Decoding the Future of Fintech Lending
It’s 2005, and Linda is all set to purchase her first home, her “starter home.” She knows finding that perfect home won’t be easy. On top of that she knows closing on her mortgage is going to be a time-consuming process. She is exasperated and dreading the thought of going through many cumbersome manual processes and tiresome paperwork. There is nothing much she can do but patiently wait for the process to play itself out. Fast forward. It’s 2021. The pandemic has reshaped how we work, and the world is adapting to a new era of remote working. Amid the upheaval, like many others, Linda decides to move from her ‘starter’ home to her ‘forever’ home with a backyard and home office. She found the perfect home by searching Real Estate sites on her mobile phone, yes, she found her home on her iPhone. Next step is the dreadful mortgage, but thanks to her lender’s digitalized application and closing processes, the entire home-buying process is now simplified and much quicker; Linda is pleasingly astounded by how much mortgage lending has evolved over the years. Reaching the New Wave of Borrowers With Digital Mortgage Capabilities Today’s mortgage industry is on the cusp of digital re-imagination. Driven partly by the need to meet the growing demand from tech-savvy borrowers for a quick and seamless process and partially by the pressure to cut costs and enhance efficiencies. Lenders are looking to digitize their end-to-end mortgage process. That’s the dream state for a lender. Thus far, most of them have focused on the lending process’s front end, enabling digital loan applications and consumer portals. As competition intensifies, they shift to the next stage of digital transformation by turning to Fintech lending solutions that boost efficiencies in loan production and enhance the servicing experience. Fintech Lending- The Digital Focus of New-age Lenders According to the report titled ‘The Role of Technology in Mortgage Lending,’ fintech lenders have the ability to process loan applications about 20 percent faster than other lenders. Fintech lenders process mortgages faster than traditional lenders, measured by total days from submitting a mortgage application until the closing, the report indicated. However, switching traditional mindsets and operating models to deliver digital journeys at an accelerated pace is no easy feat for a financial behemoth. But modernizing the borrower experience is the need of the moment for all lenders. Fintech is playing an increasing role in shaping financial landscapes. A fintech mortgage provides faster, more accurate, safer, and more affordable options than traditional mortgage lenders. It enables lenders to create a better relationship with borrowers with quicker and more seamless, personalized experiences. It accelerates data gathering, helps borrowers with superior communication, and reduces avoidable steps along the way. Seizing the Benefits of Fintech Mortgage Lending Enhanced efficiency: Efficiencies produced by fintech lending solutions allow lenders to close on mortgage loans faster. Automating numerous back-office operations and centralized data solutions also enable lenders to leverage customer information more efficiently than ever before. It speeds up otherwise time-consuming operations and further helps in closing the loan process faster. Delightful customer experiences: A more agile, streamlined application process indicates customers may be more likely to perform a given task that serves the lender in terms of the number of applications closed and funded. No more fragmentation: Fintech mortgages replace the fragmented siloed solutions of traditional lending with an integrated, end-to-end digital solution. It leads to greater efficiency and productivity, along with quicker loan cycle times and faster closures. To the Future: Let’s fast-forward to 2030. Linda is in the process of refinancing her ‘forever’ home. She’s astonished by the impressive advancements in cycle times and service levels compared to her 2021 experience. Her lender leverages next-gen digital interfaces that allow her to have contextual chats in real-time. Her appraisal is done same day, by a drone. Her lender uses AI-based applications to drive intelligent decisions based to ensure that Linda meets specific credit requirements, saving her significant time and effort. Not just that, the blockchain technology is there to provide a single source of verified data such as her tax information, income, assets, property valuations, and so on, improving accuracy as well as fast-tracking the loan fulfillment process. The outcome: Linda e-closes her refinance in a couple of days, or perhaps even in a few hours, thanks to an integrated digital ecosystem. It truly is a “one-click” refinance. Are you ready for the digital future? As digitally connected millennials and Gen Z borrowers coming into the marketplace expect hyper-personalization and faster closings. Lenders seeking future-proof success have only one choice – move from a tactical to a strategic mindset, modernize processes, and embrace intelligent automation. Learn how Tavant can help you lay the foundation for an end-to-end digital mortgage; reach out to us at [email protected] or visit us here. FAQs – Tavant Solutions What future fintech lending innovations is Tavant developing?Tavant is advancing embedded lending solutions, API-first architectures, real-time decision engines, and predictive analytics for market trends. They’re building platforms that enable instant lending integration across various digital channels and ecosystems. How does Tavant prepare lenders for future fintech disruption?Tavant provides scalable cloud-native platforms, open API frameworks, and continuous innovation programs that help traditional lenders compete with fintech companies while maintaining regulatory compliance and operational excellence. What trends will shape the future of fintech lending?Key trends include embedded finance, buy-now-pay-later expansion, cryptocurrency lending, AI-driven personalization, regulatory technology integration, and the rise of neobanks offering specialized lending products. How will fintech change traditional banking?Fintech will push traditional banks toward digital transformation, force innovation in customer experience, create new partnership models, and require banks to become more agile and customer-centric in their lending approaches. What is embedded lending?Embedded lending integrates loan products directly into non-financial platforms like e-commerce sites, software applications, or marketplaces, allowing customers to access credit at the point of need without leaving the platform.
To Automate Testing or Not to Automate – The Reality of Test Automation
Organizations today face many Quality Assurance (QA) challenges – time constraints in development and test cycles, executing large volumes of test cases, testing diverse legacy applications, and mitigating the impact of ripple effects that arise from configuration changes in application modules. The best way to deal with this situation is to adopt a well-integrated and robust automation solution that can predict and simulate business scenarios. Automation testing uses automated tools or programs to execute a series of tests that check the quality of a program or product. The significant feature of automated testing is its ability to perform hundreds of tests in minutes and record outcomes with accuracy and speed. Tests run repetitively based on programmed expectations, which can often be too tedious to perform manually. Vendors today provide automation testing services/platforms as part of their quality engineering services to ensure continuous feedback into the product lifecycle. THE DEMAND FOR AUTOMATION TESTING According to the Global Automation Testing Market report, the automated testing industry is expected to grow at 14.2% CAGR during the forecast period from 2021 to 2026. In another survey conducted by Compuware, most enterprises think that manual testing is one of the major hindrances to a business’s success. Additionally, more than 90% of respondents believe automation testing to be the most critical factor in accelerating innovation. And as a result, the demand for smart automation testing services is booming. WHY DO BUSINESSES WANT AUTOMATION IN TESTING? With automation testing, both developers and quality analysts can be sure of the quality of their products without lengthy test execution cycles. Automated testing can give organizations quick feedback on product or software performance. In May 2019, the difference between test execution efforts of manual and automation testing was recorded. The results showed that for a test case set of 1000 Full Regression, manual testing took 160 hours, while smart test automation required only 16 hours, a clear saving of 90% of test execution efforts. These results have defined the efficiency of executing automation testing in technology developments. EXPECTATIONS VERSUS REALITY IN AUTOMATION TESTING We’ve established that automation testing services are a necessity to improve your product performance effectively. But there is also another side to it. Faster release cycle with quality, notwithstanding, automation testing services also come with some limitations: Everything Cannot Be Automated Some businesses have started to consider whether every test can be automated, in other words, 100% test automation. But the belief that a higher level of automation is always desirable and achievable is a myth. While specific tests may benefit from being automated, others cannot be automated. Also remember, that automated test cases will only be as good as the programming behind them. Costs Versus ROI Automation testing needs to be designed based on whether the automation tests can save manual effort and offer a long-term return on investment (ROI). Automation testing tends to require a higher investment initially, with potential earnings and saving realized later. Additionally, the ROI of automation testing can be dependent on the tool that is used to conduct the tests as well as the complexity of tests implemented. Staying Objective While Test Automation can have a tremendous impact on product quality and returns, it is essential to understand its limitations and set realistic expectations. To achieve significant success with test automation, teams must first define the results objectively and carefully plan the tests without bias creeping in. Our objective should be to automate 100% of the test that should be automated instead of automating every test. So, figuring out what to automate (or not to automate) should be given the utmost importance before starting any automation activity. THE FUTURE OF AUTOMATION TESTING In today’s highly competitive market, businesses are seeking faster time-to-market along with the pressure of continuing to offer a superior product or solution. As a result, companies run more tests to find bugs faster and release their products or upgrades more quickly. The role of AI in automation testing is one of the most prominent automation trends in 2021. As AI-powered tools continue to advance, businesses will manage their automation testing even more efficiently and at speed. Machine Learning and AI testing will also develop automatic research methods and use advanced analytics to track results.
Unleash the Power of Data-driven Decisions in Lending
AI-based Attribution Models – The Future of ROI-focused advertising
The current economic climate is putting advertisers and marketers under tremendous pressure to demonstrate ROI from their marketing dollars. Gone are the Mad Men days of advertising when the art of storytelling was all it took. Today, advertisers are spending in an omnichannel environment and must account for every ad dollar. It’s no longer all about reach – or clicks on online ads. The entire focus of advertising has evolved from reach or clicks to the desired outcome – usually a purchase – either online or offline. How do marketers know which of their multitude of channels and touchpoints are performing on par and responsible for consumers’ desired actions? Enter marketing attribution As the marketing ecosystem gets more complex, the platforms that need marketing dollars to increase in number, and the demand for justifiable ROI from marketing reaches unprecedented decibels, sound measurement, and attribution have become the holy grail for brands and CMOs. They are seeing an immediate need to link different marketing touchpoints in their attribution and decision-making models to truly understand the new consumers’ purchasing path. Marketers have been following simplistic attribution models such as a first click or last click or attributing credit to the first or last touchpoint in the path to purchase. However, these attribution models are hardly comprehensive or data-driven, and they rarely say anything about the consumers’ intent. AI-based algorithms now play a significant role in building sound attribution models for the new complex, non-linear buying journeys and multitude of marketing channels, platforms, and touchpoints. Custom attribution models with AI at the core We have developed four key models for AI-based attribution that are truly data-driven and cutting-edge. AI-based custom attribution models are truly compelling in the new-age marketing ecosystem. Our four attribution methods are based on all events and channels where customer touchpoints exist and can predict – to a large degree of accuracy – whether a touchpoint led to conversion or not. What are these four methods? Here’s a quick summary: Logistical Regression – It is a well-established statistical model that takes inputs from existing touchpoint data and predicts which class the data should belong to. A non-linear function is applied to each touchpoint. Smaller the value, smaller the weightage assigned to it. Using these touchpoints, the model is trained over time. Each touchpoint becomes a variable in the logistical regression model, predicting conversion based on historical data to a reasonable degree of accuracy. Shapley Value – The Shapley Value model takes a game-theoretic approach to multi-touch attribution. The core idea is to keep or remove a channel and then ascertain the outcome. This naturally tells you your highest performing and lowest performing channels and is a fair and transparent way to attribute credit to each channel or combination of channels. Markov Chains – This model considers the sequence of the customer journey, i.e., the likelihood of each customer being exposed to some marketing tactic and the potential next step in the journey. In summary, the Markov Chains model focuses on the probability of each consumer transitioning from one exposure to the next marketing exposure and taking a desirable action in the process, such as a website visit or a purchase. The model considers all possible conversion paths. It gives appropriate weightage to each exposure on the customer’s journey to conversion. We then take away one of the channels and see the impact on conversion and subsequently ascertain the value of that channel in the attribution model. Hidden Markov Model – Hidden Markov Model, although new, is one of the most effective attribution models in marketing. It attempts to determine the state of mind of each consumer when they perform any action during the path to purchase. For example, what state of mind is the consumer in when he or she visits the website, searches, clicks on an ad, etc. This determines whether the action will lead to purchase or not. The Hidden Markov Model has had a significant impact on ML, and its impact on marketing and advertising is only beginning. It is safe to say that in the world of clicks – sometimes even inadvertent – the Hidden Markov Model can truly predict the role of each channel in bringing the consumer closer to desired actions like purchase. The Bottom Line: AI-based custom models are the future of marketing attribution. Evidently, AI-based attribution models can track each consumer at each stage of the buying journey and understand the importance of each “moment” and “action” in the customer journey. This helps advertisers truly understand the performance of each touchpoint and channel in the buying journey and optimize media spend continuously during campaigns. AI-based attribution models are driving the next generation of ROI-focused marketing. Are you ready to up your measurement game with AI? If yes, then reach out to [email protected] or visit us here to know more.
Is cloud the keystone of composable business?
Over the past 24 months, global businesses have encountered many uncertainties, from disrupted supply chains to government-led lockdowns and remote-working measures in workplaces. With many unknowns, the existing business models built for efficiency were put to the test, and the fragile nature of businesses exposed to the new reality. Enterprises were forced to digitally transform at scale to bring agility in operations and resiliency to their business systems to overcome these challenges. Businesses need to build architectures that readily share data between business processes, analyze the data to derive insights, and build composable, modular systems that can respond and adapt to changes rapidly. This post-pandemic wave of digital transformation is meant to bring in more flexibility in enterprises and has been fueled by the cloud. With the advancement in cloud technology, companies today want rapid innovation, exploit new market opportunities, or deliver better experiences and scale efficiently with reduced technology risk. But how do we make such systems a reality? Decision-making has to be autonomous and augmentative to realize change early. Technology platforms, on the other hand, have to be modular and plug-and-play in nature to personalize application experiences for the customer. This means enterprises have to look for services that offer preassembled business capabilities and deliver role-specific application experiences. What are composable services? As per Gartner, “composable business means creating an organization made from interchangeable building blocks. The modular setup enables a business to rearrange and reorient as needed depending on external (or internal) factors like a shift in customer values or sudden change in supply chain or materials.” [1] The Gartner structure for a composable business is an organization-level construct that has implications for business strategy, vendor sourcing, technology/architecture decisions, and organizational models that redefine the relationship between business and IT. A composable business delivers business outcomes through an assembly of packaged business capabilities. [2] ” src=”https://www.tavant.com/sites/default/files/html-page-assets/composable–enterprise.png” alt=”Composable services” width=”724″ height=”515″ style=”box-sizing: border-box; border: 0px; vertical-align: middle; max-width: 100%; width: auto; height: auto; float: none; padding-bottom: 35px; padding-right: 10px; padding-top: 10px; display: block; margin-left: auto; margin-right: auto;”> To be future-ready, organizations need to have all components like customer journey, role-specific priorities, vertical domain plans, application experiences, cloud-native architectures, policy, and regulations in one place. In their cloud adoption journey, organizations should factor in composability and reusability to scale it across the organization and meet business objectives. Once these building blocks are defined and available, it becomes easy for the organization to design modular systems with cloud and create services for evolving customer needs and mitigate any disruption, be it a pandemic or a regulatory change in the market. Companies need to derive value from their cloud platforms by adopting them as a business-technology transformation. They need to invest in the business domains to increase revenue and better their margins. The business strategy and risk assessment should decide the technology selection process and the operating model developed around the cloud technology. Cloud investment priorities can vary by domain. [3] ” src=”https://www.tavant.com/sites/default/files/html-page-assets/cloud-based.png” alt=”Composable services” width=”724″ height=”519″ style=”box-sizing: border-box; border: 0px; vertical-align: middle; max-width: 100%; width: auto; height: auto; float: none; padding-bottom: 35px; padding-right: 10px; padding-top: 10px; display: block; margin-left: auto; margin-right: auto;”> Key steps to develop an effective cloud-optimized composable operating model: Implementing an agile way for application development, infrastructure, and security Leverage APIs and event streams to create application experiences that are intuitive and tailored to preferences Develop a software-defined approach to the cloud with infrastructure as code Embed reusability and composability with end-to-end automation Provision workloads on the cloud with dedicated as-a-service business platforms securely The composable business will be custom-defined for individual organizations, based on the business model, competitive landscape, and operating markets. So, the company should start with defining its long-term vision, evaluate cloud infrastructure and application experiences, and decide the business capabilities it needs from its composable organization. Composability is key to the success of cloud-related strategies in today’s changing business environment. This will enable organizations to have a unified view of the application experience, infrastructure stack, and security needs of the technology systems. More important, it will be pivotal in building the foundational structure for the IT organizations of the future meant to deliver intended business success. Source: Accelerate digital transformations through cloud platforms Gartner Keynote -The Future of Business is Composable Gartner – Future of Applications: Delivering the Composable Enterprise, 11 February 2020 ID: G00465932
Cracking the AI Implementation Code by Operationalizing AI
AI is increasingly affecting our daily lives as more and more tools are using AI. There is no doubt that enterprises are taking a serious interest in adopting artificial intelligence and machine learning. But the knowledge of how it must be deployed to accelerate automation and transform business processes is still in its nascent stages. ” src=”https://www.tavant.com/sites/default/files/blog-image/Operationalizing%20AI%20blog.jpg” alt=”Operationalizing AI” width=”724″ height=”408″ style=”box-sizing: border-box; border: 0px; vertical-align: middle; max-width: 100%; width: auto; height: auto; float: none; padding-bottom: 35px; padding-right: 10px; padding-top: 10px; display: block; margin-left: auto; margin-right: auto;”> AI IS HERE. BUT WHO REALLY HAS IT? AI is making inroads into our lives. According to a new KPMG survey of industrial manufacturing business leaders, the next two years will see AI technology having varying impacts on industry needs: 21% for product design, development, and engineering, 21% for maintenance operations and 15% for production/assembly. In fact 61% of business leaders believe that increased productivity is the most significant potential benefit of AI adoption . Despite the interest, enterprises across industries still struggle with the process of AI implementation to achieve predicted business benefits. HURDLES TO PRACTICAL AI IMPLEMENTATION As companies race to digitize and embrace edge technologies, the transition often requires business leaders to shift their thinking from traditional software engineering expectations. There are many reasons AI and machine learning models don’t necessarily pay off. Some of these include: 1.Lack of Qualified Data Scientists Data science is an essential aspect of developing a suitable machine learning methodology. But the growth of data processing in AI has led to a demand for data scientists who can help turn raw data into business value. This shortage can be overcome by either outsourcing the ML model development or training employees already working with data in ML model programming. 2.Poor Data Quality AI and machine learning tools rely on clean data to train algorithms. And businesses that do not have control over their data quality or data management will struggle to make their AI initiatives successful. Data engineering enables enterprises to maximize the value of their data assets. By working towards enabling cleaner data sets, businesses can deploy machine learning algorithms to design accurate predictive analyses. 3.Undefined End Results What performance metrics are to be measured when developing and selecting machine learning models? Businesses often fail to know the desired level of performance before an AI project begins, leading to a mismatch between model results and expectations. Understanding the project deployment maturity levels can help leaders understand the progress needed to adopt AI successfully. 4.Difficulties in replicating ML model results Incremental data and different environments often cause ML models to perform differently. ML models need to be updated or refreshed to account for data drift, deterioration or anomalous data. Rather than upgrading the ML model every time, businesses need to create repeatable modelling processes to ensure continuous learning happens during production. FROM EXPERIMENTATION TO EXECUTION Operationalizing AI involves combining ML learning methodologies with software engineering principles to create a production-grade solution. Using established frameworks can help companies find a starting point to formulate best practices to go forward. Atul Varshneya, VP of the Artificial Intelligence Practice at Tavant, has detailed an approach and points to consider for businesses looking to operationalize their AI initiatives. If you are looking for ways to move your machine learning projects from experimentation to execution, watch this recorded webinar. Are you looking to overcome the challenges in operationalizing AI for your business? If yes, then reach out to us at [email protected]. Source: Impact of AI on industrial manufacturing (kpmg.us)
5 Qualities of the Best Mortgage Lending Software
Other mortgage lenders are promising to complete the home loan approval process in as low as 24 hours. If you don’t understand how this is possible, your bank probably takes weeks to complete this process. What you don’t realize is that your competitors are using mortgage lending software. So, to keep up, you should also consider investing in tools like this. By now, you’re wondering how to find the perfect digital mortgage lending software. You want a tool that helps you improve the customer journey. Also, you want digital lending software that automates various processes and helps you get rid of the tedious manual work. So, how do you find this amazing tool? Continue reading this blog to learn the five qualities of the best mortgage lending software. Check the User-Friendliness of the Digital Mortgage Lending Software As a mortgage lending company, most of your employees’ area of expertise is finance. So, you need to consider this when searching for the best digital mortgage lending software. You don’t want complex software that your employees will struggle to use. That’s why you should check the user-friendliness of various digital mortgage lending tools. Ideally, you’ll want to look for software with a simple yet elegant interface. That way, it will be easy for your employees to navigate through the functionalities of this software. To find this easy-to-use software, reach out to the top digital lending solutions company. You’ll find out that this company invests heavily in research and development. It aims to learn more about the needs of its clients. So, this company understands your problems as a mortgage lender and offers software that solves them. Besides, this company has simplified the functionalities of this software to make it easy to use. That’s means you don’t need to spend any money training your employees on how to use this mortgage lending software. Go for a Customizable Digital Lending Software To get an edge over other mortgage lenders, you need to do things differently. You must be bold and look for customizable digital lending software. You want to set yourself apart from other companies that use a general digital lending platform. The reason is that general software will not fully meet your company’s needs. Also, you may be forced to change your mortgage lending processes to fit the functionalities of this software. Not only is making these changes inconvenient, but it’s also costly to your business. So, you should check the customization options of different digital lending solutions to choose the best one. You’re looking for software that you have a high degree of control over its customization. That means you can tailor this software to need your consumer lending needs. Besides, when checking software customization options, check its scalability. You want to see if the software gives room for your changing needs. For instance, if it can effectively handle the growing number of mortgage applications. Review the Security Features As a mortgage lender, you have a moral and legal duty to protect your clients’ data. Besides, you need to keep your operational information secret to maintain an edge. So, you need to ensure that all your computer resources are secure to achieve the above goals. That’s why security is one of the key things to check when searching for the best digital mortgage software. You want to check whether this software restricts data access only to authorized users. Also, you’re seeking insights into how this tool authenticates user identity before granting permission to access data. In addition, you want to know how this software stores the data and if it offers recovery options. So, the best digital lending software is the one with reliable security and data recovery features. Thus, you have no worries about a data breach when using this secure digital lending software Examine the Accuracy of the Digital Lending Platform One of the causes of a terrible customer journey is when a client’s mortgage falls through in the last minute. It’s even more frustrating to the customers when they learn they qualify for the house loan and the rejection was due to a software error. So, you need to look for ways you can prevent this from happening. On the other hand, you don’t want lending software that ignores key requirements and awards loans to people who don’t meet all requirements. The reason is that these people will have a hard time repaying the house loan. Besides, it’s costly for your business to deal with many delinquent loans. To manage all these problems, look for a digital lending platform with a high level of accuracy. With the help of this platform, you’ll ensure that only qualified people’s loan applications are approved. Besides, you’ll avoid denying house loans, people who meet all the set eligibility requirements. Evaluating the In-Built Reporting Feature One of the biggest advantages of using computers as a mortgage lender is the ease of preparing reports. With these reports, it’s quick and simple to evaluate the performance of your business. Also, you’ll rely on these reports when developing a strategic plan for your company. So, when searching for the best digital lending software, you must evaluate the inbuilt reporting function. You want to see if this software generates any kind of reports from the data you input. Also, you’re seeking details on whether you can tailor this software to generate reports that meet your needs. To get value to choose the best digital lending software that generates comprehensive reports. With these reports, you’ll quickly analyze trends and predict your clients’ needs. Get an Edge by Investing in the Best Digital Mortgage Lending Software To get an edge over other mortgage lenders, you need to get the best digital mortgage lending software. You want user-friendly software, which can be tailored to meet your needs. Besides, you should also invest in secure digital lending software to protect your business and clients’ data. Do you want to learn more about how mortgage lending software works? Then request a demo by filling out this form. FAQs
Revamping Security Paradigm in the Evolving Technology Landscape
The COVID-19 pandemic forced governments around the world to impose strict travel restrictions and encourage employees to work from home. Technology and connectivity suddenly became more important as companies worldwide scrambled to keep functioning under difficult working conditions. But the IT infrastructure of many companies wasn’t prepared for the rise in cyberattacks. While businesses were grappling with keeping their doors open, studies found that only 38% of companies had a cybersecurity policy in place. Before the pandemic, 20% of cyber-attacks used previously unseen malware or methods. After the pandemic, that number rose to 35%, with large companies such as Honda and Canon succumbing to malicious attacks. Why Cyberthreats Increased During the Pandemic With nearly half the U.S. labor force is working from home, employees are sharing more data remotely through apps, increasing the risks for their employers. Last year the FBI reported that the cyberattack complaints to their Cyber Division rose by 400%, reaching as many as 4000 complaints every day. Let’s look at some of the key causes: Employees work from home in environments with limited or absent security IT infrastructure of businesses are not technologically prepared IT security teams is dispersed, and learning how to minimize threats remotely Hybrid Workforces and the Future of Security One of the critical questions raised is whether businesses will go back to centralized offices or continue to leverage remote workers. The answer appears to be both, as CIOs globally gear up to manage a hybrid workforce. A Gartner survey found that 80% plan to allow employees to work at least part of the time remotely after the pandemic. 47% will allow employees to work from home full-time. As a result, many CIOs and IT leaders seek ways to manage their remote workforce’s security to be better prepared against future attacks. The Importance of Security Testing Today’s new work scenario has raised the importance of improved security and a planned approach to managing security. Breaches can impact brands, customers and even bring in legal repercussions. Companies, therefore, need to focus greater attention and resources on cybersecurity awareness training. Security teams need to be involved during software development to safeguard applications. Security testing can help manage issues related to confidentiality, authorization, authentication, availability, and integrity at every stage of the development process. The New Normal of Cybersecurity To keep themselves and their products secure, enterprises have begun working with security testing services and ethical hackers. Both internal programs and software development can be positively impacted by applying advanced security testing services, including test automation, performance, quality engineering, and digital assurance testing. Cloud Security Testing Cloud-based security testing involves testing newly developed applications for performance, assessing the security of current operating systems and applications on the cloud, vulnerability testing and security assessments via the cloud. Application Security Testing By using a combination of testing tools and techniques, businesses can avail application security testing to ensure their software products are resistant to threats. Application security testing also helps software developers by identifying the applications’ security weaknesses and exposing vulnerabilities in the source code. These can then be rectified before they become a bigger problem. Web Security Testing Security testing for web applications looks for holes and vulnerabilities which hackers could exploit. The web security testing method uses advanced tools and techniques to explore weaknesses, technical flaws and ensure data protection. Mobile Security Testing Mobile phone usage has resulted in increasing attacks through mobile applications. Mobile Security testing exposes vulnerabilities in mobile applications by testing for activities such as data flow and leakage, storage capabilities, authentications, encryption, and regulatory compliance. Security Compliance Testing Compliance is essential to protect against threats to your products and protect your company against legal issues related to attacks on your software. Security compliance testing will focus on ensuring specific industry-based legal compliance are maintained. Network Security Testing In a hybrid work environment, network security testing is critical. Network security testing identifies and vulnerabilities across any type of electronic data network. It also helps businesses shore their defenses and eliminate any security weaknesses within the company network. Penetration Testing By simulating a threatening attack, security testing experts can help identify vulnerabilities in your applications and products over networks, cloud, and web. Penetration testing helps measure and identify system health and any compromises being made both internally and externally. According to Cybercrime Magazine, digital attacks are predicted to inflict damages totaling $6 trillion USD globally in 2021. By seeking improved security testing services, businesses can manage their cybersecurity with greater effectiveness. Companies that wish to protect their business while ensuring employee productivity will find the time to implement security testing and prevent losses due to cyber-attacks.
The Power of Digital Out of Home (DOOH) Explained
What is DOOH? DOOH (Digital Out-Of-Home) media is the term that refers to any digitized display advertising that appears in a public environment. This includes digital billboards, outdoor signage, and networked screens found in even businesses-oriented gatherings areas such as stadiums, malls, and hospitals. DOOH has been gaining popularity for several reasons. But primarily offer tremendous reach and control to the advertiser while catching the audience’s attention more effectively than static billboards. In fact, a 2015 study by Nielsen found that 75% of respondents recalled seeing a digital billboard in the month prior, and 82% of those recalled seeing advertising specifically. At a time when traditional advertising is often seen as a nuisance, DOOH could be the novelty that marketers are looking for in the advertising world. How is DOOH better than OOH The critical difference between DOOH and OOH is one word. Digital. OOH or Out Of Home is advertising that also reaches people outside of their homes in public places. But these are either static billboards (with fixed images) or electronic. In contrast, Digital Out Of Home advertising is dynamic. This means that the content can be changed anytime to any ad or information from a networked computer. Additionally, DOOH allows for personalized advertising based on individuals viewing the displays. Real-time Messaging DOOH offers advertisers the ability to update their messages in near real-time. This means a far greater capability of testing messages in various locations. OOH communication, however, cannot be updated as easily as DOOH ads. Vendors can also offer advertisers and network digital display network owners ad insertion capabilities by implementing client-side or server-side ad integration with third-party or in-house ad servers. Programmatic Content Programmatic DOOH advertising works similar to online advertising but for public ad spaces. The advertiser uses a platform and creates their campaign, providing targeting, scheduling, placement details. Ads are then run on public digital boards that match the advertisers’ requirements, saving tremendous time and effort. This was never possible with OOH advertising and is one of the reasons Programmatic DOOH has quickly become a leading revenue driver for overall advertising. In fact, programmatic buying accounts for 40% of all revenue, netting an estimated $4 billion in 2018. Dynamic Advertising DOOH advertisers are only just beginning to explore the range of capabilities they can achieve by combining DOOH content with technological capabilities. For example, by analyzing weather data, DOOH can be programmed to change content depending on whether it’s sunny or raining. They can also be dynamically changed based on unforeseen events. For example, if there are flight delays, restaurants can create offers. Additionally, using image recognition allows ads to switch based on sensing the demographics of the viewers! Reporting & Analytics One of the critical benefits of DOOH over OOH is that media buyers pay only for impressions and received detailed reports on the campaigns that they are running. DOOH campaigns can also generate viewership analytics, similar to online ads. This is very useful to both marketers and network operators. It offers data such as proof-of-play, report scheduled, and any incidents, which allows the advertiser to stay on top of their ad campaign. Additionally, advanced analytics technology vendors can use this data to help advertisers see real-time operational metrics through model building. In Conclusion Given the flexibility over messaging and greater control over targeting and reports, DOOH is becoming central to digital marketing campaigns. And with platforms offering easy purchase of DOOH ads, it’s not surprising to know that Upbeat predicts the DOOH market will see $8.5 billion by 2023. SOURCES:
From Cost Center To A Competitive Advantage: Warranty Management In Manufacturing Today
We have all experienced that moment… staring at a dysfunctional product and wondering what the repairs will cost us. And more importantly… is the product still under warranty? As a customer, it seems like a solid and straightforward customer service process, yet the warranty management process is viewed as a manually intensive administrative task that’s not exactly the most productive or efficient. Warranty management is a critical set of processes and activities within service life-cycle management. While manufacturers may have viewed the warranty management process to be a drain on resources and revenue, technology has changed the game. Technology: The pill to warranty management headaches In an increasingly competitive world, manufacturers are beginning to recognize the opportunity technology offers. Not only in terms of minimizing costs but also in enhancing customer experience through optimization of the warranty management process. By optimizing warranty processes, manufacturers can reap financial and operational benefits and positively impact their entire service life-cycle: increased asset reliability, better production, and improved supplier relationship management. Studies show that the market for warranty management systems is valued at $2.87 billion in 2019 and is expected to reach $6.24 billion over the next five years, growing at a CAGR of 13.8%. Let’s look at some of the issues that warranty management solutions can resolve: Problem: Multiple Stakeholders The warranty process runs across multiple partners, which can be both internal and external, to the manufacturer. It’s easy to lose sight of metrics within this network of dealers, partners, and OEMs. As a result, product performance, usage, and ultimately, the customer experience gets affected. Technology Solution: Warranty Management Systems Software solutions can radically improve connectivity across the service life-cycle. Warranty management solutions can help reduce claims processing by 70%, by bringing all stakeholders together into a seamless system. Warranty Management Systems can positively impact collaboration with suppliers, channel partners, and customers by enabling manufacturers to focus on functionality that provides visibility into a product’s warranty life-cycle. Problem: Information Siloes The lack of transparency has many manufacturers struggling to gain visibility into their customer or product usage. This is especially true in industries where products are sold through dealers, retailers, distributors, resellers such as automotive or other equipment and machinery. With so many stakeholders involved, it is apparent that information is disconnected, leading to inefficiencies, delays in new product introduction, and ultimately dissatisfied customers.> Technology Solution: IoTs and Closed Loop Collaboration The ability for warranty operations and other service processes to leverage connected product data hinges on the number of connected assets. While the data is available, legacy systems often prevent visibility across relevant teams. By connecting smart devices and sensors and the data generated by machine networks, manufacturers can access a new level of insight needed to intelligently update the warranty claims processes. Problem: Time-Consuming and Fraudulent Claims One of the reasons warranty management is considered to be a costly affair is due to the amount of time and resources needed to investigate warranty claims. Time to process a warranty claim often requires customers to wait without their functioning product leading to increased frustration. Where the processing is still manual, errors and fraud can further derail the warranty management process. Technology Solution: AI & Machine Learning AI-driven real-time analysis of warranty claims helps understand behavior based on environment and recommend efficient product usage. This technology can help improve performance, reduce failure rates, and enhance productivity. Additionally, as more data is fed into the system through IoTs, machine-learning can enable automated warranty processes to make recommendations at a much faster pace than ever before. Problem: Duplication of Efforts Redundancies in data can cause a lot of problems in warranty management processes. It is estimated that more time and money is spent that more time is spent on administrative tasks such as updated customer information than on resolving the problem. This duplication of data inadequacy can often result in errors and more siloes within the service life-cycle process. Technology Solution: Automation Automated warranty claims systems can enhance process efficiency by reducing or eliminating manual efforts in claim submissions such as parts return and payments, turnaround time, and increasing productivity. Data accuracy will also be improved human error can be circumvented, reducing errors and redundancies. The future indicates that with the increasing use of smart devices, wearables, and smart home units, adopting a warranty management solution will likely enable an unparallel customer experience. Technology partners that offer warranty and service contract management expertise, which leverage AI and ML, will provide a distinguished advantage to the manufacturers. And manufacturers that quickly move towards a system to take advantage of these capabilities can secure their business competitiveness through increased customer satisfaction and enhanced product quality. SOURCES: https://www.mordorintelligence.com/industry-reports/warranty-management-system-market