Get More Out of Your Field Service Operations with Service Analytics
Manufacturers strive to differentiate themselves in an era of connected products and services. And one way to make this happen is through improved service operations. With field service analytics, contractors often on location to install, maintain, or repair equipment, systems, or assets, can enable higher customer satisfaction and profits. From Cost Center to Competitive Advantage The sheer volume of available information can do wonders for a field service operations. The data collected by a field services organization via its fleet and workforce management technology (as well as how a business uses this data) can set it apart from the competition. Warranty Management is an example of this, which has traditionally been regarded as a cost center. Manufacturers are beginning to recognize that (when combined with the right platform, technology, and partner), the volume of data accumulated can actually be used to gain a competitive advantage. Why Service Analytics is Gaining Momentum With more connected equipment and sensors than ever, today’s manufacturers have access to more potentially valuable data nuggets. A study by the Aberdeen Group found that field service organizations that adopted analytics technology saw their service profits increase by 18%, customer retention rates by 42%, and SLA performance by 44%. Let’s look at some of the ways this can happen: Ways in Which Service Analytics is Impacting Field Service Field Tools & Knowledge Repository Field Service Technicians are constantly under pressure to provide a solution or repair as soon as possible. They are sometimes given very little time to understand the nature of the problem they are called in to solve. Field service management technology provides tools and access to knowledge repositories to field service personnel on the job, allowing them to troubleshoot more quickly. With field service technicians having access to information and insights, jobs get completed faster and result in a seamless experience for the customer, dealer, and manufacturer. Machine Failure Prediction Service analytics can help make an impact on reducing machine downtime and,as a result, project downtime for your customers. Imagine being able to send an email notification to the dealer, telling them of a 40% probable engine replacement. With parts identified and ready to be shipped directly to the dealer, on their agreement. Service analytics offer manufacturers real-time actionable insights to increase machine uptime, reduce part failures, and save on cost and effort. New Opportunities Manufacturers are drowning in data as IoT devices and sensors, connected machines, and other technologies proliferate. With the addition of smart learning models becoming more accurate, manufacturers can now use service analytics to drive decision-making. The integration of field service analytics with the sales CRM system enables product cross-selling and up-selling. This, in turn, may reveal opportunities to boost aftermarket revenue. Integrated View Service was frequently viewed as an afterthought by many manufacturers, with service prices discounted or given away to promote product sales. Sales teams are pressured to sell products or equipment with little regard for the service team’s ability to execute. This typically leads to a compromised customer experience as the service leader begins to reallocate resources to meet customer needs. These operations can be handled more efficiently with service analytics. Field service operators can get a complete picture of all assets, products, and customer information in one location. As a result, they are better able to advise customers, resolve issues more quickly, and increase productivity. The Future of Field Service is Seamless With an increasing demand for personalized, actionable customer support, service analytics can play a significant role. An entire ecosystem surrounds the customer, with field services is at the forefront. Putting analytical tools in their hands can empower personalized and quick service resolution. By using field service data, businesses can create more lifetime value for their customers while improving business processes and practices across the service life-cycle. The only question that remains is, how soon?
Code-based versus Low-Code/No-Code test automation solutions: Which one to Choose?
Concerns about the quality of software test automation solutions are growing every day, and we face an array of challenges in addressing them. One of the challenges is that we have several test automation solutions to automate our test cases (Web, API, Mobile, etc.). Some test automation solutions in the market require exceptionally good programming knowledge, whereas for a few, intermediate programming knowledge is enough, and we have a few where we can automate things with almost no coding experience. To top it all, there is pressure to deliver faster to the market What is a Code-based Test Automation Solution? Coded solutions, like traditional automation systems, necessitate a very trained workforce with an in-depth understanding of certain tech stacks. A team capable of writing custom code from scratch is required in this case. These solutions are intended to be developed and used by technical users such as SDET, developers, etc. What are Low-Code/No-Code test automation solutions? Low-code test automation solutions allow users to automate tests with some or little coding skill set/experience. Most of the automation testing happens without actual programming. Typically, the most used features/utilities are already built-in through GUI so that users can select the required actions and combine them into a sequence. However, coding expertise is necessary when achieving anything complex to interact. No-code test automation solutions allow users to automate tests in the application with almost no coding knowledge and experience. These solutions are intended to be used by non-technical users such as product owners, business analysts, etc., where we would mostly need to select, click, enter text, scroll, or drag and drop. Difference Between Code-based Vs. Low-Code/No-Code Test Automation solution Category Code-based Solutions Low-Code/No-Code Solutions Coding Need High Low or None Complexity Overly complex Less complex Flexibility Extremely flexible Less flexible Primarily Servers Technical users (Developers and SDET) Anyone who is a part of the project can contribute. Security Concerns Quite Low High Execution Speed Low to Medium (depending on the test case count and test steps). High Automation Design Robust Tightly coupled In today’s world, where new test automation solutions are frequently released, enterprises are looking for ways to expand and accelerate their software delivery processes. Even low-code/no-code solutions now have all the necessary built-in qualities that make them simple to implement with little to no coding knowledge/expertise. The question is if it is winning hearts! Code based Solutions Pros Cons Design and workflow flexibility – Design in accordance with your company’s existing workflows, expertise, and skillset. Ease of Use – Understand your intended users and their skill sets. Create the framework to match up. Need a new feature? – Decide the priority of that feature and implement it, what features your framework should have, and to what extent each feature should go. Something not working? – Find the root cause and go ahead and fix it. Reporting or Dashboard requirement – You have complete access to your execution results and can create whatever report/ dashboard format you want. Pricing – The long-term cost-per-run is much lower than any low-code/no-code test automation solution. Time to build – Creating a stable solution takes time. Depending on the AUT, it could take a lot of time. Need to provide your own DevOps/SecOps ecosystem No Outside assistance – When you develop your own solution, you have only yourself to hold accountable when things go south. Low-Code/No-Code based Solutions Pros Cons Almost no ramp-up time, it is a ready-made solution – no need to build your own. No maintenance for hardware and no need to involve DevOps/SecOps. Outside assistance – You have someone to assist you (based on your support contract) in case you have any queries or need help. Limited scalability- Need a new feature or integration with another tool? – the solution does not yet support a feature. You must wait for a feature and support ticket (you do not have any control over deciding priority) Support wait time – Response time depends on your subscription. It can be anywhere between minutes or hours to days at times. Pricing – it varies, but in the long-term, cost-per-run is significantly higher than using any traditionally built code-based test automation solution. Limited customization- Inflexible reporting/dashboard – Most of these solutions will not give you a choice to have “out-of-the-box” customization. Have any queries? – You are dependent on the solution maker to help you out. Helpless- Waking up after a few months of solution implementation to realize that you cannot increase automation coverage due to the solution’s lack of support. The automation solution company is sunsetting the tool due to any reason.” Since each organization works on different objectives, to deal with the question of which approach to use, here are the top few items to mull over: Who (Tech/Non-Tech/SDET, etc.) will create and maintain these automation test suites? What is getting automated – APIs/Web/Responsive/Desktop/Mobile apps? How complex are the test cases and business situations to be automated? What is the skill set/expertise within the team for creating and maintaining this test automation suite? Is this a new or an existing project where we have already done some automation? Is the test automation suite meant to be integrated with other tools like Test Management, bug tracking, CI/CD, etc.? The test automation suite is meant to be executed at what size? What is the budget and time duration required to complete this project? Final Thoughts It is imperative to realize that there is no silver bullet. As shown above, each choice has its own pros and cons. The key to success is choosing the right solution that balances your team’s skill sets and expertise and simultaneously meets your organization’s objectives. Until then, happy test automation!
7 Ways to Get the Most Out of an Industry Conference
Conferences play a significant role in just about every industry by providing a setting where like-minded people can connect through networking, share ideas from different aspects of their work, and learn from each other. Although conferences were forced to adjust how they function as a result of the Pandemic, year 2022 saw a return to normalcy as a more traditional business schedule resumed. Tavant took full advantage of the departure from virtual events and excitedly jumped right back into the fray. Our FinTech team represented Tavant at 25 industry conferences this past year. We know times have changed, and because of that, we want to share our top 7 tips to help you be as prepared as possible for all the conferencing that will take place in 2023. 1: Preparation is key. Preparing is an essential tip for any conference attendee, rookie, or veteran. Having an idea of what information you are looking for and reviewing the agenda allows you to plan accordingly, so you don’t waste any time and can get the most out of the event. Finding out who’s going, if possible, and figuring out with whom you want to connect enhances the possibility of you creating new connections. Also, it is important to make a plan that works for you. If you are better at networking and more of a social butterfly, maybe setting up more meetings or going to frequent get-togethers will help you get the most out of the conference. Regardless of your sensitivity to social interactions, it wouldn’t hurt to have an “elevator pitch” ready in case the opportunity presents itself. Lastly, make sure you bring any items you might need, like chargers, business cards, or anything else you might want to help you get through a long day. No one wants to leave a conference with their strongest memory being a missed opportunity. 2: Gold star attendance. This may seem like a no-brainer but attending the sessions is one of the best opportunities that you will have to learn new aspects of the industry and potentially what your competitors are up to. Take notes, bring a recorder, or whatever you need to do to help you gather and retain as much information as possible to benefit you and your company. 3: Networking is essential. Networking is a big part of conferences – it leads to new relationships and opportunities that can have incredible value for your individual professional career or the success of your company (preferably both). If you have been to conferences before, reach out to past or potential colleagues in the industry beforehand instead of wasting time trying to find them during the conference. This will save you time and allow you to take in more of what the conference has to offer. Try to connect with a speaker if you can, but this might require a backstage pass and some luck. Don’t forget: a lot of networking is done outside the venue, so attend those after-parties and other extracurricular events in order to maximize success. After the conference, reach out and chat with those you met to help build those new relationships. This allows the connection to grow and shows your appreciation for having met them, which feels good for whoever is on the receiving end. 3: Be active on social media. One aspect that people might overlook with everything going on during a conference is being active on social media. LinkedIn is the easiest way to connect with like-minded professionals and stay in contact with people. Twitter is also a solid option depending on how public-facing your industry or company tends to be. Scrolling through the newsfeeds of your relevant social media accounts before a conference may give insight into who is attending and the topics that might be covered. Furthermore, checking for any conference-specific hashtags is always a good idea. This will also make it easier to anticipate and understand the thoughts and interests of those attending, specifically those who are excited enough to post about it online and share it with their colleagues. If you are short on content, try diversifying the type of content you share on social media by taking more pictures and videos while at the conference, particularly of yourself with people you’ve met. Remember: a photo is a lasting memory, making it a great way to connect with people and develop strong professional relationships. Not to mention, if you are one of the first people to post about a conference you are attending, then you are far more likely to maximize the size of your audience for that content, thereby bringing more brand awareness to your company. Be sure to ask for the consent of all parties involved before taking pictures to avoid any unwanted photographic attention! 5: Stay organized. Conferences can be overwhelming – there is lots of information being exchanged with not always enough time to process everything. This is where staying organized comes into play. Having a well-thought-out organizational system will help you stay on top of your schedule during the conference and make it easier to keep track of names, session notes, business cards, and other information after the conference has finished. Whether it’s labeling information properly right away or taking time to collect everything necessary at the end of the day, the small efforts you make could be the difference between new opportunities and missed ones. Whatever you come up with, make sure you have a plan that works for you. 6: Divide and conquer. While this applies more if you attend a conference with coworkers, it is essential that you don’t all attend the same sessions, as it limits how much your company could have gotten out of the conference. Instead, spread out and take advantage of as many opportunities as possible to learn more and connect with more people. Also, make sure to set up a meeting time with your coworkers before and after the events to plan or recap the day. Divide and conquer
QAOps – Shift in the QA paradigm
What is it? Is it a specialization or a new team role? – The answer is No. QAOps, also known as Continuous Quality (CQ), is a process of including quality engineering (QE) in Continuous Integration and Continuous Delivery (CI/CD). So, instead of being an isolated process, software testing is integrated into the CI/CD pipeline. It requires solid collaboration between the QA team, development, and IT operational teams to build a highly effective and cohesive process. In contrast to DevOps, QAOps emphasizes QA engineers’ problems and the importance of integrating software testing into the DevOps workflow. How to Implement QAOps: We can successfully implement QAOps by implementing Automation Testing, Parallelization, Scalability Testing, and Integration of Dev and IT Ops with the QA team. Let us look at each of these briefly: Automation Testing. Automated testing forms the base for the QAOps. It involves performing tests with the help of scripts, tools, etc., to certify test cases by repeating pre-defined actions that require very minimum human-centric efforts. To make it happen, SDET engineers must work on building a solid automation framework. Once we have an automation framework in place, QA engineers select the tests that can be automated which saves time and tests functionalities well. QA engineers should focus manual effort on testing only those functionalities that cannot be automated and/or exceptional testing use cases that are not good candidates for automation. Although it is impractical to automate every test due to tool and technology stack limitations, we should strive for high automation coverage by automating as many tests as possible. The best way to approach QAOps is to integrate automation testing into the CI/CD pipeline. Parallel Testing (Parallelization) Parallel testing entails running multiple tests concurrently rather than sequentially. It allows you to run tests in various browsers and platforms at the same time, drastically reducing testing costs, effort, and time. In the QAOps framework, your tests should run quickly because if the execution happens slowly, then it will impact the entire delivery process. We should run our tests parallelly instead of sequentially to achieve speed. Additionally, it improves test coverage. Parallel testing necessitates a good infrastructure to run the tests concurrently, but the results are impressive with no impact on the delivery pipeline. Scalability Testing Test scalability comes into play once the application goes live and begins to gain popularity and gives you the desired results, which is when you must scale it judiciously. When the application scales, the testing of that application must necessarily scale as well. Scalability helps in determining the application’s performance under varying load conditions. With the result of scalability testing, we can conclude the response of the application with respect to the differential loads. As a standard QAOps practice, the QAOps team must have access to the scalable infrastructure and framework to perform testing and increase the speed of tests when needed. Integrate Dev and IT Ops in QA The final and most crucial step toward the framework’s success is incorporating all QA activities into the CI/CD pipeline. Applying a shift-left testing approach to integrate the QAOps framework can help to avoid launch delays. When the QA engineers collaborate with Developers and IT Operations teams, it helps in testing new features without any lag from the team. This collaboration between the different teams makes the development and testing process more effective. Here are a few responsibilities (not only limited to) of an engineer who performs QAOps work: Building an automation test plan Developing and maintaining the QA automation framework and scripts Configuring remote automated test execution (including parallel run) Reporting and distributing the results via communication channels such as Slack, MS Teams, email, etc. Communicating and collaborating with the Operations and Development team (from development start to deployment into live environments) Few tools/technologies stack that the QAOps team use for different streams to add value to the QAOps process: Functional Automation: Selenium, Appium, Cypress, Playwright, Protractor, WebdriverIO, and others. Performance Testing Tools: JMeter, LoadRunner, NeoLoad, and others. CI/CD Tools: Azure DevOps, AWS CodeBuild, Jenkins, Git workflow, and others. Cloud infrastructure: Azure, AWS, Docker, and others. Remote browser execution: BrowserStack, SauceLab, Pcloudy, and others. Reporting: Extent, Allure, Report Portal, and others. Benefits Of QAOps: As this process demands collaboration between the QA, Development, and IT Operations Teams, it allows them to enhance their skills in a variety of areas. As the QAOps process abides by the shift-left testing approach, this accelerates issue fixes early without sacrificing time and deploys the application sooner. CI/CD testing allows issues to be identified at an earlier stage, providing a reliable application with the utmost quality. Because testing occurs on a continual basis, the chances of an improved customer experience increase since the application quality and delivery are improved. IT operation team avoids any delays by having the QAOps operations run constantly. This permits the QA team to test new apps/features without being slowed down. In Conclusion QAOps is critical for teams that automate their CI/CD pipelines because it emphasizes speed without sacrificing quality. Once implemented, this process in the CI/CD pipeline saves time and money on testing products. The rise of QAOps highlights the problem that quality is frequently overlooked in software development.
Why Data Modernization Should be a Priority for all Lenders and Bankers
Before the pandemic, mortgage companies were already under attack from fintech and nontraditional lending organizations. Most businesses didn’t have much to say about how the changing competitive landscape would impact their lending programs, so it seemed like the mortgage and banking industries were happy with how things were going. They mainly relied on the relationships they had built with their customers. However, in a post-COVID world, digital technology has completely transformed the financial industry. Mobile banking has gradually replaced brick-and-mortar banking. The cadence of transaction processing has shifted from periodic batching to real-time processing, posing a significant challenge to financial institutions; legacy IT systems are obsolete and incapable of providing a real-time digital banking and lending experience. This is challenging because customer expectations are not just sky-high – they are stratospheric. The demand for consistent real-time digital lending has made the financial services industry more competitive, and banks and fintech mortgage lenders are struggling to meet the needs of modern customers with their legacy, rigid IT systems. Data Modernization: The Foundation for Digital Transformation Data modernization, a process of migrating siloed data from legacy databases to modern cloud-based databases, enables organizations to be more agile by eliminating the inefficiencies, bottlenecks, and unnecessary complexities associated with legacy systems. Until recently, the processes for implementing loan origination hadn’t changed for decades. In many organizations, the process is still “informal” and carried out manually, often with paper documentation sent from department to department. The pandemic has revealed flaws in nearly every company’s data management practices. Organizations have recognized the urgent and critical need for a modern data infrastructure that manages data to make it highly accessible, practical, compliant, and valuable. Fintech mortgage lenders benefit from near-term cost savings and powerful analytics that extend personalization and optimize forecasting when they have a modern data backbone and digital mortgage solutions. As a result, mortgage companies have started shifting their focus from optional to critical digital transformation. The first step toward modernization is to create automated flows for this overall process, employing RPA, artificial intelligence (AI), or machine learning (ML) technologies to reduce human involvement, reduce errors, and automate adjustments where necessary, all in support of human activity where desired. Second, reviewing the data needs for this process and making the data better and more complete can help people make better decisions and grow the credit market. Data becomes even more powerful when it is smartly combined with intelligent process automation (a combination of Robotics Process Automation (RPA) and Artificial Intelligence (AI), or more precisely, a mix of tools and techniques such as OCR, speech recognition, Machine Learning, and Natural Language Processing (NLP) techniques. Loan forgiveness and mortgage forbearance are not new elements of loan servicing, but those areas have reached a scale hitherto unknown in the mortgage industry. Therefore, additional data and analytics are needed to make better decisions about loan modifications and their potential impact on the business’s risk and capital. AI analytics will only be helpful if the mortgage companies have the additional data to make a meaningful decision. A real-world example of the value of alternative data came from 2016 when severe flooding affected homes owned by a regional bank. Rather than waiting for homeowners to default on flooded or destroyed homes, the bank enlisted the assistance of a mapping and analytics firm to confirm flood-stricken homes against the bank’s mortgages. As a consequence of this, the bank was able to make use of the data and achieve a significantly improved comprehension of the threat that this event posed to its portfolio. And the organization was given the tools it needed to proactively reach out to the customer to arrange forbearance or provide other assistance to the homeowner. Why is Data Modernization an opportunity for lenders and bankers? Process efficiency: Reducing the “time to yes” The underwriting process’s inefficiency occurs in preparing the credit proposal, outlining detail for the credit committee what all these risks are, and calculating their likelihood and impact. Automation, data insights and analytics, and underwriting platform-based digital mortgage solutions are key levers that significantly impact the underwriting value chain. These technologies influence risk assessment and proactive risk monitoring and thus aid in risk prevention. Next-gen modern technologies automate manual processes and integrate legacy applications, such as policy administration systems, to eliminate information duplication. Other interventions, such as agent/customer portals, intelligent workflow, and real-time process visibility, allow agents and underwriters to work closely together. Subsequently, it reduces sales cycles, bringing “time to yes” down to five minutes. Raising the standard: transparency, consistency, and auditability Modern loan origination systems help standardize the credit underwriting process by ensuring that the best method for managing operations is used. Individual lending organization differs in some details, but most follow a consistent pattern in credit underwriting, and this process can be improved if everyone involved uses the same platform. Having instant shared access to the information required to complete the underwriting process improves efficiency while also increasing transparency and lowering the operational risk of critical information remaining in the hands of a few key personnel. It’s all about the data Banks and financial organizations generate massive amounts of data, and the vast majority of them are terrible at managing it. Data can now be found everywhere. Data modernization allows for more informed decision-making by reliably extracting data from various disparate systems. It facilitates the identification of high-value data combinations and integrations. It also enables people to identify opportunities at the moment quickly, allowing them to capitalize on something that would otherwise have gone unnoticed, eventually generating more revenue. Furthermore, data modernization reduces the risks associated with data security and privacy compliance. In its process, it looks for sensitive information so that you can limit user access to data in a precise and more efficient way. How can your organization take advantage of digital lending modernization? Consumers have shifted dramatically toward online channels during the pandemic, and businesses and industries have responded in kind. To help financial institutions achieve operational efficiencies, credit process optimization and automation of low-end credit processes to
How Fintech Automation is Changing the Face of the Lending Industry
Understanding the Changing Landscape in the Mortgage Industry The mortgage industry is under enormous pressure to perform in the face of fierce competition, increased due diligence for loans and borrowing (due to the COVID-9 pandemic and its economic ramifications), crunching timeliness, and ever-growing data. According to Gartner, a human error in the financial sector results in 25,000 hours of pointless rework per year, costing up to $878,000. Our only stumbling block, we believe, is fintech automation. The global fintech industry is estimated at $65,88,780mn in 2021 and is projected to reach $1,66,52,680mn by 2028 at a CAGR of 13.9% for the forecasted period. Fintech automation can define even the most unstructured data and support lending process automation to deliver a resource, cost, and time-efficient process. Mortgage and lending have already been reimagined using automated technology such as chatbots and digital assistance. Given the massive amount of data, the need for real-time, data-driven strategies for effective customer UX and UI, and loyalty retention in the mortgage lending industry, this shift is inevitable. Customer onboarding to Know Your Customer (KYC), legal processes, due diligence, credit checks, and form fill-ups have previously been observed to require 50-75% of the onboarding process cost. However, well-integrated lending automation, combined with optical character recognition and natural language processing, assists mortgage lenders in shortening the lending cycle and lowering costs. “Generative AI enables bank CIOs to offer technology solutions to the business in pursuit of revenue growth,” according to Moutusi Sau, VP Analyst at Gartner, “while autonomic systems and privacy-enhancing computation are long-term solutions that provide new options for business transformation in financial services.” We can cite several reasons for the rapid adoption of automated technology as a core business process in lending process automation across verticals. Customer expectations have risen dramatically in terms of complete transparency, customer-centric, highly personalized interactions, and maximum participation. As a result, maximum fintech implementation can be seen in customer relationship management (CRM), accounts payables, mortgage automation, risk management, payment arrears, reconciliation requirements, insurance premium calculations and settlements, back office, and front office, among other areas. Aside from process integrations, the lending industry requires fintech capability to eliminate cyber fraud risks and identity thefts, as well as a digitally secured infrastructure to protect customer data related to mortgage and lending. Customers feel empowered with access to secure omnipresent, omnichannel, digital transactions, and payments when lenders provide mortgage lending automation with simplified tasks. Self-service in CRM with chatbots and instructive guidelines, according to Deloitte’s Finance 2025 report, creates a better customer interface. Furthermore, mortgage lenders can use fintech automation to absorb data from borrower application forms, extract information from borrower payroll applications, and automatically upload loan data into respective portals. More importantly, automated technology enables credit decision-making systems and microdata inspection with low error rates for seamless loan approval and disbursement. Considering this massive shift, organizations have been striving hard to develop deeper hyper-automation processes or at least implement partial automation, machine learning, and artificial intelligence to attain optimum operational efficiency. Categorically, RPA tools have also matured from traditional desktop automation to enterprise solutions. This has profusely helped in managing complex processes like strategic decision-making, cognitive learning capability, user interfaces, and so forth. Fintech automation is undeniably booming, and competition is heating up. Companies are planning both organic (diversification, geographical expansion, etc.) and inorganic (mergers and acquisitions) strategies to gain a competitive advantage and remain sustainable. Gartner estimates that banks and investment firms will spend $623 billion on technology products and services by the end of 2022. The major investments will be in generative AI, autonomic systems, and threat-nullifying technologies. Final Thoughts During our research, we have found that CEOs across the globe believe that cloud-based ERP, cognitive technologies, and hyper-automation will radically simplify lending processes and accelerate the lending industry as a whole and not in silos. In fact, hyper automation is already in its nascent stage of enterprise adoption. Apart from this, the banking and investment services will also witness larger use of generative artificial intelligence, generative adversarial networks (GANs), and natural language generations for fraud detection, predictive analysis, synthetic data generation, artificial intelligence-backed follow-ups, and risk-factor modelling. With the help of algorithm-driven and interactive AI and robots, new service models will emerge. This will not only diversify the financial workforce, but will also link the entire organization into a real-time, digitally connected workplace. What’s Next? Tavant’s consulting-driven approach to automation helps mortgage lenders and banks significantly improve productivity and enhance customer experiences using our deep automation and domain expertise. By combining the power of industry tools and accelerators, we drive organization-wide transformation through RPA, ML, and AI to solve your most important business challenges. To learn more, visit us here or reach out to us at [email protected].
Realizing the Goal of Fully Automated Lending
Borrower expectations are still not being met by the mortgage industry. While many lenders have provided a seamless mortgage application experience by digitizing the front-end platform, the industry’s digitization remains incomplete. Many origination and servicing processes remain slow, manual, labor-intensive, and fragmented, making them vulnerable to disruption—mortgages close in 51 days on average, which is far too long in today’s fast-paced world. Underwriters and processors do not have the tools to complete their tasks efficiently and effectively. The mortgage industry has been embracing technology to streamline the mortgage application process to make the consumer experience smoother and faster. Touchless lendingTM is quickly becoming the industry’s standard operating system for large-scale automation of mid and back-office mortgage operations. How does Touchless LendingTM change the game? Despite the amalgamation of multiple technologies into the mortgage origination process, the cost of originating a loan has steadily increased over the years, reaching a peak north of $10,000. Tavant wanted to create a product that was completely, directly, and only focused on solving this problem. The vision of Touchless LendingTM is to eliminate the many humans-in-the-loop embedded in the mortgage process, to phase out the rivers of paper that flow through each loan in the application intake and decisioning process, and to knock out the need for multiple thrashes and iterations between the borrower, loan officer, processors, and underwriters, which result in an increased cycle time of anywhere from 45 to 60 days to close a loan. Touchless LendingTM targets these underserved middle and back-office associates, allowing them to make a clear-to-close decision in as little as five days, handle five times as many mortgages at once, and save more than 75% on processing and underwriting costs per mortgage. To solve the complex problem of using a machine to do the work of a senior processor and an expert underwriter, the Touchless LendingTM platform prudently employs AI and Machine Learning techniques. We combine computer vision and natural language processing with procedural rules processing to provide the best technical solution for straight-through processing, automated loan decisioning, automated loan processing and automated underwriting. The automated lending platform is LOS-independent and will work with any CRM and POS platform in the mortgage industry. The platform employs Digital Ledger Technologies to ensure that all operations on loan are immutable and can be tracked from its inception to its closure/funding, reducing repurchase risk and allowing investors to perform their due diligence when purchasing the loan more efficiently. Delivering an Exceptional Mortgage Customer Experience Touchless LendingTM is an AI-powered lending-as-a-service platform that offers straight-through mortgage processing and automated underwriting as part of the mortgage manufacturing pipeline from start to finish. Instead of relying on physical documentation and manual data entry, loan officers, processors, and underwriters use Touchless Lending’s optimized workflows to engage with data and make decisions faster. This one-of-a-kind automated mortgage software solution enables lenders to originate more mortgages more quickly while lowering costs and repurchasing risks. Touchless Lending seamlessly integrates with your existing systems, such as CRM, POS, and LOS, and automates the loan production process. Each service provided by the Touchless LendingTM platform is unique in that it includes embedded innovation that provides a true business and operational lift to that service. Touchless Documents, for example, uses a multi-OCR strategy to extract the best possible classification and data extraction from a paper document via an intelligent selection among a network of best-of-breed OCR providers. From Chaos to Order: A Perfect Mortgage CX Strategy and a Boon for Lenders First, lenders do not need to purchase the entire end-to-end platform to gain and lift their mortgage manufacturing pipeline. Individual service endpoints for Document, Income, Credit, Collateral, Asset, Title, Multi-Investor, and Fraud Analysis can be consumed independently through the platform’s API Store. Second, Touchless LendingTM services can be integrated into the Lender’s ecosystem in days or weeks rather than months, resulting in immediate benefits and an impact on the Lender’s cost and cycle times. Third, Touchless LendingTM provides: A 77% cost savings for underwriting and processing. A 4.5-fold increase in underwriting to handle more mortgages at once. Clear-to-Close decisions in as little as five days to a week. Touchless LendingTM has resulted in an 11% increase in total annual gains for lenders and significant savings in operational costs. Touchless LendingTM can accomplish this by reducing process time due to improved quality and digital loan files, lowering document processing costs, gaining warehouse line interest savings, gaining GSE interest rate arbitrage, and maximizing appraisal waiver utilization. Reinventing the mortgage customer experience: now more than ever, mortgage lenders need to focus on delivering a superior online customer experience. Lenders value the quicker time to product deployment and the seamless/intuitive integration into their existing workflows and business processes. The ROI is immediately observable and tangible and can be demonstrated through multiple real-world deployments of Touchless LendingTM services. The Touchless LendingTM platform includes an optional Underwriter’s Workstation, the most user-friendly and advanced workstation for underwriters available in any offering on the market today. Data visualization techniques, combined with AI and Machine Learning-driven insights from the borrower’s and property’s profile and characteristics, provide the quickest path to comprehending a loan’s story and thus the quickest path to loan decisioning. Although the Touchless LendingTM platform initially focuses on automating the mortgage processing and underwriting lifecycles, the platform’s goal is to automate anything and everything that can be automated in the path from the borrower’s post-application submission all the way to its destination of becoming a closed or funded loan, including post-closing activities. What’s Next To know more about Touchless LendingTM, reach out to us at [email protected] or visit us here. FAQs – Tavant Solutions How does Tavant enable fully automated touchless lending?Their AI-powered platform processes applications, verifies documents, assesses credit, and makes decisions without human intervention, integrating with multiple data sources to complete the lending process digitally. What percentage of loans can be processed through Tavant touchless lending platform?The platform can process 80-90% of standard applications without intervention, flagging only exceptional cases for manual review. What is touchless lending?A fully automated loan process using AI, machine learning, and automated workflows to process applications,
CI/CD and Security Testing Integration
Introduction CI and CD = Continuous integration & Continuous delivery OR Continuous deployment. CI/CD is the modern-day software development process in which we can release updates at any time in a sustainable way. The code changes are made frequently and dependably based on customer requests and the sprint life cycle. A CI/CD pipeline, popularly known as the DevOps pipeline, builds up code, executes tests (CI), and wisely deploys an updated application version into the following environment. It also ensures that code changes being merged into the repository are efficient to deploy into the live environment to meet the final goal, i.e., ship software with swiftness and effectiveness. The Pros CICD is a low-risk option – as the process is completely automated. There are no manual interventions for setup or even config changes. Releases can occur in defined frequencies and with the client’s feedback. So, this ought to be a faster & optimum way. Smaller, more recurrent software releases are less disruptive and are easier to troubleshoot or roll back in case of any problem. The process with a structured manner increases productivity; a product will be released independently of other objects, and in the case of multiple series of code- we can release changes independently. This will increase development effort with productivity. A CI/CD pipeline allows teams to analyze builds and test results in detail, leaving little room for last-minute bug surprises. The Cons Team dependencies – Infrastructure, including servers, could be managed by different teams, and when the need arises to access those, it can cause unnecessary delays. Thus, all groups need to be well coordinated with each other all the time. Procedure orientation delay– If defined for any pre-approval process in a project, like no direct access to the infrastructure, it can sometimes delay troubleshooting. New skill sets must be learned – Multiple tools to be used and vendor dependency on those require people with a different skillset in your team. This demands a severe intellectual investment to learn these tools. Why do we need to infuse security validation in our CI/CD pipeline? Continuous integration and Continuous delivery are about speed, repetition, and automation. Development and QA teams are constantly under pressure to deliver releases as fast as possible – provide any new feature(s) or fix the critical bug(s) or an enhancement. But the need for speed repeatedly ignores the importance of security testing, which leaves you at risk of failing to secure your application. Vulnerabilities or flaws found in the live version of an application can cause a breach of confidentiality and expose the software to malicious activity, which costs time, money, and resources to fix and eventually will delay future releases. Integrated security testing makes life simpler for software development teams. That is why DevOps teams habitually embrace the concept known as DevSecOps, which promotes security integration into core DevOps practices. To lessen the chances of vulnerabilities going unobserved during the SDLC, all organizations must add security testing to their existing CI/CD pipeline. Undoubtedly, adding security checks will initially slow down your development cycle. Still, we all need to understand that these steps will improve the security of your organization’s CI/CD pipeline and adds another layer of oversight to ensure security for the end-users. Velocity is the key for every business, where security testing integration is a terrific cream over CI-CD. Thus, it is important to introduce security best practices throughout the build/release pipeline. Conclusion: It is not a secret that security is hard to get right. Still, security is the key in this technologically fast-moving world; therefore, performing security testing is no longer a preference. It should be performed frequently, especially with all critical releases, and should be added to the build/release pipeline for top results. With strong CI/CD security in place, teams can find and fix security issues without notably slowing down the pipeline flow or having to delay/roll back releases. Securing your CI/CD pipelines at every stage and environment that comprise the pipeline should be a priority for any organization that embraces DevOps.
Pinch me… Dreamforce 2022 is back!
Is it just me, or does this year’s Dreamforce feel like a much-anticipated reunion? Part tech conference, part homecoming is how Salesforce describes one of the most anticipated technology conferences this year. And after three years of online interactions, it’s no surprise that Dreamforce 2022 is generating tremendous excitement amongst the technology crowds. But enough about everyone else! Here are my Top 5 reasons I’m looking forward to Dreamforce 2022: Everyone who’s anyone will be there. Like on a summer beach after the exams, the crowds are expected to be everywhere. Three days of networking opportunities with over 30000 people – meeting new leaders and connecting with old friends. That’s a lot of handshaking, so don’t forget the sanitizer. In-depth sessions, larger-than-life speakers. Do you know those cinematic flash-forward sequences where the team describes how they will break into a super secure location? Everything time-coordinated and moving to peppy music.? That’s how I imagine my approach will be to attending keynote sessions this year. I know we must pick our favorites, but frankly, between celebrities, activists, and athletes, it’s hard to decide. We are talking about over 1000 potentially thought-breaking sessions. Planning, my friend. It takes planning (and the agenda builder on the Dreamforce webpage is just what you need). Boring Demos? No, it’s a Demo Battle An epic game show theme where Salesforce partners get a mere three minutes to showcase their tech. It’s going to be fast and exciting. And the best part? The audience gets to vote. I don’t know about you, but I’ll carry a poster saying I WANT TAVANT! Dreamfest Fundraiser Every year, Dreamforce organizes a fundraising event which is also a chance for Dreamforce attendees to chill. This year’s concert will benefit the UCSF Benioff Children’s Hospitals. Dreamfest will take place on Wednesday, the 21st of September, at Oracle Park in San Francisco. And this year, we will be rocking to… The Red Hot Chilli Peppers!!! That’s right; the Red Hot Chili Peppers are performing at Dreamfest! And by the way, all proceeds will benefit the UCSF Benioff Children’s Hospital. See what I did there? Dreamforce will take place in San Francisco at the Moscone Center from September 20–22, 2022, and is slated as the largest Salesforce conference of the year. Appropriately, this year’s theme is ‘Go big and come home.’ I can’t wait! ABOUT THE AUTHOR: I am Simran Tayal, Director Marketing at Tavant and I’ll be at Dreamforce with my team at Hotel Zetta, 55 5th St, San Francisco. For more information, click here.
The Rise of Streaming Analytics in the Media Industry
Compared to a decade ago, the increase in devices and quality of connectivity have transformed how we consume media. Streaming services made it possible for us to consume content continuously without the need to upload or download an entire file. Additionally, the sudden boom in OTT applications during and post-pandemic has expanded the use of media streaming platforms worldwide. This explosion in the volume of streaming content data fueled the need to understand customer consumption faster, resulting in the need for real-time analytics or streaming analytics. For example, providing recommendations in near real-time is now required, and the ability to analyze advertising data gives providers an advantage. The Evolution of Streaming Analytics In time-sensitive scenarios, real-time analytics uses newly generated data to make predictions, ask questions, and automate decision-making in the application. While previous analytical systems could run periodically (say every 24 hours), this was insufficient for time-sensitive data. In the case of streaming information, periodic analytics would be outdated by the time it is processed. Also, as data streams have no beginning or end, they cannot be broken into batches. This continuous flow of data also requires a different processing and data architecture. Streaming Analytics Processes Data Differently Streaming analytics is the processing and analysis of data flowing continuously, and it relies on real-time data. Real-time data can be streamed from transactional databases using change data capture (CDC) or from applications using an event streaming platform such as Amazon Kinesis and Kafka to data sinks. Stream processing engines are runtime libraries that help developers write code to process streaming data without dealing with lower-level streaming mechanics. It uses event stream processing, which analyses large-scale real-time information and in-motion data. Some of the most widely used stream processing engines are Apache Spark, Apache Flink, Apache Kafka, Apache Storm, Apache Samza, AWS Kinesis Streams, and Apache Flume. Real-Time Analytics Made Real Streaming analytics aims to offer up-to-date information and keep the state of data updated with very low latency. It provides real-time insights to enable more responsive decision-making. With media and entertainment companies generating vast volumes of data with every click, analytical speed is crucial. Real-time analytics or streaming analytics can help the media industry gain an advantage over competitors in the following ways: 360-Degree Customer View Streaming analytics enables businesses to measure data usage across multiple media platforms accurately. As a result, media providers can now aggregate data sets to develop a clear 360-degree customer view. These analytical data points can even include user viewing and engagement for companies to know how long, when, and where their viewers consume their content. Apache Flink is an open-source platform that can ingest massive amounts of continuous streaming data from multiple sources, which is processed in a distributed manner on multiple machines. Apache Flink is used by King (the creator of Candy Crush Saga) to analyze their 300 million monthly users who generate more than 30 billion events every day from different games and systems. Flink offers processing models for both streaming and batch data, enabling data scientists to access these massive data streams while retaining maximum flexibility. Anticipating Viewer Churn According to Interpret’s Video Churn Today in 2021 report, SVOD subscribers increased by 14% in the second half of 2020. During the same time period, the cancelation rate increased from 15% to 20%, and nearly 20% of subscribers switched services to gain access to exclusive content. In such a volatile and highly competitive market, streaming analytics provides operators with more accurate churn prediction models. Streaming analytics brings together both real-time and historical users (including user behavior and engagement) to identify subscriber clusters with a high churn risk. Impacting Customer Experience Media companies must be able to introduce user activation, reactivation, and engagement campaigns that get their users to continue consuming content on their platforms. Streaming analytics uses click records from various source platforms and enriches the data with demographic information to serve more relevant content to the targeted audience. Europe’s leading media and communications company, Sky, provides TV, streaming, mobile TV, broadband, talk, and line rental services to millions of customers in seven countries, and relies on the Google Cloud Streaming analytics services to deliver customer service at scale. Sky collects diagnostic data from its millions of TV boxes. By combining this set-top box diagnostic and viewing data with streamed and batched information from reference feeds, Google Cloud Streaming analytics created a data warehouse on BigQuery, to help ensure the best possible user experience. Real-time Recommendations Today’s media consumers demand personalized, relevant, and contextual content. But with an increase in streaming services, competition for viewership is intense. Recommendation engines driven by streaming analytics can offer more customization and personalization to keep viewers coming back for more. Based on the real-time analysis of this big data, media companies can make better decisions on content dissemination. Content Usage Insights Deep big data streaming analytics is also giving media companies deeper content insights. It helps uncover which genres are in high demand, what content is preferred at which time of the day, when they pause, or what they skip. By analyzing this live data in real-time, businesses can detect and act on strategic content opportunities. Apache Spark is an example of a streaming analytics tool that makes use of a big data processing engine to provide scalable, high-throughput, and fault-tolerant live data stream processing. Online news provider Yahoo uses Apache Spark for personalizing its news. It uses Apache Spark’s streaming analytics processing to find out what kind of news users are interested in and the kind of users who would be interested in reading each news category. Troubleshooting apps, devices, and more According to video analytics solution provider NPAW, 4.9% of video-on-demand views experience some error; for live views, the number is 7.6%. While media houses offer the same service across different devices, the understanding is that the approach cannot be the same. Netflix uses the Amazon Kinesis streaming analytics solution to monitor the communications between its applications so it can detect and fix