Case Study
Who we worked with
A multinational financial services corporation that offers a range of products to consumers and businesses.How we helped
Established a team of consultants and digital experts with banking experience. Ran root-cause analysis through site visits, workshops, and employee interviews Used process mining to identify when and why delays were happening.What they needed
On average, the processing and approval of business loans took 13 days. This was very slow, and competitors, including fintechs, could offer similar loans much faster the customer experience was suffering, but it was unclear why delays were happening.
What they got
Recommendations on how to decrease processing times and reduce the time to fund to just two days A plan to improve the customer experience and increase loan application speed and transparency
Challenge
Time is money
When time is money, speed is a differentiator. On average, it takes approximately 13 days for business
loans to progress from application to funding. The company wanted to identify where delays were
happening, why, and ultimately create an action plan to reduce processing time to just two days. But the
merchant finance process is global and complex. With three major teams working across multiple
geographies and time zones, it was difficult to know where to start.
However, the company knew it needed to streamline the process to compete with other providers, such
as fintechs, which can offer similar loans faster. In addition, it is passionate about customer experience,
and wanted to give customers transparency throughout the process and reduce the time it took to
notify them of the outcome of their application.
Solution
User research and process mining creationThe first step was research. Even if you think you know where the problems in a process are, research often reveals challenges in places you might not think to look. AYFI’s consulting team took the innovative approach of collating qualitative and quantitative data across the process, including the steps managed by third parties, to make recommendations on how to streamline loan applications from beginning to end.
To do this, AYFI visited client sites to observe and interview employees. We saw their challenges – such as a reliance on paper-based and manual processes – and uncovered insights that otherwise wouldn't have been captured with digital research.
Combining this research with digital data allowed the AYFI team to create process models. The process models are digital copies or representations of a process followed by a user and/or technology to complete a given task. They offer a consolidated view of the end-to-end processes across multiple touch points and disparate systems. They also flag what is and isn't working to make it easier to identify opportunities for improvement and, potentially, automation.
The AYFI team also collected event logs – or time-stamp data – from the core loan-processing system, and used process mining to create the process models of the merchant finance process. Then, the team could identify wait times and bottlenecks across the process, quantify reworks and exceptions, and understand the factors influencing approval times. And, because a process model focuses on facts and figures, it removes emotion and bias from the research phase, which is a problem often seen in traditional interview-based approaches.
Impact
An action plan for digital transformation
Using the process model of the merchant finance process, the AYFI team made recommendations
spanning process changes, opportunities for automation, and ways to improve transparency.
We made five major recommendations:
Streamline document gathering: The longest delays happened early in the process. Applicants were slow
to submit documentation and the process was largely paper-based. AYFI recommended requesting
documentation earlier, creating automated reminders to chase documentation from customers, and
implementing e-signature capabilities to reduce the need for paper. And as all applicants are existing
customers, we identified how the company could find customer data within its client database, rather
than ask for information it already had.
Explore exceptions and improve training: Exceptions cause applications to stall or move backwards in
the approval process. Exceptions happened in 35–40% of loans, causing major delays, but they could not
explain why many exceptions were happening. We recommended increasing visibility and documenting
the exact reasons for exceptions, while also improving employee training to reduce unnecessary out-of-
process issues.
Align working hours: If employees are working across different time zones, and there's a handover from
one team that's online to another team that's offline, the loan may be stuck until working hours in the
latter team's time zone begin again. AYFI suggested adjusting employee working hours to limit
downtime and prevent application backlogs.
Automate risk assessment: Financial spreading – a key part of risk assessment – was largely manual. AYFI
identified opportunities to automate spreading with AI and improve internal quality control to prevent
delays at this stage.
Increase process transparency: Overall, the process lacked transparency. If a loan was stuck with an
approver, or had an unresolved exception, neither employees nor customers would be notified. We saw
opportunities to improve employee productivity by implementing real-time monitoring, alerts, and
control measures using the Digital Twin. This would provide the visibility required for a proactive, rather
than reactive, customer experience.
Result
The financial services firm is now turning these recommendations into action. It's focused on improving the customer experience and accelerating the time to fund from 13 to just 2 days.
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