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In 2013, the UK government launched the Help to Buy initiative to support people struggling to own property.
It brought in an influx of mortgage applications to brokers such as SPF Private Clients. In response, we at SPF teamed with EscalateAI to create a portal and AI-powered virtual assistant based on IBM Cloud and Watson solutions to speed up qualification.
Making the most of new opportunities
Help to Buy brought new business and the opportunity to help more people buy homes to SPF Private Clients. However, the complexity of the applications left us with a huge administrative workload. Brokers had to inspect lots of information from each applicant, so it would sometime take two days to evaluate a single application.
SPF Private Clients wanted a way to simplify and speed up approvals, so we could qualify more people for Help to Buy. We began looking for a technology partner to automate the process.
Speeding up the move from applicant to customer
We engaged EscalateAI to design a solution based on IBM Watson technology to speed up qualification for Help to Buy applications. Watson was an obvious choice, designed for use cases just like ours. It helped us bypass adapting the technology to fit our needs or worrying about scaling it. Watson also had extra features we could turn on in the future to augment the solution.
We developed an AI-powered virtual assistant named Ava based on IBM Watson Assistant to provide relevant information to clients. EscalateAI combined IBM Watson Tone Analyzer with its technology to create a hybrid chatbot. Ava can respond automatically to a range of client queries, but if the solution detects a low level of confidence or tone, it automatically brings in a mortgage advisor to take over the interaction.
EscalateAI created a portal for Help to Buy applicants based in IBM Cloud Foundry. In the portal, potential clients can submit information, upload documents and interact with Ava, even outside of office hours, enabling around-the-clock service. It securely holds and manages client information in IBM Cloud Object Storage with IBM Compose for MongoDB.
Once information has been submitted through the portal, we use algorithms to provide immediate feedback on applicants’ viability for a mortgage. Visitors can get a quick mortgage indication in three minutes, a decision in principle in 15 minutes and a tailored mortgage recommendation in 30 minutes.
The solution assigns a traffic light rating to each lead. Green indicates leads qualified by Ava for action, amber those needing further information and red applications that require attention from an SPF Private Clients specialist team.
Making customers feel at home
Today, SPF Private Clients can process more Help to Buy leads at greater speed. Best of all, we achieved these improvements without expanding our team. Leads come in prequalified with the supporting documentation verified and uploaded in one place, enabling brokers to focus on more urgent client requests and drive up conversion rates.
We’re also improving the experience for clients. Applicants can receive instant responses to frequently asked questions from Ava at any time of the day and quickly find out answers to simple and complex mortgage inquiries. This gives clients peace of mind in a time that can be quite stressful.
The speedy, efficient service enabled by our portal and Ava has given us a strong head start on our competitors, but we won’t stop here. Based on its success, we plan to extend the solution to other departments, such as commercial finance, remortgages, insurance, wealth-management and short-term financing.
The IBM and EscalateAI solution is helping us transform mortgage qualification and the client experience. That’s a win-win scenario we’re keen to replicate elsewhere in the business.
Read the case study for more details.