Understanding the Changing Landscape in the Mortgage Industry
The mortgage industry is under enormous pressure to perform in the face of fierce competition, increased due diligence for loans and borrowing (due to the COVID-9 pandemic and its economic ramifications), crunching timeliness, and ever-growing data. According to Gartner, a human error in the financial sector results in 25,000 hours of pointless rework per year, costing up to $878,000.
Our only stumbling block, we believe, is fintech automation.
The global fintech industry is estimated at $65,88,780mn in 2021 and is projected to reach $1,66,52,680mn by 2028 at a CAGR of 13.9% for the forecasted period. Fintech automation can define even the most unstructured data and support lending process automation to deliver a resource, cost, and time-efficient process. Mortgage and lending have already been reimagined using automated technology such as chatbots and digital assistance. Given the massive amount of data, the need for real-time, data-driven strategies for effective customer UX and UI, and loyalty retention in the mortgage lending industry, this shift is inevitable.
Customer onboarding to Know Your Customer (KYC), legal processes, due diligence, credit checks, and form fill-ups have previously been observed to require 50-75% of the onboarding process cost. However, well-integrated lending automation, combined with optical character recognition and natural language processing, assists mortgage lenders in shortening the lending cycle and lowering costs.
“Generative AI enables bank CIOs to offer technology solutions to the business in pursuit of revenue growth,” according to Moutusi Sau, VP Analyst at Gartner, “while autonomic systems and privacy-enhancing computation are long-term solutions that provide new options for business transformation in financial services.” We can cite several reasons for the rapid adoption of automated technology as a core business process in lending process automation across verticals.
Customer expectations have risen dramatically in terms of complete transparency, customer-centric, highly personalized interactions, and maximum participation. As a result, maximum fintech implementation can be seen in customer relationship management (CRM), accounts payables, mortgage automation, risk management, payment arrears, reconciliation requirements, insurance premium calculations and settlements, back office, and front office, among other areas. Aside from process integrations, the lending industry requires fintech capability to eliminate cyber fraud risks and identity thefts, as well as a digitally secured infrastructure to protect customer data related to mortgage and lending.
Customers feel empowered with access to secure omnipresent, omnichannel, digital transactions, and payments when lenders provide mortgage lending automation with simplified tasks. Self-service in CRM with chatbots and instructive guidelines, according to Deloitte’s Finance 2025 report, creates a better customer interface. Furthermore, mortgage lenders can use fintech automation to absorb data from borrower application forms, extract information from borrower payroll applications, and automatically upload loan data into respective portals. More importantly, automated technology enables credit decision-making systems and microdata inspection with low error rates for seamless loan approval and disbursement.
Considering this massive shift, organizations have been striving hard to develop deeper hyper-automation processes or at least implement partial automation, machine learning, and artificial intelligence to attain optimum operational efficiency. Categorically, RPA tools have also matured from traditional desktop automation to enterprise solutions. This has profusely helped in managing complex processes like strategic decision-making, cognitive learning capability, user interfaces, and so forth. Fintech automation is undeniably booming, and competition is heating up. Companies are planning both organic (diversification, geographical expansion, etc.) and inorganic (mergers and acquisitions) strategies to gain a competitive advantage and remain sustainable.
Gartner estimates that banks and investment firms will spend $623 billion on technology products and services by the end of 2022. The major investments will be in generative AI, autonomic systems, and threat-nullifying technologies.
Final Thoughts
During our research, we have found that CEOs across the globe believe that cloud-based ERP, cognitive technologies, and hyper-automation will radically simplify lending processes and accelerate the lending industry as a whole and not in silos. In fact, hyper automation is already in its nascent stage of enterprise adoption. Apart from this, the banking and investment services will also witness larger use of generative artificial intelligence, generative adversarial networks (GANs), and natural language generations for fraud detection, predictive analysis, synthetic data generation, artificial intelligence-backed follow-ups, and risk-factor modelling. With the help of algorithm-driven and interactive AI and robots, new service models will emerge. This will not only diversify the financial workforce, but will also link the entire organization into a real-time, digitally connected workplace.
What’s Next?
Tavant’s consulting-driven approach to automation helps mortgage lenders and banks significantly improve productivity and enhance customer experiences using our deep automation and domain expertise. By combining the power of industry tools and accelerators, we drive organization-wide transformation through RPA, ML, and AI to solve your most important business challenges.
To learn more, visit us here or reach out to us at [email protected].
