London, UK – 14 Feb 2019: Ava was created to handle the significant increase in inquiries from first time buyers looking to take advantage of the Government’s help-to-buy scheme and the added complexities of the eligibility criteria. Ava answers customer questions, verifies personal details and documentation while processing enquiries at a faster rate. This allows SPF advisors to secure some of the best available lending rates and spend more time with clients providing tailored recommendations. Ava is powered using IBM’s Watson Assistant, an AI-enabled virtual assistant, and is built on the IBM Cloud. Ava is trained on 200 common questions regularly received by the advisors and knowledge from the Certificate in Mortgage Advice and Practice (CeMAP). Ava offers quick clear answers that help guide buyers through the early stages of the buying journey. Buyers can now get a mortgage recommendation in just three minutes, a Decision in Principle in 15 minutes and be connected to an advisor for a full mortgage recommendation in 30 minutes, a service previously taking up to five working days.
Integrated with IBM Watson Tone Analyser and Escalate AI solution, from IBM Business Partner Escalate AI, Ava offers a hybrid human chat solution. By analysing tone and confidence in client messages, Ava can ensure that clients needing extra support are seamlessly handed over to an advisor. Over time and with built-in machine learning functionality, the virtual assistant gets smarter and better at advising on help-to-buy mortgages. As new questions come in Ava alerts the advisors who can jump in to connect with the client and Ava learns the answer remembering it for next time.
Ava was also developed using additional services on the IBM Cloud, including IBM Cloud Foundry, IBM Compose for MongoDB and IBM Cloud Object Storage. This has enabled the customers personal information to be securely managed and allows Ava to continuously deliver during peak times whilst also learning and developing as a service as she works hand in hand with the advisors.
Freddie Savundra, Digital Architect at SPF Private Clients, said: “Lots of people want help understanding how much they can afford through the Help to Buy scheme and how each stage of the mortgage application process works. With Ava, we can guide them at every step, giving them confidence that they’re in the best possible hands. Combining live and bot chat allows us to elevate the client experience and manage a number of different client scenarios within one user-friendly interface.”
The Government Help-to-Buy scheme opened the door to the property market for first time buyers, with advantageous equity loans and the growing number of new build developments. With many property developments opening viewings on weekends, SPF noticed an increase in inquires over the weekend and late on Sunday evenings. Traditionally reliant on speaking to a mortgage advisor during working hours, SPF wanted to better support their clients and provide a round the clock service. Available 24/7 on the company website, Ava can pre-qualify potential buyers for the scheme by verifying client’s details such as bank statements, credit card transactions and passport information uploaded into the portal and present applications ready to progress to the advisors first thing Monday morning. Helping clients get closer to exchanging and simplifying the process associated with buying a house.
Ava is enhancing the role of the traditional advisors, enabling them to spend more time conducting analysis on new developments and properties and building relationships with banks to secure the best lending rates for clients. It has allowed SPF Private Clients to have more informed conversations with clients and deliver a greater client service.
“With the increasing digitisation of the mortgage market and more people seeking mortgage information and application processing online, Ava delivers real competitive advantage and expertise in simplifying the house buying process.” Richard Voaden, Channel Leader, Watson and Cloud Platform, IBM UK and Ireland.