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Automation of serviceability verification: improving time to yes in mortgages

With the mortgage market facing an ever-increasing competitive landscape with many new and emerging lenders, and a customer base that is becoming more discerning, established lenders have been moving to ensure that they remain competitive and a lender of choice. In responding to customer expectations of increased personalisation and simpler access to credit, as well as market erosion from highly agile and digital competitors, established lenders are leveraging their data and assets to optimise their lending processes. Through a series of upcoming blogs this transformation will be explored in detail, with multiple facets of the lending process covered to highlight current innovations.

The lending process has many opportunities for automation and digitisation:

Across the lending process there are several key areas that need to be included in a digital transformation, but this blog will specifically be addressing one of the largest opportunities for improvement that lenders face; the verification of a customer’s serviceability and the impact it can have on the time to decision (time from application to final approval) of a loan application.

Over the last two to three years there has been a noticeable increase in the time to decision for home loan applications driven in part by a housing market that has been experiencing significant growth leading to large volumes of home loan applications.  However, the last 12 months has seen a steady decrease in the average time to decision (back to pre-pandemic levels) as more lenders improve processes to drive faster decisions. With the increased rise of online lenders and fintech’s focussed on improved digitisation and automation of income and expense gathering, the ease and timeliness of completing an application is quickly becoming a competitive advantage.

Although the struggle with time to decision is not new, and income and expense verification has always been a focus area for lenders and regulators, this renewed focus on the speed of application processing is not lost on the major lenders in the market. While online lenders are capitalising on their position (a lack of legacy data, systems, and processes) and an innovative mindset to push the boundaries on what can be achieved with fast approvals, established lenders are looking to invest heavily in refining and redesigning existing processes to drive improvements straight through processing.

So, how can a quick time to decision be achieved through fast serviceability verification whilst maintaining rigorous and robust compliance with regulations? Through combining the smart and efficient use of automation and data with the right tools, a successful solution can include the following elements:

  • Automated extraction and reading of bank/credit card statements
  • OCR reading of uploaded documents
  • Single touch payroll and superannuation retrieval
  • Ingestion and use of internal data 

Open banking is also expected to provide further opportunity for assessing customers income and expenses once the supplied data is being more widely utilised and ingested as it will increase a lenders ability to assess a customer’s financial situation across all lenders.

Many lenders are already utilising automation throughout the application process, but as these tools become more ubiquitous and widely adapted, the next phase is understanding how best to optimise them to drive positive results for both the customer and the lender. Knowing the best way to implement, control, and utilise the output is arguably the most important element of a successful automated income and expense verification process. There are many key elements and hurdles that need to be considered when implementing any new automated processes for verification purposes, some of which can be approached in the short-term allowing for rapid improvement, while others are longer term strategic issues with more complexity. A successful serviceability process will consist of several key steps:

Identifying the depth of information available for assessment is a key aspect of the verification process and should drive the verification requirements. As an example, existing customer can have a more streamlined approach compared to new to bank customers, allowing for fast decisions with reduced requirements. 

Once obtained, the data needs to be converted into a useable and consistent format using categorisation models. The categorised data must be organised and crafted into features (such as income with frequency and consistency measures) that are most relevant to the verification required.

How to use the data that has been sourced and classified in the verification process is a key strategic decision. To allow for maximum automation it is likely that some tolerance in the degree of matching between customer declared and verified income will need to be built into the verification.  Through a risk-based approach in calculating this tolerance the maximum automation can be achieved whilst also maintaining a compliant approach. 

It is important to have a robust monitoring process in place to continue to enhance and uplift the verification process with ongoing learnings. Optimisation will also need to occur in the instance of changes in environment or overall verification requirements.  Any implemented strategy needs to be dynamic, and not “set and forget”.

While the digital transformation and automation can revolutionise the verification process, it is important that this in underpinned by a robust manual verification process. This needs to be a safety net used by exception in cases where the automated solutions are not possible, or when a customer does not opt in to utilise the new options. Even a legacy manual process can still be optimised for better customer experience though and should remain part of any transformation agenda.

Technology is developing rapidly within this space in the industry (even if adoption and implementation is slowly following suit), with the goalposts and available tools constantly evolving. It is with this in mind that the ongoing adaption of new tools will help to set lenders apart from their competitors. There is ample scope for automation of serviceability verification and the above considerations are key components for creating a process that will meet the growing needs of the customer while maintaining a rigorous compliance framework.