Theoretically, each new loan application carries a 50% chance of default
Lenders need to be able to make precise assessments of the likelihood of default and other lending-related risks to be able to move capital at scale
To better understand the distribution of risk within the portfolio, it's important to slice data into homogeneous segments
Determining whom to lend to, how much to lend, and at what rate is one of the most important mathematical challenges for modern lenders. Theoretically, each new loan application carries a 50% chance of default. This theoretical supposition, however, is of very little value when it comes to real-world credit decisions.
Lenders need to be able to make precise assessments of the likelihood of default and other lending-related risks to be able to move capital at scale. By applying statistical methods to a large and diverse portfolio of loans, you can obtain accurate estimates of the overall risk exposure faced by a lender.
However, to better understand the distribution of risk within the portfolio and make risk-adjusted business decisions, it’s important to slice data into homogeneous segments based on shared characteristics, behaviours, or preferences.
These may include attributes such as digital engagement, conscientiousness, financial planning, and credit propensity, among others.
Advantages Of Leveraging Customer Segmentation
While such segmentation helps make more informed decisions regarding underwriting and loan pricing — it plays a much bigger role in business growth, helping lenders understand their incoming customer segments better and identify and cater to their needs at different points in time.
By leveraging segmentation, lenders can tailor their offerings to meet the specific needs and preferences of each segment, thereby improving customer satisfaction, increasing retention, and driving business growth.
In the context of cross-selling, upselling, and offering tailored loan products, segmentation plays a vital role in targeting the right customers with the right products at the right time.
For example, any in-device risk engine that securely analyses reams of customer data from even non-traditional sources such as demographic indicators, app usage, bill payments, occupation type, and digital activity data to generate a comprehensive persona. These personas help lenders segregate profitable customers and premium customers.
However, segmentation is only the first step. Lenders must be equipped to utilise business insights from segmentation at speed to gain a competitive edge. This is achievable with the help of a business rules engine.
These workflows can be easily configured to ensure that premium customers encounter minimal friction and relatively riskier segments are offered risk-adjusted loan terms. Such adaptive journeys help maximise the approval rate, minimise cost, and mitigate risk, effectively.
In Conclusion
Customer segmentation is clearly about so much more than risk. It is the key to overall business growth. A comprehensive segmentation solution can help lenders offer borrowers customised products, maximise cross-sell and upsell, share the right promotional offers on the right channel, and identify new, profitable segments.
At the end of the day, treating every customer equally doesn’t make good business sense. In the era of hyper-personalisation, segmentation is the key to unlocking meaningful connections with your audience.
