AI-Analytics: The Gamechanger for ISVs & Lenders in Digital Mortgage
Technologies like AI-based analytics are revolutionizing business models and customer relationships in new and surprising ways. In particular, the BFSI sector has not been immune to the lure of this powerful technology. Companies across this industry are using AI analytics to elevate customer experiences with personalized interactions and tailored product offerings. Mortgage lenders and large banks have embraced the potential of AI analytics to make the entire MLO ecosystem more efficient, cost-effective, and customer-aligned. However, many are also discovering that implementing any AI-enabled solution requires careful planning and a strategic approach to ensure ROI.
Let‚Äôs examine the promise of intelligent analytics and whether it could be the gamechanger for enabling digital mortgage.
Where are we now, and what must change?
Manual processes in the mortgage ecosystem have always been time-consuming and error-prone. Borrowers tolerated the delays with the understanding that intensive manual effort was being directed towards fulfilling their particular needs. However, today‚Äôs tech-savvy borrowers have zero patience for old school business models ‚Äď with good reason. Studies show that traditional lenders take 20% more time to process applications compared to institutions using digital processes.
Pre-Covid, the mortgage industry had been digitizing tasks such as online loan applications, e-verification of income and assets, drive-by automated appraisals, etc. However, throughout the pandemic, workers and consumers increasing relied on digital devices, tools, and applications to accomplish every task. This shift left exploded customer expectations for faster, round-the-clock support, self-service portals, mobile apps and personalized user experiences. As it happens, this took place at a time of rising competition in this space. The net result was a sharp increase in the pressure on lenders and mortgage companies to accelerate LOS digital transformation ‚Äď from first touchpoint through the back office and into the sunset. An ambitious goal, but where and how to introduce digitization was uncharted territory.
Now, as we move into the new normal, the focus is on automating core functions to streamline loan origination and forbearance and continuously modernizing and improving the lending experience to appease customer expectations. So, the pressing business need for lenders and ISVs is to not only enable faster, more intuitive, error-free loan processing, but also to anticipate changes in borrower expectations and market conditions using modern technologies like artificial intelligence and advanced analytics. But MLOs still carry legacy debt on all levels. Embedding AI-based analytics in applications and infrastructure with underlying conflicts and dependencies could create new, long-term problems. Fortunately, there are a panoply of options as to how and where to leverage AI analytics ‚Äď key among them being AI-powered process automation and AI-ready ISV products and support. Of course, this changes the game for the ISVs building the products and platforms that digital mortgage companies need.
Today, the entire mortgage ecosystem is exploring high-impact, customer-focused, cost-effective ways to leverage AI analytics to drive new efficiencies, innovations, and growth. The race is on!
4 Ways AI-Based Analytics Can Help Mortgage Lenders and ISVs Improve Business Outcomes
1. Simplify onboarding processes
Identifying borrower pain points in the digital mortgage journey is essential and onboarding is a perennial thorn in the customer‚Äôs side. From the borrower‚Äôs standpoint, onboarding should be simple, fast, convenient, and seamless. After all, if Amazon can do it‚Ä¶
Fortunately, increased adoption and experimentation is proving that the onboarding process can be significantly improved using AI-based analytics. AI can analyze borrower data to determine creditworthiness and recommend appropriate, pre-approved loans. Also, it can assist in providing a prospective borrower with a pre-filled loan application based on ‚Äúsmart‚ÄĚ history searches, making the documentation process swift and seamless.
AI-based analytics makes these improvements possible by analyzing millions of data points to identify the flags and roadblocks that impede progress. With data-backed information, it becomes easier for organizations to reduce risk and close more profitable loans, while providing enjoyable customer journeys, starting with painless onboarding.
2. Optimize efficiency, reduce costs
As mentioned earlier, legacy mortgage systems that rely on outdated technologies tend to require manual processes which are error-prone, time-consuming, and costly. AI and ML can be used to automate and continuously improve LOS processes and monitor key databases, while AI analytics is able to pinpoint issues proactively, triggering rapid response and remediation. These ‚Äúsmart‚ÄĚ technologies and tools can process data from reports, documents, spreadsheets, media, etc., to identify patterns, glitches and gaps, then generate alerts and context-based predictions, conclusions and/or recommendations.
When strategically planned, implemented and socialized, AI-driven analytics and workflows can help humans, processes and products become more efficient and productive. Now it‚Äôs up to the lenders, ISVs and ecosystem players to identify the ways and means to do that with minimal business disruption and maximum impact.
3. Enhance the compliance posture
Managing the constantly evolving compliance and regulatory landscape is an important point of consideration when it comes to digital mortgages. Legacy systems tend to be monolithic and rigid, making updates or modifications impractical when it comes to integrating or supporting compliance on the fly. Such systems almost always kick the can of non-compliant loans down the road for manual review and end up increasing costs and turnaround time.
AI-powered processes and analytics tools can improve data validation capabilities and ensure more comprehensive compliance when designed by highly skilled AI engineers with deep domain knowledge. These systems will automatically check for compliance with federal and state-level requirements in real-time, reducing risks and downtime costs, while enabling automated, near-error-free processing.
AI-based analytics can also improve the organization‚Äôs compliance posture. Analytics solutions can be used to scan a variety of data sources such as credit histories, banking databases, rules and regulations for red flags, while predictive analytics can identify potential risks and anomalies. Fact-based insights into borrower histories and behavior patterns can also identify risks associated with repeat borrowers or potentially fraudulent behavior. These solutions may then automatically alert process owners to take actions like modifying workflows to plug any compliance gaps without compromising speed and efficiency.
4. Elevate the customer experience
The digital mortgage space is becoming crowded as loan and refi applications break records and agile, new players enter the field. With millennials comprising a big chunk of the market, lenders need to differentiate their brands by delivering modern, uber-efficient digital experiences. The aim is to acquire new clients and increase customer stickiness with AI-assisted marketing and communications and an almost fanatical focus on ‚Äúknowing the customer‚ÄĚ (KYC) to continuously enhance the borrower experience - and drive loan revenues.
With insights from AI-based analytics, ISVs and lenders can use data-backed insights into consumer behaviors and needs to deliver personalization at scale and move from a reactive to a proactive customer service posture.
AI-based analytics is also driving innovations across the spectrum of digital mortgage initiatives. For instance, AI-based chatbots are transforming customer service by automatically logging interactions and providing personalized, real-time assistance based on the borrower‚Äôs account history, social sentiment and other relevant demographics.
AI-based analytics can also map the activity of repeat borrowers over time to facilitate their decision-making with relevant information and offers based on contextual personalization.
The mortgage industry is set to become more tech-driven as we move deeper into the post-pandemic world. Putting the customer at the core of your digital mortgage model is essential to thriving in this challenging and ever-changing business environment. Technologies such as AI-based analytics and similar smart technologies can help lenders, ISVs and ecosystem players transform the mortgage industry landscape into a delightful, clickable digital adventure with rewards for everyone for a job well done. Of course, obstacles, failures and hard-earned learnings are also on the agenda. But this is the game, and four ways AI can help you win.
Discuss AI Analytics with our Digital Mortgage Engineering experts, connect with us