Are Mortgage Lenders Saving Big by Adopting Intelligent Automation and AI?
In 2020, when the pandemic hit the world, it started a wave of rapid digital changes that spread across the globe. In 2021, these changes were put into place. It took a lot of money for businesses around the world to change so that they could work from home, be more socially isolated, and do business in a way that may never be the same again. In 2022, it’s clear that those changes will stay. The technology that is easy for people to use is getting a lot of attention again. Trends are likely to become the norm in the future. AI in Fintech market size is expected to reach $17 billion by 2027, and it’s no surprise that AI and ML (machine learning), and Intelligent automation will be at the heart of this. The only question is, how do fintech companies use these tools to make digital transformation happen and make it work for them? Fannie Mae’s quarterly Mortgage Lender Sentiment Survey® conducted a research among senior mortgage executives in August 2021 to better understand lenders’ views on AI/ML technology and to see how interested they were in different AI/ML applications. The study revealed the following key findings: Most lenders (63%) say they know about AI/ML technology, but only about a quarter (27%) have used or tried AI tools for their mortgage business. Lenders expect to use some AI tools in two years. Lenders who already use AI/ML technology say they mostly use it to make their operations more efficient or improve the customer/borrower experience. People use it to apply for a loan, get a loan, and get it approved. The biggest problems for lenders who haven’t used AI or ML technology are integration issues, high costs, and not having a proven track record of success. AI/ML applications that help businesses run more efficiently are the most appealing to lenders. Lenders found the concept of “Anomaly Detection Automation” to be the most appealing. “Borrower default risk assessment” came in a close second, though. There are solutions, but they are task-oriented rather than holistic. In terms of customer-facing solutions, 75% of organizations say AI supports or drives one. This high figure is reached by combining distinct procedures. Next to loan applications, AI is used for documentation, marketing, and closing. Overall, 83% have at least one AI-powered back-office solution. The top three most reported sub-processes are loan servicing, title search/registration, and underwriting. Mortgage lenders are saving big by automating their manual, time-consuming cumbersome legacy systems and process; thereby increasing cost efficiency and productivity. How AI, ML, and Intelligent Automation Technologies are Game Changers in the Fintech Industry? Cost Reduction and Scalability to Support Growth Given the changing market, more lenders are turning to digital financing. AI and ML deliver a significant gain compared to utilizing only normal statistical models. This invention is at the forefront of sustaining transparency and performance. In response to changes in data and outliers, AI/ML models require less manual intervention, enhancing overall efficiency. By understanding mortgage application information more precisely and quickly, AI and automation can replace optical character recognition (OCR). AI can also read text from emails, documents, and other sources. An AI-powered support automation technology optimizes loan processing by enhancing customer satisfaction and communication between lenders and borrowers. Save Time and Reduce Errors AI eliminates human errors and uses machine learning to improve accuracy. This is huge for the mortgage business. Errors in human data entry have a high cost. AI can handle mortgage papers fast without getting tired or bored, leading to calculation or judgment errors. Enhance Customer Experience (CX) AI-powered chatbots can quickly answer borrowers’ questions and guide them through the loan application process. Mortgage lenders can use AI to quickly gather information from borrowers (for example, their credit scores or student loans). Mortgage businesses start the mortgage procedure and offer superior goods for those consumers. Based on their income and credit history, a company can predict which customers are at higher risk for defaulting, enabling them to offer different types of better loans for those individuals. Improve Efficiency through Intelligent Automation Machine learning, data analytics, neural networks, and other AI-based technologies can greatly improve financial technology. AI is becoming crucial in lending. It is bringing new efficiency and value to Fintech. For example, AI can write expense reports faster and with minor inaccuracies than a human. Also, AI may power technologies that help human workers track and automate operations, including compliance, data input, fraud, and security, while also learning from and verifying events for anomalies. Deliver Great Customer Service Consistently Customer service is one of the most notable areas where AI has benefited Fintech. Artificial intelligence has advanced to where chatbots, virtual assistants, and other AI interfaces can consistently engage with customers. Answering basic questions can significantly reduce front office and helpline expenditures. Wrapping up: COVID-19, as a whole, is proving to be an effective catalyst, with the ability to inspire industry leaders to reinvent their digital strategy. AI adoption is growing: more businesses are catching up, familiarizing themselves with innovative tools, and starting to explore new capabilities. This is a good time to start assessing the impact of AI, ML, and intelligent automation on their mortgage business. What next? Tavant can help mortgage lenders diversify how they do business and effectively unlock savings with next-gen digital technologies. To gain more insights, reach out to us at [email protected] or visit here. FAQs – Tavant Solutions How much can mortgage lenders save by implementing Tavant intelligent automation?Mortgage lenders using Tavant intelligent automation typically achieve 60-80% reduction in processing costs, 70% faster loan approvals, and 50% decrease in manual errors. ROI is often realized within 6-12 months of implementation. What cost-saving automation features does Tavant provide for mortgage lenders?Tavant offers automated document processing, intelligent underwriting, compliance automation, and workflow optimization. These features eliminate manual tasks, reduce staffing needs, and minimize compliance penalties while improving loan quality. How much money can lenders save with automation?Lenders can save 30-70% on operational costs through automation, including reduced labor costs,
Data Analytics: A Catalyst for Change in Service Life-cycle Management
The past few years have seen manufacturers look at their aftermarket services management in a completely new way. While technology and digitization have largely driven this change, the recent global pandemic has rocketed the drive for remote yet effective service support to ensure that customer requirement are still seamlessly met. Tech Innovations and the Flood Called Data The inadvertent result of this upsurge in digitization has been the data. Data, which is often collected from disparate sources, is now becoming a big challenge and an opportunity for manufacturers. With the adaptation of technology, many manufacturers can capture and utilize data but fail to do so. Why Measurement Matters Data-driven manufacturing is in the realm of being seen as a strategic necessity that can help manufacturers compete effectively. And with the application of analytics, manufacturers, suppliers, and distributors can achieve significant value in speed and operational efficiency. The ability to measure and use data is also leading manufacturers to offer services based on usage, uptime/downtime, and create value for customers through personalization. Let’s look at some of the key uses of data analytics and how it will impact manufacturers. Manage Demand and Supply Chains Data analytics is helping manufacturers understand the cost and efficiency of every aspect of the product lifecycle, from suppliers to customer usage. By analyzing the parameters and conditions that impact the supply chain from all angles, businesses can uncover problems such as hidden bottlenecks or unprofitable production lines. As a result, they gain insight into the conditions that affect the complete profitability of an integrated supply chain and learn how best to capitalize on given conditions. Forecast Demand for Products & Services Manufacturers can combine data with predictive analytical tools to create an accurate projection of purchasing trends. Insights driven by analytics can even help manufacturers understand how well lines are operating, enabling smarter risk management decisions. The ability to analyze when warranties are expiring can also result in additional service revenue channels for manufacturers. IoT solutions for asset management offer real-time alerts, enabling manufacturers to act quickly, and minimize losses from delayed, damaged, or lost goods. Proactive System Maintenance Predictive maintenance is helping manufacturers increase their product lifetimes while preventing downtimes. It analyzes the historical performance data to forecast potential failure and further identify the cause of the problem. This is particularly effective in field service management, where predictive maintenance can result in tremendous savings. According to McKinsey, manufacturers using predictive maintenance typically reduce machine downtime by 30 to 50 percent and increase machine life by 20 to 40 percent. Optimize Machine Efficiencies and Utilization Data analytics can significantly improve assembly-line efficiency by identifying bottlenecks and defects. With advanced analytics, manufacturers can ensure that machines operate at high efficiency, resulting in improved quality and increased productivity. Optimize Inventory and Warehouse Costs Efficiently Advanced analytics can be applied to improve product flow management, which positively impacts inventory operations while reducing unnecessary expenditure. For example, manufacturers can assess fill rates which can reduce stock-outs. Improved insights can help manufacturers know which locations/equipment are operating at an optimized level and improve other production centers and address warehousing deficiencies if any. Final Thoughts Enhancements Across the Service Life-cycle Analytics is enabling manufacturers to scale cloud-based operational intelligence, AI-enabled monitoring, diagnostics, and asset lifecycle management. AI-enabled digital technologies are seamlessly addressing service life-cycle challenges, increasing transparency across the process and functions, and creating a seamless and rich experience for the customers. SOURCES: http://www.wonderware.es/wp-content/uploads/2017/02/WhitePaper_InvensysandMicrosoft.pdf https://www.mckinsey.com/business-functions/operations/our-insights/manufacturing-analytics-unleashes-productivity-and-profitability