your customer base. What is important to them? What kinds of questions do they have that you can answer through content? How can your brand really offer value?

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You’ll notice that none of those questions had anything to do with your product. Knowing your customer is about being a good citizen. About asking questions and listening rather than making assumptions and talking.

Remove the Mask

Many times, brands put up this facade to come off as … I don’t know, more professional? They don’t see value in letting customers into the heart and soul of the business. But that’s part of your story, and your story is what people want.

If you got the idea for your business when you fell down a flight of stairs, why not share that very human tale? If every Friday your staff dresses up like superheroes, that’s a great visual that turns your company into a band of actual humans. Don’t distance yourself from your audience; instead, connect with them.

Be Consistent

If you see success in writing really long, in-depth posts, keep doing that. Or if people go crazy for your daily Twitter tips, lather, rinse, repeat. People will begin to trust your brand when they know what to expect from it, so make consistency the key rather than being all over the place, experimenting to see what sticks.

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Don’t Be Afraid to Make Mistakes

Because brands are made up of people, they make mistakes, and that’s okay. Whether it’s a typo on your blog or misinformation you shared, don’t try to cover up the error with corporate speak. Be apologetic (“Oops! We goofed. Our apologies.”) and move on.

Being authentic is simply about being true to your brand’s nature. It should feel organic and easy.

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