In this piece, we will delve into a method that empowers banks with deeper insights into their existing operations, facilitating a structured approach to discerning primary revenue sources and gearing up for impending challenges.
You’re a bank CEO going through the quarterly financial results of your institution. Spread out before you are pages of intricate financial reports showcasing myriad metrics – NIM, unrealized losses, Quarter-Over-Quarter reserve adjustments, recent Default rates, the influx of new clients, shifts in operational expenses, and so on. As a seasoned professional, you possess the ability to look between the lines and discern the most lucrative products and identifying the most valuable customers.
Yet, this experience-based intuition is not free of pitfalls:
The perception of other management members might diverge from yours, given that everyone has their unique viewpoint.
As highlighted by the seminal Kahneman-Tversky research, personal judgments are often riddled with biases, rendering sole reliance on them risky.
This just scratches the surface of potential issues.
Not many within an organization might match your depth of business acumen. Instead of focusing on enhancing the overall profitability, some might be fixated on department-specific goals. Take for instance the marketing department: in their zeal to design a new advertising campaign, their primary concern might revolve around the generating maximal amount of new applications, sidelining crucial metrics like approval rates, default rates and attrition. There’s a well-known business saying: “You can’t improve what you don’t measure”. If one could pinpoint the exact revenue each client generates, assessing the efficacy of marketing campaigns would become substantially more straightforward. Implementing such systems ensures everyone aligns with a singular objective – augmenting the institution’s bottom line.
Another complexity arises from the multitude of parameters influencing profitability. Establishing connections between these parameters and profitability can be intricate. Aspects like default rates, recovery rates, operational expenses, and marketing costs all play pivotal roles. Moreover, the interplay between these parameters isn’t always linear. It’s not a given that a 4% spike in default rate will lead to a fourfold impact compared to a 1% hike.
Armed with a comprehensive framework that captures these multifaceted relationships, scenario analysis becomes more precise, providing clearer insights.
There are countless arguments underscoring the immense benefits of comprehending the profitability of each customer and having an adept framework to compute every profitability determinant. Why then is this not a mainstream practice? The advantages seem self-evident, making it an indispensable tool for every bank!
From our vantage point, the reality diverges from the ideal. Stay tuned for our subsequent piece, where we expand on this disparity and explore the construction of such frameworks, with a special mention of timveroOS.