Delivering Value Chain Performance Through AI in Warranty
Long gone are the days when we had to remember dates for our next free service, oil change, tire rotation, wheel alignment, etc., but time has changed now, these days we receive notifications about these things via SMS, calls or e-mails. Do we comprehend what happens, what components come together so that your mobile phone can chime in a juicy offer on your next car service? Let’s just clear the air about Artificial Intelligence (AI) first to completely understand why it is a driving force that every organization wants to do something with it. AI in its crudest form is reacting to scenarios with actions that are meaningful but isn’t that what we humans do! Yes, but when we consider thousands of customers with vehicles bought at various times, with multiple requirements and problems, the number of data points run into billions, and it becomes humanly impossible to make even a tiny impact. This is where AI becomes the pivot to recreate success in all the scenarios and for all the data points. With the vast amount of data, a human would get overwhelmed to act accurately, whereas an AI works better with a large number of data, it can create and evaluate patterns in the data that hold good and learn from them. It can create new actions and repeat old actions to any amount of data points without fatigue. Aftermarket is the perfect place for an AI system, the vast dealer network, millions of vehicles, each with thousands of components that are serialized, and many more non-serialized parts, numerous variants, and configurations. That’s all well and good only if it benefits all the stakeholders in the aftermarket process, customer, manufacturer and the dealer. As a manufacturer, they can benefit at every stage of the product lifecycle; AI can help with product design, product safety, R&D, Material design, and styling. It can speed up the design and production of new products; AI can manage the supply volume better and in a streamlined manner. All these improvements add up to provide an edge over other players in the market in cost and market share. Like all businesses, the dealer will always benefit the most from a returning customer than a new one. The real money is in service, spares, and campaigns on the existing models of vehicles. AI can help with a predictive and preventive plan for thousands of customers and help improve the total earnings of the dealership. Additionally, the credibility that the dealer amasses with these transparent campaigns would be immense and everlasting with the existing customers and new customers as well. Most of the customers know little about servicing the vehicle that they are driving, many would not even know all the components, this lack of knowledge is a curse to the customer. Customers will want to come back to authorized service centers if there is a transparency, constant support system as well as a positive experience with the brand. AI can help with all these scenarios, help in the analysis of the vehicle for faults, DIY repairs, deploying services such as road assistance and service dealers and make driving more convenient. In Conclusion: We are always in search of a better solution, a better way to deal with issues and problems, AI is that tool that can deliver at all the above and many more scenarios and help grow Aftermarket revenues, after all, everything from lead-scoring to car-sharing to vacation-rentals has been disrupted through AI. Customer Convenience is essential for brand survival. Artificial Intelligence and machine learning can increasingly be deployed to manage customer experience across ecosystems. Meet our AfterMarket experts at Warranty Chain Management conference, WCM 2018 in San Diego from March 6-8, Booth 11.
Transforming Customer Experience with AI
Eighty-five percent of customer relationships will take place without human interaction by 2020 and AI-derived business value will more than triple to $3.9 trillion by 2022, according to Gartner. By 2019, 40 percent of digital transformation initiatives will be supported by some cognitive computing or AI effort as predicted by IDC. Furthermore, Servion has forecasted that AI will be able to power 95 percent of all customer interactions by 2025 and it will do it so effectively that customers will not be able to ‘spot the bot.’ Many organizations are at the center of this digital transformation and are turning to the emerging technologies such as chatbots, intelligent ad targeting, recommendation engines, personalized communications, and image recognition to gain business value, bolster their relationships, differentiate themselves from their competitors, and increase revenues. AI is quickly becoming a mainstream technology in consumer devices and services. In 2018, the business conversation in boardrooms on Twitter, LinkedIn, blogs, print media, and conference keynotes about AI, machine learning, RPA, chatbots, and virtual assistants have reached the new pinnacle. Surprisingly, nearly two-thirds of consumers are already using AI without even realizing it with products such as Alexa, Siri, Cortana, and Watson. Thanks to the adaption of AI into CX, we are witnessing how enterprises are attempting “true” personalization with predictive capabilities in real-time. This indicates better listening to your customers, understanding the context and providing them with a CX. Organizations are embracing AI to enhance the customer experience by: Intelligently augmented self-service technologies Collating data to enable price and feature comparisons Gathering data by smart assistants Using sentiment analysis to track customer emotions and respond accordingly Forecasting customer needs and then responding proactively Gaining more information about the customer based on data patterns Discovering user interaction on websites to determine if they need help Giving recommendations based on the behaviors of similar customers How can AI Enhance CX? Customer Insights Bring Important Findings for Businesses Leveraging AI can help unleash actionable customer insights that can help in driving impactful business decision-making. AI has also transformed how organizations get customer insights. Leveraging the vast amount of data that is available on customers today, AI can keep a track on trends and predict what customers’ need in the future. One of the best examples of this is Spotify, which used data from its more than 100 million customers to create a compelling ad campaign. The Use of Personalization Improves Customer Experience In customer experience, personalization is a significant advantage to AI. Modern customers expect offers to be tailored to their needs—a blast email with some general offer won’t appeal to nearly as many people as a targeted offer that directly addresses what precisely a customer wants. However, creating those personalized experiences is extremely difficult and tedious for humans. AI can quickly sift through millions of pieces of information to figure out exactly what matters to customers to create a personalized experience. Process Automation Increases Business Efficiency Streamlining repetitive tasks is a big change happening across industries. Deploying AI to automate the process efficiently and effectively can save time and increase efficiency. It provides a seamless experience for the customer by having a near 0% error rate. Additionally, it becomes easier for service representative relaying information and responding to the customer’s needs. This efficiency enables them to take care of more people in a much shorter amount of time. By automating processes and allowing for real-time data integration, communications are significantly improved. Looking ahead AI is undeniably a powerhouse when it comes to customer experience. This technology will not only allow organizations to create faster, more personalized experiences but will also help in gaining customer insights to deliver better customer experience in the future. Companies must unleash the potential of AI and act now to reap its possible rewards to gain a better competitive advantage in their business. Reach out to us at [email protected] to know how we can enable your business garner customer loyalty, improve experiences, and help you stay relevant in the business.
The Magic of Clubbing Customer Experience & Text Analytics
Analytics-driven customer experiences are redefining the Customer Journeys in the Digital 2.0 world now. According to Gartner, “By 2020, with the help of AI, customers will be able to manage 85% of their relationship with the brand without interacting with a human.” Today’s digital-savvy customers live in an omnichannel world and transact with businesses in many ways. When they set out to accomplish a task over time, they expect a seamless hand-off among devices and channels. The entire journey needs to be consistent, contextualized and connected to satisfy these increasingly demanding and fickle customers. Customer experience can drive superior revenue and is critical to growth and competitive differentiation for business. Data insight is one of the primary tools for CX enhancement. An enhanced CX clubbed with an in-depth data is an opportunity window for smooth customer journey. However, the practical challenge for organizations is to integrate all their digital and traditional channels to manage a friction-less experience. It is likely that data is trapped in siloed systems across marketing, sales, commerce, and service. Unlocking the potential of unstructured data hidden in the customer journey If structured data is so big, then unstructured data is enormous. It is known that organizations exploit only structured data that represents only 20% of the information available. That suggests that 80% of the data is lying mainly in unstructured form and there is a tremendous potential waiting to be leveraged in the analysis of unstructured data. Unstructured data usually includes comment boxes in feedback forms, is undoubtedly a significant way to gather consumer views on a brand or a service. Unstructured data is highly valuable when merged with structured feedback since it helps in visualizing the consumer’s journey with the brand. Making sense out of unstructured feedback is hugely complicated and organizations that decode this, gain a better grasp of the customer experience. Moreover, when monitoring customer feedback, the element that brings a couple of benefits is Text Analytics. This Text Analytics can help bridge the gap between customer expectations and the experience provided during entire customer journey. These days customer feedback data are coming from the emerging channels such as social media and mobile devices enabling companies to rely more on text analytics. Organizations that are quicker to identify emerging trends have drastically improved the survey experience with much shorter questionnaires where their questions are getting answered easily and are also realizing the potential of non-verbal expressions like emoticons in conveying customer’s sentiment in feedback. Business Value of Text Analytics Analyzing the overall sentiment of the conversation and ‘what, who, where, when, why’ transforms the unstructured data into structured data and enables organizations to pay attention to all of the conversations. An essential goal of analyzing unstructured data such as customer complaints, opinions or comments is to catch the pulse on what users perceive about an entity. It also helps organizations recognize what do the customers think of the various attributes of a company’s product such as quality, price durability, safety, ease of use. The key to digital transformation lies in combining the Text analytics pieces together with a well-thought customer journey at a strategic level. In conclusion The use of text analytics is burgeoning quickly, and organizations are unleashing the potential that is possible if textual data are analyzed and integrated with decision making. Given the exponential growth of unstructured data both outside and within the organizations, text analytics will continue to expand. Organizations need better insightful text analytics to understand the most relevant drivers to improve the customer experience, ultimately leading to ‘Delightful Customer Journeys’. Text analytics is undeniably actionable if it supports decision making optimally and if the results of the analytics can be shared in a way the business is empowered to act.
10 Ways AI Can Disrupt Consumer Lending
Artificial Intelligence (AI) and Machine Learning (ML) are having a significant influence on industries. From robotic process automation and speech recognition to virtual agents and driverless cars, the extent of its impact has moved us from a mobile-first world to AI first. In a recent study of digital executives, the majority, 31%, said, virtual personal assistants following Automated data analysts (29%), automated communications like e-mails and chatbots (28%), automated research reports and information aggregation (26%), and automated operational and efficiency analysts (26%) rounded out the top five. Business leaders said they believe AI is going to be fundamental in the future. In fact, 72% termed it a significant ‘business advantage.’ AI enables enterprises to unleash the trapped value in their core businesses. Machine-based neural networks can comprehend a billion pieces of data in seconds, placing the ideal solution at a decision maker’s fingertips. Your data is constantly being updated, which indicates your ML models will be revised too. Your enterprise will always have access to the latest information, including breaking insights that can be applied to rapidly changing business requirements. No doubt, many FinTech companies have cut down the costs of credit underwriting to find the right customer through Machine Learning applications. How can AI help Consumer Lending? Consumer Lending (CL) of all kinds, such as mortgages, autos, credit cards, student loans, etc., is a data-rich environment. We can say, at its core, lending is undeniably all about ‘big data’. For example, in a typical mortgage lending scenario, we estimate that between the borrower’s credit history, property, employment, income, tax, and insurance information, more than five thousand data attributes are captured during the lending process. This is a time-consuming and expensive process and in case of many lenders, an extremely manual and cumbersome process. And it is difficult to predict how much of this data is even relevant? How much of it is useful in forecasting borrower behavior during the application processing, closing, post funding and servicing stages? By leveraging more data and analyzing customer default probability, the credit scoring systems can predict behavior, thereby helping lenders come to a more conclusive decision based on data. Fintech organizations need to drill into the insights to grow their business, manage risk, and capture more market share in the competitive consumer lending landscape. 10 ways AI can impact the Consumer Lending industry Below are just some of the ways that this technology is taking the consumer lending industry by storm. Lower underwriting and origination costs by machine Reduced credit losses Fewer Losses from fraud Decreased agency recourse risk Better risk-adjusted margins Less servicing costs Reduced Write-offs Greater Customer Satisfaction Higher origination revenue Lower due-diligence cost The Road Ahead It is apparent that AI and ML are the future of consumer lending. Digital Transformation is drastically impacting the mortgage process, and it is imperative for lenders to stay updated with these changes and adopt them proactively. Technology is no more a roadblock and today’s customers are very receptive to digitalization efforts. Consumers no longer want the same old experience; they want convenient, secure solutions that meet their lending needs. It is therefore crucial for the lender to create digital mortgage experience that goes beyond an online application to offer a data-driven digital process through AI-powered automation. AI and Machine Learning have enabled key players across the consumer lending landscape to transform, both regarding their back and front-end processes dramatically. From cost reduction to streamlined operations to increased efficiency, both AI and Machine Learning will continue to pave the way for the consumer lending industry. The promise of AI has always been to make lives better and to enhance the way we work. AI can reverse the cycle of low profitability through intelligent automation and innovation diffusion. Advancements in ubiquitous computing, advanced algorithms, low-cost cloud services, analytics and other next-gen technologies are now allowing AI to flourish. However, AI’s full potential will never be realized until organizations take more risks and begin to experiment with AI technologies more aggressively Later this month, we will be releasing our white paper on “Reshaping Artificial Intelligence with Consumer Lending.” FAQs – Tavant Solutions How does Tavant implement AI to revolutionize consumer lending processes? Tavant leverages advanced AI technologies including machine learning algorithms, natural language processing, and predictive analytics to automate loan underwriting, enhance risk assessment, and streamline the entire lending workflow. Their AI-driven platform reduces processing time by up to 80% while improving decision accuracy and customer experience. What AI-powered lending solutions does Tavant offer to financial institutions? Tavant provides comprehensive AI-enabled lending platforms including automated credit scoring, real-time fraud detection, intelligent document processing, and personalized loan recommendations. Their solutions integrate seamlessly with existing banking systems to deliver end-to-end digital lending transformation. What are the main benefits of AI in consumer lending?AI in consumer lending offers faster loan approvals (often within minutes), more accurate risk assessment, reduced operational costs, improved fraud detection, and enhanced customer experience through 24/7 availability and personalized service. How does artificial intelligence improve loan approval processes?AI improves loan approval by analyzing vast amounts of data in real-time, automating credit decisions, reducing human bias, and providing consistent risk evaluation. This results in faster processing times and more accurate lending decisions. What challenges do lenders face when implementing AI technology?Key challenges include data quality and integration issues, regulatory compliance requirements, initial implementation costs, staff training needs, and ensuring AI models remain fair and unbiased across different customer segments.
How Mobile Solutions Can Reduce Warranty Costs
As technology advances every day, so do customers’ expectations from manufacturers. To be competitive and to survive in the market, manufacturers must provide improved solutions with lower costs. These goals can be achieved through mobility solutions. 1. Maintenance of Accurate Data Unavailability of exact product and customer information is a major challenge in the warranty industry. Mobile solutions help in capturing that exact data. Field service personnel can visit the customer site and capture the proper customer address, contact information, usage details, and service information. Maintenance of proper data helps in providing the correct coverage and maintenance, which in turn, helps to reduce warranty costs. Proper data also gives insights about warranty problems. 2. Lower Transit Time Field inspectors visiting the customer site can check machinery, perform the repair at the customer site, and update the problems directly from the mobile. The warranty team can start working on the case immediately. This reduces delays between various departments, speeds up the process and reduces the warranty costs. 3. Improved Process Mobile solutions help in reducing paperwork. When using a manual process involving paperwork, there’s a chance valuable data could be missed. Mobile solutions help in avoiding duplicate entries and important data cannot be missed since everything is maintained electronically. Regular reminders are sent to dealers, contractors, and field inspectors. This improves the overall warranty process, which in turn reduces the total cost. 4. Real-Time Connectivity Mobility solutions help in managing the process from any location. GPS monitors can be integrated with a vehicle to track its location. Telematics help to monitor the driving pattern of the vehicle, which reduces fraudulent claims and parts and service costs. It also helps to identify failures earlier, which helps to increase warranty cost savings later. 5. Increased Productivity Mobility solutions provide an option for employees to contribute to business process even while not at the office, which increases productivity. For example, the warranty processes like Warranty Registration, Arrival Condition Report, and Inspection can be done during installation/delivery from the customer site itself. The warranty team can start working on the claims immediately. This helps in improving productivity, thereby reducing warranty costs.
Evolution of Automotive Ecosystem
Decreasing sales, environmental regulations and increasing demand for more efficiency and new features are challenges every other manufacturer is looking to overcome. These challenges may decide the future of the automobile industry. If powerful engines, composite material, and lighter weight engineering were the trends at the start of 20th century, going forward, what may disrupt the industry is electrification, connected cars, diverse mobility and autonomous driving. These changes are not only important from the perspective of the automobile industry, but will potentially impact multiple other industries, such as insurance, high-tech, and telecommunication, connected with these solutions. Electrification The electric vehicle market is forecasted to grow at a CAGR of 23% through 2021, according to market research firm Technavio. There are multiple factors that may push for electrification, such as a drop in the price of battery prices (prices may fall by 70% by 2030(1)), government support in the form of tax breaks, incentives and benefits, and most importantly lower maintenance costs. What could further support this change are government initiatives to build and maintain electric charging stations in major cities as well as on connecting routes. Connected Cars (Vehicle-to-Vehicle Communication) Vehicle-to-vehicle communication is one of the critical new changes that may have a huge impact on passenger safety. With vehicles communicating with each other to share details such as speed, the direction of travel, and traffic conditions over a dedicated network, the speed and response period of every vehicle on the same road could be synchronized to the vehicle in front, thereby reducing the probability of a collision. According to WHO, auto accidents cost most countries almost 3%(2) of their gross domestic product (GDP). According to the U.S. Department of Transportation, deploying vehicle-to-vehicle communication can reduce 80% of the accidents that occur on roads in the U.S. Diverse Mobility Consumers today use their all-purpose vehicles for a wide range of tasks, but in the future, they may demand individual solutions for specific purposes, on demand, probably via their smartphones. There are already trends that point toward this change, such as a 30%(3) increase in car-sharing members in North America and Germany over the last five years. According to McKinsey, one in ten cars sold globally in 2030 will potentially be a shared vehicle, which could also mean more than 30% of miles driven in a new vehicle could be from shared mobility. Autonomous Driving With commuters spending an average of 42 hours every week in traffic in places like North America, there is a huge demand for autonomous driving, which could help drivers refocus and invest their time in more productive activities. The time spent in traffic increases to 104 hours per week in Los Angeles, the highest in the world, followed by Moscow where a commuter may spend 91.4 hours per week during peak time, according to the INRIX Global traffic scorecard. The beneficiaries. OEMs would now be looking at plethora of information getting generated from individual equipment to not only improve the product, but also to create a new set of complementing products and services, such as networked parking service, vehicle usage monitoring and scoring (a service already available in many markets), predictive maintenance, over-the-air software updates and add-ons that could become alternate sources of recurring income for the OEMs. Dealers may move away from sales of vehicles to a fleet management model, managing only the service part of the business, resulting in highly consolidated market players with huge fleets. The transportation sector will be able to optimize its operational expense with autonomous driving opportunities for faster expansion and cost-cutting. IT companies and semiconductor manufacturers may become the largest suppliers for OEMs moving forward. With digitization and the electrification of the automobile, the major components that would come into play are the electrical hardware that will run the vehicle, the semiconductors that will be the brain for operations, and the software that will drive the logic on how the vehicle will operate. Companies that are able to integrate these into a single package (auto vision, artificial intelligence, IOT, etc.) may develop more of an edge over other companies. What is in it for others? Nearly 1.3 million people die globally due to car accidents. In the U.S. alone, for every death, there are 100 treated in emergency rooms, with an annual cost of USD 33 billion (4) in 2012. Autonomous driving could help reduce health care costs and change the car insurance industry completely. The telecom sector would benefit from the increase in traffic on their networks because of vehicle-to-vehicle communication but may have to upgrade its infrastructure to support higher speeds and lower latency. Electric utility companies may be one of the biggest beneficiaries of electrification; according to the 2017 report by Bloomberg New Energy Finance (BNEF), electric vehicles could account for nearly 54% of new car sales by 2040, which could mean a requirement of more than 1900 TWh of electricity every day — equivalent to 8% of global electricity demand in 2015. (1) Electric Vehicle Outlook 2017 by Bloomberg New Energy Finance (BNEF) (2) http://www.who.int/mediacentre/factsheets/fs358/en/ (3) https://www.automotiveworld.com/analysis/eight-disruptive-trends-shaping-auto-industry-2030/ (4) CDC 2014: Motor Vehicle Crash Injuries -Costly but Preventable
Transforming Customer Engagement Using AI
When you buy a new vehicle today, you automatically subscribe yourself to the usual ritual of taking the vehicle for the scheduled service so that the equipment is 100% operational and the warranty does not get void. This is not always a pleasant experience for the end customer since they have to keep track of the distance the vehicle has covered, or the days covered from the registration date to align with prescribed service schedules. Finally, when they take the vehicle for service, there may be a long waiting period, and in the end, the whole service may just be an inspection of the vehicle parameters and a basic preliminary service. This process creates apprehension in the mind of the customer regarding the whole process of scheduled service. OEMs focus a lot on customer engagement in the initial phase of the customer lifecycle, but there are little efforts to improve the experience once the sale is done. This in turn severely impacts the customer retention process. With the advent of new technologies, maintaining a consistent customer experience throughout the lifecycle becomes easier for the companies. Let’s look at a few existing solutions which can change the customer experience drastically while improving efficiencies upstream in the supply chain. Vehicle Telematics combined with Artificial Intelligence (AI): Most of the modern vehicles today come with an inbuilt telematics solution from the factory floor or at least have it as an aftermarket option. This system can capture and transmit the real-time information of the vehicle to an AI solution which will identify when exactly the vehicle should be brought to a service center and at the same time communicate the same to the customer. This will not only reduce the burden on the end customer to keep track of the scheduled maintenance but can also help to reduce the load on the service centers due to visits which may not be warranted. The solution can further suggest servicing slots (like booking movie tickets) to end customer so that load can be balanced across the complete servicing capacity. This will also have a significant benefit upstream in the supply chain with parts supplier being able to predict the possible demand for their parts at various geographical locations during specific time intervals in the future, based on the real-time data while removing the total dependency on the historical data for production planning. The solution once developed needs to be delivered to the end customer in a robust and scalable platform. Mobility: With over 37% of the world’s population expected to use a smartphone by 2018 from the 10% in the year 2011, this is a platform every company should take advantage of to reach to their end customers. By going mobile, companies can not only reap the benefit of being connected 24/7 with their customers but can use it as a platform to deliver wide array services both free and on demand. Companies can also use the mobile platform to communicate with their customers, provide a snapshot of the vehicle performance, help the customer book the servicing slot as per their convenience and provide customer support using integrated chatbots. Integrating all the key stakeholders with such a solution can help improve the operational efficiency as well as the customer satisfaction. Customers get notified when a service is due and get an option to quickly schedule it in advance, while the servicing centers can see the expected number of vehicles for the future dates and have the resources allocated to get the most optimum results. For all the stakeholders upstream in the service chain such as the parts supplier, this could help them move from the demand push to a demand-pull model wherein their production plan is synchronized to the predicted service schedule and the part replacement. Hence, it is a kind of win-win situation for all the stakeholders in the service chain ecosystem. Final Thoughts AI-powered customer service is a new reality. Customers aren’t waiting for companies to catch up, they simply shift their loyalty to a competitor with superior experiences. Companies hesitating to adopt, or even experiment, with AI, are already losing the innovation game and losing customers. AI is the future, and the future is now. Meet our AfterMarket experts at Warranty Chain Management conference, WCM 2018 in San Diego from March 6-8, Booth 11.
Machine Learning in Lending Summit Recap and Key Highlights
Last Wednesday, September 27th, we at Tavant hosted the first ever Machine Learning in Lending Summit at the JW Marriott in San Francisco Union Square. This was an exclusive leadership summit – invites were extended to key executives in the mortgage and consumer lending industries. This one-day summit consisted of keynotes, workshops, a panel discussion, and interactive sessions that showcased the practical applications of Artificial Intelligence and Machine Learning in the mortgage industry. The summit began with a welcome address by our CEO, Sarvesh Mahesh. Next on the agenda was R.V. Guha, a renowned scientist, who spoke on accelerating digital transformation with AI and empirical modeling. He began his keynote by defining what exactly the buzz is around data science and the importance of empirical modeling. While analytic models have limitations, empirical modeling has had a lot of success in the past decade. He continued on to state that “datasets drive research” and deep dives into the varieties of data sets, available databases, current resources (i.e. Schema.org), and proposed future solutions (i.e. datacommons.org). Key takeaway: Empirical modeling is for complex systems what calculus is for classical engineering. This new class of models can handle complex phenomenon that has a significant social and behavioral component. The next speakers featured Manish Arya (CTO, Tavant) and Aseem Mital (Tavant Founder), who had an interactive session on Applications of Machine Learning in Lending and how these applications and concepts can be applied to the mortgage industry. Prasun Mishra (Senior Director, Tavant) and Harsha Naidu (Director, Tavant) led the Lending Club Workshop which demonstrated a general approach for creating decision models. Prasun and Harsha used publicly available Lending Club data and created a stepwise approach that used Machine Learning to develop a credit risk model and predict loan performance. They also introduced supervised learning techniques. Next up was an engaging panel discussion featuring Robert Carpenter (Principal in Technology, CoreLogic), Nick Stamos (CEO and Co-Founder, Sindeo), Brian Pearce (SVP, Wells Fargo), Ronald Olshausen (Managing Director, HedgeServe) and Gabe Minton (CIO, Guild Mortgage). The panel provided key insights into problems and challenges that businesses currently face with AI and Machine Learning in respective industries. The final session featured Mohammad Rashid (VP, Tavant) and Matthew Wood (Senior Director, Tavant) who discussed blockchain 101, applications and case studies, and how blockchain technology is disrupting industries globally. Key takeaway: Overview of the Tavant digital mortgage landscape, and how to disrupt the mortgage process and lifecycle. The summit concluded with a closing session presented by Hassan Rashid (CRO, Tavant). The summit was highly successful and attendees found the content thought-provoking and valuable. We wanted to express our concerns with how AI and Machine Learning were being applied in other industries at a rapid rate, but companies in the mortgage industry are falling behind by not utilizing the newest technologies. We wanted to demonstrate to senior leadership that it is now easier than ever to apply AI and Machine Learning in the mortgage industry. It is imperative for companies to apply this technology, accelerate innovation, and strengthen their competitive advantage. The summit concluded with innovative and disruptive ideas that senior business executives were able to take back to their respective organizations. Watch a recording of the live stream of our Machine Learning in Lending Summit here. We’re listening. Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter. OR Please add your thoughts, ingenious analysis, and feedback in the comments section below. We look forward to hearing from you. FAQs – Tavant Solutions What machine learning insights did Tavant share at recent lending summits?Tavant presented breakthrough applications in predictive underwriting, automated document intelligence, real-time fraud detection, and adaptive risk modeling that are transforming lending operations and customer experiences. How does Tavant stay at the forefront of machine learning innovations in lending?Tavant invests heavily in R&D, participates in industry conferences, partners with academic institutions, and maintains innovation labs focused on emerging ML applications for financial services and lending automation. What are the latest machine learning trends in lending?Current trends include explainable AI for regulatory compliance, federated learning for privacy, automated model governance, real-time decisioning, and the integration of alternative data sources for more inclusive lending. How is machine learning changing credit scoring?Machine learning enables dynamic credit scoring using alternative data, real-time updates to creditworthiness, more accurate risk assessment, reduced bias in lending decisions, and personalized credit products. What machine learning applications are most valuable in lending?Most valuable applications include automated underwriting, fraud detection, customer segmentation, price optimization, default prediction, and document processing that significantly improve efficiency and accuracy.
Extended Warranties: A Retrospection
In today’s competitive business environment, organizations are concentrating not just on sales but the aftermarket promises – made in the form of extended warranties. While people have been reading the abundant material, written by skeptics, on why extended warranties might do more harm than good, efficient contract management has proved to be the key to retaining and expanding customer bases for many manufacturers. There is a growing need for organizations to imbibe technological innovation into their culture. The current drive is to automatically manage rules for payouts, contact deadlines, rates, replaceable spares, and so forth. A better customer management process can only be achieved with an end-to-end support system in place. What customers like Customers relentlessly crave for more – every time and are willing to spread the good words about your brand if you make them feel that warranty claims are a cakewalk. Organizations, thus, use intelligence-driven claim-submission modules for customers to avoid unnecessary interactions. Extended warranty holds the key to OEM profitability, and in the long run, helps achieve deeper market penetration with smart, dynamic pricing. It is a general observation that a field asset may not be replaced entirely, especially if the spare is not included in the warranty. That is where you can create the scope for a customer to benefit from the power of dynamic pricing while offering extended warranty. You can offer to replace the spare for a one-time payment, which optimally covers the cost and doesn’t create a burden on any of the sides of the business. Thus, extended warranty helps attract more customers and improves ROI, while increasing your aftermarket profitability. Offering what customers prefer With artificial intelligence and machine learning enabled features like new quote management, OEMs and dealers can reach out to customers with new offers and promotions. Preparation of such offers can be a daunting task for sales teams, but with smart technology, they can progress efficiently. In this way, customers can benefit from real-time pre-approved discounts, pricing updates, the latest products and services, and more. Organizations are looking for platforms which can deliver such information in a customized way and increase the real value offered to customers. Extended warranty a common practice Decentralized operations of extended warranty have reaped benefits for organizations as well as customers. It is standard practice these days that dealers or distributors offer extended warranties in addition to standard warranties provided by OEMs. This approach creates value and captures market share by extending goodwill toward the market. And, finally It is imperative for OEMs to monitor their internal policies continuously and maintain their command over operations. That would prevent leakage through fraudulent claims and add to customer delight. Organizations require a flexible technology arm, which can modify, cancel, and alter contracts as per client demands, but in line with the organization culture. Get to know more about ideas and thoughts from a team that is passionate about delivering artificial intelligence and machine learning solutions that impact customers’ core businesses. Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter. Meet our AfterMarket experts at Warranty Chain Management conference, WCM 2018 in San Diego from March 6-8, Booth 11.
Tavant to Attend eTail East 2016 in Boston
Santa Clara, Calif., August 10, 2016: Tavant, a leading global provider of specialized software solutions, announced today that it will be participating in the eTail East Conference, August 15-17, 2016, Sheraton, Boston. The eTail East 2016 is one the most innovative eCommerce show on the East Coast for investors, thought-leaders and established etailers. At the event, Tavant Technologies will showcase its cutting-edge expertise in analytics, data insights, consumer insights, pricing analytics, customer engagement, real-time dashboards, mobility, cloud infrastructure integration, agile development process, automated testing and more. “There is a huge opportunity for etailers today to embrace a more comprehensive and holistic approach to analytics and mobility,” said Vibhor Mishra, Senior Director, Marketing, Tavant Technologies. “At eTail East 2016, we will share insights on how we have helped retailers to overcome their data challenges, problems in taxonomy, search optimization, recommendations engines and much more.” Call +1-866-9-828268 or email [email protected] to schedule a meeting with our experts. Find Tavant Technologies on LinkedIn and Twitter. Media Contact: Vibhor Mishra Tavant Technologies Inc. +1 (408) 519-5400 [email protected] tavant.com