High-quality data is an invaluable asset for secured finance lenders, helping them to unlock all of the information they need to drive growth. However, having access to this data is just the first step. Using it to its full potential is not always straightforward, and many lenders may be missing out on the opportunity to have a deeper understanding of both their customers and the wider market. There is also an added challenge when there are vast amounts of data available.
Data analytic tools are the answer to these challenges. These allow lenders to track performance, make better decisions, protect themselves from risks and offer products that align with their customer’s needs. Now, lenders have access to much more sophisticated techniques to reap these benefits. Modern technologies such as AI and machine learning are transforming the way in which lenders can use their data. These are known as advanced data analytic tools.
Large volumes of data that are too complex to be processed without modern technologies are referred to as “Big Data”, and is common in the secured finance sector. 89% of enterprises believe that if they do not adopt a Big Data analytics strategy, they risk losing market share and momentum. Even so, only 16% of these enterprises are currently using Big Data analytics to optimize processes and strategies, and just 12% are using them to predict outcomes.
There is a huge gap in the market to take advantage of modern data analytic technologies to drive growth. Here’s why:
Crack down on risk
Risks for lenders start at the very beginning of a loan. With predictive modelling, lenders can access important information such as the borrower’s credit history, behaviors, and payments, to assess the likelihood of defaults. Using this information, lenders can ensure more favorable loan terms.
Once the loan has been agreed, lenders can also proactively react to unusual activity within their accounts. These tools can detect patterns and identify any issues, which could easily be missed.
Predictive models to tackle risks can also weigh up risk versus return using the valuable data lenders already have, leaving much less margin for error while ensuring that profits are maximized.
Personalized products
Every customer has different needs and circumstances. A “one-rule-for-all” approach could leave lenders more at risk, and causes them to miss out on a wider range of customers. With advanced data analytics, lenders can examine the customers’ needs and habits and offer a product that suits them, such as a higher credit limit.
An accelerated loan process
In the past, loan processing has been a manual task, taking up a lot of time and resulting in less applications being processed. Advanced analytic tools can automate the decision-making process, leading to a higher volume of accepted applications in much less time. It also ensures the decision-making process is far more accurate.
Want to learn more about advanced data analytics, alongside the other current top trends in secured finance? Download our eBook.
Top Trends in Finance 2025
Read part 1: Why your growth strategies should be data-driven
Read part 2: Important regulation changes you need to be aware of
Read part 3: Why you should be paying attention to embedded finance
Read part 4: Technology challenges in lending and how to solve them
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