Package Utility Using Query Builder
Package utility facilitates the building of a user interface (UI) to execute XPath queries for information-related pages, assets, etc. The user gets an option: either view the result of the query on the same UI or build a CQ package of the result obtained from the query. This approach has two key benefits: It can be used while syncing the environments Example: Suppose we have 100 pages in QA environment, 100 pages in production till date, and after a week, around 50 pages get created in QA. Now the environments are not in sync. We can fire a query in QA environment to fetch information about the pages created since last week, make a package of that result, and install in production. That will sync both environments. The UI allows authors to execute queries without having xpath knowledge. Steps for implementation 1) Create an html form to input the parameters regarding query. 2) To handle the request, create a servlet, in which the input fields will be retrieved from a form and query description will be created as a map. 3) Use the Query Builder API to create the query and get results from it. 4) Use Package Manager API to create the package of the CQ query and download it from AEM package manager console. User interface Using the Query Builder tool Select the Xtype from dropdown to search for like assets or pages. Select the name of the property from the dropdown. You can also provide a property which is not present in the dropdown by selecting ‘Other’. A text box will appear to enter the property. You can provide the value of the property and the date-range value for properties specific to date range. You can click on the ‘Show Results’ button to view the results of the query and for building a package click on Build Package button for package creation of the results generated.
Integrating Kaltura with AEM for a Seamless Video Experience
Adobe Experience Manager (AEM) enables users to create, edit, manage and optimize websites across different digital channels such as web, mobile, and social. Integrating Kaltura with AEM helps save all the videos in Kaltura and reduces the burden on AEM. Kaltura is a secure audio/video streaming service that allows uploading and embedding high-quality media. Some of the key advantages of AEM-Kaltura integration are: ˃   Helps in uploading individual media files from desktop to Kaltura ˃   Facilitates multiple media file upload from desktop to Kaltura in one shot ˃   Helps to record just-in-time audio/video messages for assignments and/or feedback Steps for Integration Create an account on Kaltura’s website using the link below: http://corp.kaltura.com/Products/Kaltura-API Create a job in AEM project which is scheduled to run at regular intervals. Its purpose will be to fetch all the metadata and thumbnail images of all the videos from your Kaltura account and save them in Digital Asset Management (DAM). Below is a screenshot of the Job created in AEM. This code fetches all the metadata from Kaltura. The properties shown in the above screenshot are of the account created on Kaltura’s website which would be used to connect to Kaltura API to fetch the videos. The video player can be dynamically embedded on the web page to run videos. Below is the code to do this.
Future of Mortgage Originations: Here’s Why Today’s Mortgage Originators Will Lose 35% Market Share by 2020
Who originates a mortgage loan? Mostly mortgage brokers, be it as firms or individuals. In some cases, the lending organization itself acts as the originator. A large volume of mortgages are originated by only a few giant firms. However, an equally large volume is originated by thousands of individuals and small firms. Is the industry going to stay that way, or will it change sometime soon? Studies and statistics say that the share of proportions is changing fast. However, how fast, is anyone’s guess! A study by Accenture says that by 2020, today’s mortgage originators will have collectively lost 35% of their market share to new entrants and small lenders, who are adopting new operating models. Online and independent lenders, who emerged after the great credit crisis, have already stolen the market share of midsize banks. Origination comprises marketing mortgages to consumers, assessing their credit-worthiness, verifying the documents and legal papers, identifying the right products for borrowers, processing the mortgages, capturing data, and storing it productively. Steps involved in originating a loan differ based on factors like loan type, loan risks, regulations, and lender policies. The disruption The traditional pen-and-paper mode of lending is gradually being replaced by IT intervention, leading to online implementation of almost all origination processes. All that is required is a web portal or a mobile app. Recent trends and projections for the coming years highlight some good news and some bad news for firms. The good news is that loan processes will be quicker and less dreary. The bad news is firms that are failing to gear up will gradually lose business—just like Kodak faded away from the photography industry, or the way tape recorders disappeared when digital disks and iTunes took over. The traditional model Sales agents assist customers to understand how terms of payment and interest rates matter before selecting the right product. Agents also help select suitable add-on products like insurance protection for loans. The assistance is available for filling application forms, and the required documents like proof of income, identity, address, assets and liabilities, right up to when the application is submitted. Back-office functions of loan origination continue from that point. The new self-service model Web technologies and smartphone apps are revolutionizing mortgage lending. Online-only originators like GuaranteedRate.com, QuickenLoans.com, and Sindeo.com have come up with websites and mobile apps capable of doing everything that sales agents and mortgage brokers did. With uncluttered user interfaces and intuitive algorithms, they can guide even the not-so-tech-savvy customer smoothly through the entire process. The smart systems help customers calculate loan durations and identify suitable repayment structures. The systems auto-fill customer forms with the right data and provide tips and suggestions depending on the needs of particular customers. All documents, photographs, and signatures can be submitted online, and the systems can verify immediately if the applicant qualifies for the loan. If everything is fine, the whole process will be completed and the amount disbursed in less than 15 days—without any in-person interaction. Quick and cost-effective, such a system also offers customers a seamless experience. In addition, smart systems provide personal financial management tools and access to other multiple accounts of the user. At any point of time, the customer will have access to all relevant data pertaining to the mortgage. New firms benefit from big data, analytics, cloud, and virtualization, and are able to come up with market-smart mortgage products. They can analyze customer profiles in no time and offer risk-adjusted products offsetting risk costs. It helps the firms with greater volume of business than one can fetch by sticking to the traditional risk-avoidance model. Moreover, new firms stay embedded in social media, constantly grabbing attention of customers and building rapport. In short, the coming years will present increasingly complex and highly dynamic environments. Besides immediate implementation, agility in IT development and constant innovation shall be the key drivers of lending businesses. FAQs – Tavant Solutions How is Tavant preparing for the future of mortgage originations?Tavant is investing in next-generation technologies including AI-driven automation, blockchain verification, predictive analytics, and cloud-native architectures. Their forward-looking approach ensures mortgage origination systems can adapt to emerging technologies, regulatory changes, and evolving customer expectations. What innovative features will Tavant include in future mortgage origination platforms?Tavant will incorporate voice-activated applications, biometric verification, real-time collaboration tools, predictive document generation, and intelligent risk assessment capabilities. Their platforms will offer seamless integration with emerging technologies while maintaining security and compliance standards. How will mortgage origination change in the next decade?Mortgage origination will become increasingly automated, with AI handling most routine decisions, blockchain providing secure verification, and predictive analytics enabling proactive customer service. The process will be faster, more transparent, and highly personalized for each borrower. What technologies will transform mortgage origination?Technologies transforming mortgage origination include artificial intelligence, blockchain, machine learning, cloud computing, mobile platforms, biometric authentication, IoT integration, and advanced data analytics that enable more accurate, efficient, and secure mortgage processing. What will the mortgage application process look like in the future?Future mortgage applications will be conversational, using AI assistants to guide borrowers through personalized application flows. The process will be completed primarily on mobile devices with automatic data verification, instant pre-approvals, and real-time status updates throughout the origination process.
Big Data Analytics Will Drive Mortgages and Property Valuation
The mortgage industry has become information-centric and highly competitive. Firms that understand mortgagor behavior and industry trends are managing to survive better than the others. Sophisticated big data analytics is central to this trend. ‘Big Data’ involves large and complex data sets, which yield surprisingly detailed insights. But the immensity of data necessitates special technology to process it and draw meaning out of it. Big data includes customer data captured from various sources, data bought from third parties like credit-rating agencies, and data from web, mobile, and social sites. To comply with regulations, there is a need to maintain account-holder information in the system for seven years. But for better reporting at the loan level and borrower level, data needs to be maintained for longer periods. Thus, data at all levels—origination, underwriting, fulfillment, servicing, modifications, bankruptcy, and foreclosures—keep swelling into terabytes. Then there are numerous variables that influence property value. Data keeps growing massively at micro and macro levels. Big data helps appraisers, lenders, and investors to estimate the present and future values of any real estate. It helps to better understand where markets are headed and make smarter decisions. With legacy systems, 80% of an appraiser’s time is spent on data entry. The process is tedious and prone to errors. The support of big data in an appraiser’s software helps make accurate valuations many times faster than it is possible with legacy systems. That will transform the nature and productivity of the appraiser’s job. Let’s see how big data helps the valuation process: It provides better insights on market environments that enable appraisers to comprehend growing and sinking markets in depth. It helps appraisers to address inconsistencies. Currently, the dependence on too many data sources creates misalignment between appraisers, lenders, and investors. Big data technology can collate all the different data sets, and create better objectivity and transparency. It helps them create detailed and compelling graphs and illustrations that make information more digestible. So what happens if an appraiser chooses to ignore big data? There are huge implications related to market risk. Big data can provide immensely insightful predictive reports that highlight dangerous or favorable trends like high debt-to-income ratios, unusual spikes in value and more. However, big data intelligence is not a substitute to human intelligence. Rather, it is a highly powerful supplement to it. Machine intelligence can do huge volumes of complex calculations at astounding speed and deliver reports. But to understand the implications of those reports and to take wise decisions, human intelligence and practice are important for appraisers. Big data helps them do their job better with respect to speed, efficiency, and accuracy. Efficiently performed big-data analysis through SaaS (Software as a Service, also known as cloud-based software) can be used by anyone with internet connectivity. This facilitates data entry from the verification site, and it can be continued seamlessly outside the office setup or during travel. Many data fields can be auto-filled or imported from databases. This eliminates redundant data entry and manual errors. Overall, it reduces manual labor, processing time, and rate of errors, thus reducing the need for review appraisals. That means huge leaps in efficiency in spite of difficult market conditions.
When the right technology is out there, who needs a mortgage broker?
The traditional lending process required a middleman who could handhold consumers through reams of paperwork. 30 percent of loan originations in 2006 were through brokers. In 2014, it has come down to 10 percent. Where are all the brokers going? After the financial crash, many left business. The Consumer Financial Protection Bureau (CFPB) put in rules, which still prevent brokers from increasing their incomes by pushing clueless consumers into expensive mortgages. The CFPB rules also disallow brokers to extract commissions from borrowers and lenders simultaneously. Such developments have made brokering a less luring field in the US. In addition, there is the reverberation of advanced technology. These days, technology helps consumers avail mortgages through personal computers and mobile devices. Online-only mortgage firms like QuickenLoans, Lenda, GuaranteedRate, and Sindeo are beginning to dominate the lending industry. Even big traditional banks are losing market to them and mortgage brokers are finding it increasingly difficult to hold relevance. Mortgage software: the champion of multi-tasking Mortgage software plays the broker, the loan officer, the assessor, the reviewer, and the gatekeeper, and processes become swifter. The costs of processing shrink remarkably. Noticing the lightning pace at which online-only lenders are growing, traditional lenders are building their online lending models. Intelligent rule-based mortgage software can provide a simple online form to submit applications. Applicants can submit their income proofs, identity proofs, photographs, and signatures online. The software retrieves the applicant’s credit scores from credit-rating agencies (third party) and check for eligibility. Such a system can also parse credit behavior of applicants and offer risk-adjusted rates. Speed, cost efficiency, and convenience on the cloud Taking your consumer lending system to the cloud makes the entire processing and loan disbursal complete in about 15 days. With legacy systems, the steps might take more than a month. Legacy banking systems involve volumes of pages to be filled by customers and call for help from a knowledgeable person, who in this case, happens to be the broker. With software as a service deployed over the cloud, only relevant forms and fields, based on the kind of loan applied, show up. Many of those fields get auto-filled from the database related to the applicant due to access to big data. This saves a considerable amount of time, and ensures accuracy by evading manual entries. The information can be updated in the central database and reported to credit rating agencies. These steps occur quickly and seamlessly, avoiding many layers of redundant data entry and wasted manpower. The gain goes to the lenders and the borrowers. The pain goes to brokers and other intermediary officers. However, this model is here to stay. More and more banking institutions are turning to automation with robust software to improve business and customer satisfaction.
Five Ways in Which Big Data Helps You Grow as a Lender
Everyone is after big data these days, thinking more data means more success. On the go, they realize it is not data alone that matters, but also the tools to manage and analyze them. A recent survey by Gartner Inc. found that in 2013, 37.8% of North American organizations had invested heavily in big data. The proportion rose to 47% in 2014, and is expected to be 74% by 2018. Customer information is particularly important to the lending sector. As regulations increase, finding the best customers should not involve overheads that don’t translate into profit. That is why big data is a vital aspect in technology that supports lenders. What lending institutions can do with big data – Security and fraud detection Big data technology identifies patterns buried in data and gives a holistic view to customers. It makes predictive analytics an important part of banking software. By clustering information, technology can help to distinguish suspicious activities from others. This pre-emptive intelligence helps you eliminate fraudulent transactions. Risk Management An integrated finance and risk management data platform can quickly address new requirements. This will facilitate new regulations for better internal management. Offering the right mortgage is necessary because it minimizes the risk of defaults and revenue loss for lenders. Offer personalized products Integrated processes help understand customers’ spending habits, and identify online channels they use and who the key influencers are. This helps take the right mortgage plans to the right people. For some borrowers, you already have their basic data and financial profiling. If they have enquired somewhere else about refinancing, or listed their house for sale, or advertised to buy a new house, you will get an alert through big data technology. That is the most opportune time to offer them new products matching their new needs. Group borrowers for better targeting Big data intelligence helps to classify potential customers based on their buying behavior, interests, age, purchasing power and more. This can boost the response rate to sales, promotions, and marketing campaigns. Compliance and regulatory reporting The Dodd-Frank Act requires lending firms to document everything that goes into the deal through a deal monitoring system. New generation data technology can ensure thorough documentation and automated compliance with regulations. Big-data based subscription to software services will allow your system to stay updated on new regulations as well. Big data is all the data possible to be related to the daily life of your potential borrower. It comprises customer data recorded at your organization, data that the systems harvest from mobile, social media, and ecommerce sites, and data that you can buy from data vendors. You can also avail credit ratings from relevant agencies to maintain in-depth knowledge about your market. FAQs – Tavant Solutions How does Tavant leverage big data to help lenders achieve growth?Tavant uses big data analytics to identify new market opportunities, optimize pricing strategies, improve risk assessment accuracy, personalize customer experiences, and predict market trends. Their platform processes millions of data points to drive strategic growth decisions. What big data capabilities does Tavant provide for lender expansion?Tavant offers customer segmentation analytics, market penetration analysis, portfolio optimization tools, predictive modeling for loan performance, and competitive intelligence dashboards that guide strategic growth initiatives. How does big data improve lending decisions?Big data improves lending by analyzing alternative data sources, identifying patterns in borrower behavior, predicting default risk more accurately, enabling dynamic pricing, and providing insights into market opportunities. What types of data do lenders use for growth strategies?Lenders use demographic data, transaction histories, social media signals, economic indicators, geographic data, competitor analysis, customer feedback, and behavioral patterns to identify growth opportunities. How can small lenders compete using big data?Small lenders can leverage cloud-based analytics platforms, focus on niche markets, use alternative data for underserved segments, implement automated decision-making, and partner with data providers to level the playing field.
Mobile Application Security
I have spent 12 years working in Mobility with many mobile platforms one could think of like BREW, Windows, QT, Symbian, J2ME, OEM proprietary mobile platforms, SHP, Android, iOS. It has been an amazing journey. Started with devices with few KB of RAM to devices now running 4 GB RAM and above. All these years and for all mobile platforms there has always been a significant common ground. It was valid then, valid today and will be valid forever – Mobile Security. Many businesses today are adopting mobility to streamline process, increase employee productivity, aiming at rapid growth. Gartner report shows that companies which are adopting mobility have simply increasing their reach by 18%. Mobility is playing such an important role, what starts bothering the enterprises is the security. Gartner reports suggest that about 75% of Mobile Applications are prone to security breaches due to wrong security practices adopted while developing a mobile application. This prediction is alarming and makes it compulsory for the developers to be well aware of the threats and methods to mitigate the risks. Below I am listing some common pitfalls and how I went about alleviating them. 1)     Weaker server side API: While writing one of the web-service using Express and NodeJS recently, I forgot to build security for my API’s exposed for the Mobile Client. Anyone who is aware of the API could exploit it. It made my server immediately prone to a variety of DoS attacks 1 Here I provided the hackers with a plated opportunity for Man-in-the-Middle (MITM) attacks. I immediately patched this up using secure coding practices, limiting API access to authenticated users only. 2)     Susceptible data on the move: While developing the web service I made one more error. I used HTTP exposing my server once again to (MITM) attacks. Most developers believe that just by using HTTPS this problem will be solved but they are wrong. This problem should be solved by using certificates signed by a valid CA, Certificate pinning or HTTP Strict Transport Security. In a typical SSL usage scenario, a server should be configured with a certificate containing a public key as well as a matching private key. I have seen many application developers when using HTTPS will accept all certificates as shown below: SSLContext sc = SSLContext.getInstance(“TLS”); sc.init(null, trustAllCerts, new java.security.SecureRandom()); HttpsURLConnection.setDefaultSSLSocketFactory(sc.getSocketFactory()); Do not trust all certificates and don’t use self-signed certificates. For a good understanding, you can refer documentation2. Payment, banking, and enterprise application developers should make sure that they rely on above mentioned methods for rendering their data safe when in transit. Weak Authentication: Use Digest authentication over Basic authentication. Digest authentication communicates credentials in an encrypted form by applying a hash function to the username, the password, a server supplied nonce value, the HTTP method, and the requested URL. Whereas, Basic authentication uses unencrypted base64 encoding. Basic authentication is generally used where transport layer security is provided such as https. Try to safeguard your API with authentication such as token-based authentications.  Make sure the incoming HTTP method is valid for the session token/API key and associated resource collection, action, and record. For example, if you have a RESTful API for a library, it’s not okay to allow anonymous users to DELETE book catalog entries, but it’s fine for them to GET a book catalog entry. On the other hand, for the librarian, both of these are valid uses. 3)     Susceptible data at Rest: Programmers often believe that no one can have access to their application database. Like in Android, usually application database resides in the /data folder which is not visible to a normal user. Rooted devices easily provide free access to this database. Hackers can also get hold of this data using platform vulnerabilities. Developers can use various encryptions to safeguard data at rest. 4)     Susceptible visible data: What I mean here by visible data is the text, image, any mime type rendered on a screen which a user can see. Sometimes enterprises don’t want their data like emails to be copy pasted, forwarded, cached, etc. Programmers should be sympathetic with such needs and utilize Mobile device management features. iOS, Android and Windows platforms boast of a robust set of MDM features. 5)     In-app vulnerabilities: Native applications on Android and iOS are sandboxed normally giving them a comfortable secure environment. I was once working for a big security company and learnt running a cron application validator or a data validator a good option to safeguard an application against malicious intents. Programmers need to be extra careful when developing on hybrid or cross platforms. These platforms can also inject security vulnerabilities of their own. For eg: hackers increasingly aim for cross-platform vulnerabilities. When using Webview we need to be well aware of this3. 6)     Not using Proguard: Prevent your mobile application from reverse engineering and malicious injection. Proguard should be enabled for all your mobile applications in production. This blog is just an introduction on above discussed points. My aim was to keep it simple and easy to remember. It will soon follow-up with detailed write-ups on each topic. Please feel free to inbox me. References: 1)     https://en.wikipedia.org/wiki/Denial-of-service_attack 2)     https://www.owasp.org/index.php/Certificate_and_Public_Key_Pinning 3)     https://securityledger.com/2015/08/the-challenge-of-securing-rest-apis/ 4)     https://www.google.co.in/work/android/ 5)     https://www.apple.com/support/business-education/mdm/ 6)     https://tools.ietf.org/html/rfc2617
Architecture of a Massively Scalable Distributed ETL System
An Extract, Transform and Load (ETL) tool needs to be robust, scalable, high throughput and fault tolerant. Very much like an e-Commerce transaction system. Designing such a system on a distributed computing backbone can be extremely rewarding, given that mid-size to large organizations might be collecting data from multiple sources and bringing it all together into an integrated warehouse—resulting in thousands of batch and real-time jobs running during the course of a day. For example, retailers collect inventory, sales, finance, marketing, clickstream, and competitor data multiple times a day. But aggregating this data by running ETL jobs, only once daily, can slow down decision-support systems and rules engines, which must feed essential decisions (like dynamic prices) back to the system to control demand. For many e-commerce analytics and data-mining solutions, a slow ETL tool might prove to be a huge bottleneck. While commercial and open source tools help implement such workflows, it is often better to consider a homegrown ETL tool based on good design and distributed-computing principles. Learn how to build your homegrown ETL solution and use a task queue to scale the tool horizontally. Download the whitepaper to read more: http://lf1.me/Ncc/
Video Ads are a Huge Hit with Millennials
In a study identifying major celebrities popular among the youngsters (aged between 18 and 30) YouTube superstars topped the list. Millennials have been found flocking mostly to videos recently. These video sites are reaching more than any other networking media across people within the age group 18-35 (Source:Â Sprinklr.com). Every year the time spent on watching videos is growing by 60%, average video watching on mobile being higher than 40 minutes. This provides a huge opportunity for advertisers to specifically design ads for videos targeting millennials. The advertisers on video channels like YouTube have grown higher than 40% and the big companies are trying to capture millennials on other video sites as well (Defy-sponsored survey in 2014). It is the time spent by millennials on YouTube and Google Video that has resulted on top brands (rankings by Interbrand) spending almost 60% more on these channels than they did a decade ago (around 2005). Although site owners are not explicitly coming out with revenue figures, the available data indicates there is an explosion of demand for video advertisements. Advertisers have found millennials spending substantial time on these sites. Also, Google has said there is a huge growth in revenue from YouTube recently. The influx of advertisements has made many video sites turn their platforms more appealing for advertisers. They created a computing systems and dashboards where marketers can compare effectiveness of video ads with television advertising. A recent survey on millennial women found the main influencing factors for their shopping decisions to be websites, social media and word of mouth. See figure below. Source:Â AdWeek Guided by the huge popularity of video networks, many renowned brands on television and the internet are coming up with new video channels. They are trying to cover topics that interest the age group of 18-49 mainly, some specifically catering to the audience looking for answers to questions continuously. For brands trying to capture this vibrant and media-proactive group of millennials, video advertisements offer immense potential. Using a comprehensive campaign management solution, you can design your content and run it across the video networks to find maximum conversions happening within a short time. Make sure your products and your ads match the intellectual and emotional worlds of these millennials, and your job is almost accomplished.
Automated Mortgages: Just a Click Away
 Mortgage lending processes are becoming quicker and user friendly. Thanks to web and mobile technologies, loan applications and procurements are entirely online and do not involve any human interaction. Online-only originators like GuaranteedRate.com, QuickenLoans.com, and Sindeo.com have websites and mobile apps capable of doing everything that the sales agents and mortgage brokers traditionally did. The uncluttered user interfaces and intuitive algorithms can guide all applicants smoothly through the entire process. Looking at the times ahead, every mortgage firm is trying to offer its services online. Many firms are struggling with implementation of their digital versions, and yet, they are wondering why customers are not embracing them widely enough. It’s all very simple: if, at all, you choose to go online, do it right. The last thing you want is embarrassment in a new venture. Here’s the right way to go online with mortgage lending: Wondering what the smart online way is? Self-service online models have intelligent features, and they are designed to keep absorbing more intelligence day by day. Agile development of your online services should make it increasingly user friendly, so that no applicant feels like abandoning the process half way. Let’s see the key differentiators of a good mortgage-lending website: Assistance in planning: Built-in calculators should help mortgage customers try various loan plans, durations, and repayment options, and finally select what best suits their situations. Data collection: a) The system should be able to collect all necessary data, and show up only the necessary data for an applicant. What data is needed depends on the type of loan selected. Difficult or confusing fields should be supplemented with tips and suggestions. b) Photographs, scanned images of signatures, identity cards and other relevant records should be submitted online. All that should be archived with relevant meta tags that help in identifying and retrieving them later. It is important to find the latest credit reports of applicants. Web widgets should be connected to credit-rating agencies and they can fetch the report of the applicants even as the applications are being filled out. You should be able to provide instant responses to applicants as to whether they qualify or not. Long verification processes only discourage mortgage customers. Predictive analytics about the probability of defaults, delayed payments, or non-payments should enable you to offer risk-adjusted rates and nullify the potential risks. Normally, the entire process should be completed and the amount disbursed within 15 days. All the actions need foolproof security and privacy around them. Besides that, personal financial management tools are a great value addition, as they can help customers make wise decisions in a convenient way. The right online implementation of your mortgage banking system can save you from high payroll expenses. Cost and time-efficiency is of utmost importance, and going online should give the client a happy experience. To thrive in a complex and constantly evolving business environment, firms need to improve their systems constantly and maintain the habit of innovation. FAQs – Tavant Solutions How does Tavant make automated mortgages accessible with simple click-based processes?Tavant provides intuitive mortgage automation platforms with one-click applications, automated data verification, instant pre-approvals, and streamlined digital workflows. Their user-friendly interfaces enable borrowers to complete mortgage applications with minimal clicks while sophisticated AI handles complex processing in the background. What automation capabilities does Tavant offer for mortgage lending?Tavant offers automated income verification, property valuation, credit analysis, compliance checking, document processing, and decision-making capabilities. Their platform can process up to 90% of mortgage applications automatically, requiring human intervention only for exceptional cases or complex scenarios. How automated can mortgage processing become?Mortgage processing can be highly automated, with modern systems handling application intake, document verification, credit analysis, property valuation, compliance checking, and initial underwriting decisions. However, complex cases, regulatory requirements, and quality control still require human oversight. What is one-click mortgage approval?One-click mortgage approval refers to streamlined digital processes where borrowers can receive instant pre-approval or preliminary decisions with minimal input, leveraging automated data verification and AI-powered risk assessment to provide immediate feedback on loan eligibility and terms. Are automated mortgages safe and accurate?Automated mortgages use advanced AI, machine learning, and data verification systems that often provide more consistent and accurate decisions than manual processes. They include robust fraud detection, compliance checking, and audit trails while maintaining human oversight for quality assurance and complex cases.