Reimagine mortgage lending with generative AI

In this proof of concept, Teradata demonstrates how banks can use generative AI to deepen relationships with customers.

Simon Axon
Simon Axon
1 février 2024 2 min de lecture

As discussed previously, banks should follow three crucial steps to prepare for the AI-driven future. Now, we’ll explore a specific example of delivering an AI-centric strategy that reimagines a fundamental aspect of banking: the mortgage lending process. 

Mortgage lending was valued globally at $11,487 billion in 2021 and projected to reach $27,509 billion by 2031, so it’s significant business for banks. For customers, mortgage lending is often difficult to navigate. McKinsey reports that the application process is one of the most frustrating aspects of the experience. Customers often start house hunting before researching lenders, and many find that applying with a current bank versus a new one makes little difference to the length of the process.

Generative AI proof of concept for banks

Generative AI’s capability to connect, query, and derive insight from multiple sources of data in near real time presents opportunities to radically improve this process. Banks can use it to retain clients and connect them with high-value financial products. Teradata has developed a proof of concept (POC) to show how banks can use generative AI to create deeper, stickier, and more profitable relationships with customers. It uses a generative AI-powered automated assistant to analyze internal and external data sources and deliver personalized affordability calculations from the start of the home-buying journey. 

This demonstration shows how trusted artificial intelligence (AI) can engage in natural language conversations with customers while also analyzing internal and external customer and transaction data to provide accurate and reliable advice. Rather than requiring customers to complete complex forms, banks can use generative AI to calculate affordability in real time with any customer.

Helping homebuyers make more informed decisions

With generative AI, banks can integrate data from multiple sources. So, instead of a yes-or-no decision, banks can act as trusted guides to help customers understand what they can afford, based on almost limitless parameters. Generative AI can reference and analyze income and expenditure data from customers—plus current home prices in local areas, insurance and utility costs, taxes, crime figures, and school ratings. In addition to recommending what is affordable and preapproving loans, it can advise on property types, locations, and financial products that meet individual customer needs.  

This also creates potential to partner with—and act as ecosystem developers for—real estate agencies, legal firms, and other move-related service providers. With generative AI, banks can transform customer journeys, repositioning themselves at the very beginning of the lucrative home-buying cycle.  

This POC is just one example of how generative AI can deliver better customer experiences and profitable opportunities for leaders who embrace it. Banks that orchestrate their own data and build processes for accessing external data will succeed. To learn more, or to request a demonstration of this generative AI POC, connect with your local financial services consulting team.


À propos de Simon Axon

Simon Axon leads the Financial Services Industry Strategy & Business Value Engineering practices across EMEA and APJ. His role is to help our customers drive more commercial value from their data by understanding the impact of integrated data and advanced analytics. Prior to his current role, Simon led the Data Science, Business Analysis, and Industry Consultancy practices in the UK and Ireland, applying his diverse experience across multiple industries to understand customers' needs and identify opportunities to leverage data and analytics to achieve high-impact business outcomes. Before joining Teradata in 2015, Simon worked for Sainsbury's and CACI Limited.

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