Consumer lending has evolved into a sophisticated enterprise of data-driven personalization, hyper-local segmentation, and real-time, always-on digital marketing, trying to find the perfect offer for the right consumer at the most opportune time. It is all about ‘leads’ and about ‘conversion.’ Financing a house through mortgage lending is no exception. What used to be a predominantly relationship business has reinvented itself on the internet-social-digital stage and is working on playing a bigger part in the digital transformation of our daily lives.
Through this analytical machine, though, fall through many that don’t have the highest ‘conversion’ value – consumers who may not imminently make a purchase and, therefore, are less valuable for the transaction-driven revenue models prevalent in most financial services marketing. While perfectly rational on the surface, how, then, should we look at the impact of this transformation on those consumers who ‘need some help’? Those that (i) may not have the historical advantages of being able to build credit, (ii) that have had a bump or two in their past credit histories due to structural economic conditions, and (iii) those that self-select and sit out of the mortgage process because of various levels of uncertainties and intimidations that present themselves. Marketing machines tend to ensnare this population in less-than-ideal credit products, often unsustainable for the borrower, further compounding their economic viability.
Here in lies an opportunity for seasoned industry veterans and always hungry-to-learn technologists to pursue a path of innovating with a purpose: to make that first homeownership opportunity possible, make credit for that first home accessible, and set our consumers on a path for sustainable wealth-building. The mortgage industry can do this by riding on two fundamental technology transformations that will underpin human-computer interactions for decades to come… open banking and generative artificial intelligence.
Open banking, the concept of making a consumer’s financial data securely available upon consent, is a driving force of innovation for fintechs in the banking industry, who have already transformed our daily lives with numerous products and services. We can access our bank accounts while on a website’s shopping cart, transfer bank account-to-bank account, and not write checks or use credit cards that take days, if not weeks or months, to settle.
There is a ton of innovation here. Opening banking can help lenders obtain a more accurate picture of a consumer’s financial situation and risk level by responsibly identifying their willingness to pay their monthly recurring obligations, for example, utilities and rent, and create a viable alternative to conventional credit scores. This development has been a game changer, especially for those with no credit or thin credit files. This population, who, by traditional means of credit assessment, would have remained on the edges of accessing prime credit, now has a fighting chance of securing a home loan just as much as those with deep conventional credit histories. This is what we call purposeful innovation. We do that a lot at FinLocker. We analyze consumer-permissioned financial data and establish sustainable paths toward homeownership. We employ artificial intelligence to analyze the consumer’s consented financial data to see if we can help them better understand their spending habits and project cash-flow volatility to get a more accurate picture of their own finances before taking on debt.
While data and analytics fueled by open banking set the table to analyze a consumer’s finances, generative AI can further lower the bar of engagement. Generative AI is the ability of the machine to synthesize and create novel content, not just regurgitate pre-fab numbers and information. ChatGPT is a trending example.
When applied to financial services, generative AI (without their hallucinations) can understand not just a consumer’s financial data but derive motive and intentions, contrast you with others in a ‘journey like yours’ – and create the certainty and confidence that you are not alone in your pursuit. Does generational income come into play for my demographic, and is it acceptable to ask about that with a mortgage professional, or will I be denied? Can I rent-share my future home with other millennials, or will the lender judge me as unable to afford the property? How do downpayment grants work? All these are contextual questions, quite personal, that open-banking-driven financial analytics may or may not shed light on. Using responsible and generative AI, we can open the door for these conversations without fear of judgment or rejection and let our consumers fully explore their journey.
Recall our objective, sustainable homeownership. The more a consumer can understand, explore and own, the better it is for them – and most certainly, the better it is for the housing finance ecosystem (15-18% of our GDP!). So for those charting a course for purposeful and profitable innovation in this digital-social-ChatGPT era, look no further than this powerful convergence of generative AI and open-banking technologies.
Prabhakar “PB” Bhogaraju, Executive Vice-President, Head of Strategy & Product Development with FinLocker, has over 20 years of mortgage technology and consulting experience, with extensive experience in design thinking and digital business strategy, product development, and customer engagement.
Prior to joining FinLocker in 2021, PB held executive leadership positions in product development, data management, and digital business architecture at Fannie Mae for ten years..